Execution Finality for AI-Factory Silicon and Accelerated Infrastructure: Enforcement Profiles for GPUs, Chiplets, Memory Fabrics, DPUs, RDMA, CXL, and Photonic Boundaries

Internet-Draft	AI-Factory Silicon Finality	September 2026
Das	Expires 18 March 2027	[Page]

Abstract

AI factories and AI-native network infrastructure are becoming heterogeneous hardware systems rather than isolated model-serving applications. A single consequential operation may traverse CPU control planes, GPUs or NPUs, high-bandwidth memory, chiplets, coherent memory fabrics, CXL or PCIe paths, IOMMUs, DMA and RDMA engines, SmartNICs or DPUs, accelerator fabrics, model-serving runtimes, optical interconnects, storage systems, and physical power or cooling controllers. At these layers, a computation can be valid and a component can be authenticated while the resulting tensor transfer, memory exposure, queue activation, model load, routing change, optical emission, partition transition, firmware update, or physical actuation is still not authorized to become effective.¶

This document describes a silicon-oriented execution-finality architecture for preserving that distinction. A consequential hardware or infrastructure operation is represented as a Candidate Act and remains in a Non-Effective State until a Protected Enforcement Domain establishes a machine-verifiable act descriptor, validates applicable authority predicates, updates or consumes protected state, and commits Protected Validation Evidence. A scoped non-bearer capability is then bound to the exact Candidate Act, relevant descriptor or digest, protected evidence, protected state, freshness and policy conditions, permitted scope, execution-finality boundary, and applicable Finality Sink. Possession of the capability alone is insufficient.¶

The Finality Sink is the hardware, firmware, protected-runtime, fabric, controller, or adjacent enforcement role that has mandatory control over the consequence. Depending on the profile, it may be a memory controller, HBM gate, GPU scheduler, accelerator partition controller, IOMMU or SMMU, DMA or RDMA engine, DPU or SmartNIC, fabric switch, chiplet link controller, CXL component, cache-coherence controller, model loader, token-emission gate, optical modulator or wavelength controller, eFPGA configuration gate, rack power controller, or another protected effectuation point. The Candidate Act becomes effective only after sink-local verification confirms that the actual pending operation still corresponds to the validated act and current protected state.¶

The document provides 83 detailed Enforcement Profiles. Sixty-three translate the supplied GPU, silicon, chiplet, memory-fabric, and AI-factory source set; twelve supplementary profiles map the same execution-finality model onto current hyperscale and Arm-based infrastructure directions; and eight additional profiles selectively adapt non-duplicative mechanisms from the companion DAS Protocols V, VI, and VIII disclosures for silicon and AI-factory use. The catalogue covers GPU and accelerator egress, rack-scale AI fabrics, sparse expert routing, coherent and disaggregated memory, collective communication, DPUs and SmartNICs, RDMA and GPU-direct access, model and KV-cache lifecycle state, accelerator partitioning, arithmetic-mode control, AI-factory scheduling, storage, telemetry, firmware, power and cooling, digital-twin actuation, in-network compute, federated-learning egress, neural-waveform release, silicon photonics, optical lanes and wavelengths, chiplet admission and UCIe control, CXL memory pools, RAS and memory quarantine, eFPGA configuration, reconfigurable optical accelerator topologies, accelerator-pod membership, compiler-to-silicon executable activation, confidential-realm memory ownership, secure device attachment, coherent-mesh admission, confidential offload-device attachment, network and storage offload queues, isolated management-controller actions, host-independent cloud-offload actions, UltraServer-scale accelerator-fabric membership, distributed EFA/RDMA endpoint admission, protected neural-state proofs, hardware-isolated shadow auditing, cross-committed collection-time and execution-time validation, attested measurement paths, distributed partial-share finality, separate compute-enable and produced-output authority, output-derived digest binding, and effectuation-enabling-resource withholding. Profile identifiers retain the source numbering for traceability; gaps are intentional.¶

The architecture is intended to complement, not replace, existing work in accelerated computing, AI networking, confidential computing, chiplet and coherent-memory systems, accelerator fabrics, optical I/O, attestation, authorization, and hardware isolation. In particular, the profiles are directly relevant to public technology directions associated with NVIDIA, AMD, Intel, Google, Amazon Web Services (AWS), Microsoft, Broadcom, Marvell, and Arm, whose current infrastructure spans GPUs and AI accelerators, custom AI silicon, high-bandwidth memory, CPU-to-accelerator coherence, chiplets, CXL and PCIe-class fabrics, DPUs and infrastructure offload, RDMA and high-speed AI networking, rack-scale accelerator systems, optical and silicon-photonic interconnects, hardware roots of trust, and large-scale AI-factory orchestration. Additional industry relationships with Qualcomm, Meta, Cisco, HPE, Nokia, Ericsson, Samsung, Huawei, AT&T, Verizon, Orange, and Deutsche Telekom are discussed in the body of the document where AI-native networking, RAN, 6G, edge compute, and operator-controlled infrastructure create related effectuation boundaries. These references identify technical complementarity and possible enforcement locations; they do not imply that any named organization has reviewed, endorsed, adopted, or lacks equivalent mechanisms.¶

The IETF relevance is principally the cross-layer security relationship among identity, attestation, evidence, cryptographic binding, workload authorization, protected state, replay resistance, delegation, and the final effectuation boundary. The hardware protocols themselves remain within the remit of the appropriate hardware, semiconductor, telecommunications, optical, and system standards bodies. The common invariant is: computation may produce a Candidate Act, but computation is not authority for consequence.¶

2. Relationship to Current Industry Work

2.1. Basis of Comparison and Invitation to Correct

The following subsections compare the execution-finality question with selected publicly described work from equipment vendors, semiconductor and accelerated-compute companies, and network operators.¶

The purpose of naming these organizations is orientation.¶

Their work demonstrates why the consequence boundaries described by the Enforcement Profiles are becoming technically relevant: AI is moving into radio control, shared compute, network APIs, autonomous operations, sensing, Open RAN, cloud-native infrastructure, and closed-loop network management.¶

No organization named below has reviewed, endorsed, adopted, approved, sponsored, or participated in this document unless expressly stated elsewhere. No affiliation is implied.¶

The comparisons are based only on public material reviewed by the author. They should not be interpreted as statements that an organization does not implement a particular security or authorization mechanism internally. Public descriptions necessarily provide an incomplete view of commercial architectures, and similar concepts may be implemented using different terminology.¶

Where a characterization is incomplete or incorrect, correction is welcomed.¶

The narrow question used throughout the comparison is:¶

After a system has computed, selected, generated, or prepared an operation, where is the machine-enforced boundary that determines whether that exact operation may become externally effective, and what exact current evidence and protected state must be verified there?¶

2.2. Qualcomm Technologies

Qualcomm Technologies is relevant to this document at a considerably broader level than RAN management alone. Its public direction spans on-device and edge AI, modem-RF systems, fixed-wireless and gateway platforms, AI-native RAN, telco-server infrastructure, distributed compute, sensing, autonomous network management, and 6G architectures extending across device, RAN, and core.¶

Qualcomm's 2026 6G Foundry material describes an AI-native, context-aware platform with intelligence distributed across device, RAN, and core, including device-side adaptation of protocol and radio parameters within network-defined guardrails. Its 6G infrastructure material also describes integrated telco compute combining Oryon CPUs, Hexagon NPU AI engines, and RAN accelerators for deterministic unified processing. Public references include Qualcomm 6G Foundry and Qualcomm AI-Native 6G Infrastructure.¶

Those directions intersect with several different execution-finality boundaries rather than one narrow telecom use case. At the device, an AI or context engine may infer an application state and propose a protocol, radio, routing, communication, tool, or cross-application action. At the RAN, an optimization or management agent may propose scheduler, handover, power, beam, spectrum, topology, or configuration changes. In telco compute, a CPU, NPU, RAN accelerator, memory system, I/O path, or virtualization layer may allocate or alter resources whose state affects deterministic network execution. In an exposed network service, an API or autonomous agent may request a QoS, location, identity, communication, or other operator-controlled effect.¶

The proposed architecture is complementary to those compute, modem, radio, accelerator, and network-control capabilities. It does not replace inference, scheduling, RAN acceleration, network policy, hardware isolation, attestation, access control, or the control algorithms themselves. It introduces a separate question after those mechanisms have selected an operation: whether this exact Candidate Act has current machine-verifiable authority to produce this exact effect.¶

A resulting control or device action can therefore remain non-effective while a protected descriptor binds the exact operation, relevant output or parameter state, purpose, destination, device or network context, policy epoch, freshness, and permitted effect. Protected validation evidence can be committed before or atomically with a scoped non-bearer capability, and the capability can be verified at the point that actually controls the consequence.¶

Depending on the operation, the Finality Sink could be implemented at a modem or radio-control boundary, RAN accelerator or scheduler, device OS or driver boundary, NPU/accelerator controller, memory or DMA translation boundary, network-interface or packet-egress path, network API gateway, RIC or orchestration controller, or another component that has mandatory control over the effectuation-enabling resource.¶

This relationship becomes more important as intelligence moves closer to hardware and as devices and networks become more autonomous. A correct inference, a valid model execution, a successful digital-twin test, an authenticated agent, or a valid network credential may all be necessary inputs while still remaining distinct from final authority for the exact resulting hardware, radio, network, data, or communication consequence.¶

Nothing in this comparison asserts that Qualcomm products lack equivalent safeguards or that the proposed profiles are required by Qualcomm architectures. The public roadmap is used to show where execution-finality properties could complement increasingly AI-native device, silicon, RAN, and 6G infrastructure.¶

2.3. Nokia

Nokia is relevant to this document across AI-native RAN, anyRAN software, accelerated and cloud-native radio compute, open interfaces, distributed real-time applications, autonomous network operations, integrated sensing, and the migration path from 5G and 5G-Advanced toward 6G. The relevance is therefore broader than a single RAN-optimization use case.¶

Nokia's 2026 AI-RAN platform is built on AI-native anyRAN software and accelerated computing, with three deployment paths spanning AI-accelerated capacity added to existing baseband deployments, standalone accelerated AI-RAN nodes, and cloud-native AI-RAN on COTS infrastructure. Nokia has also described NVIDIA programmable merchant silicon and Marvell AI-accelerated merchant silicon as parts of its ecosystem approach. Public references include Nokia AI-native RAN platform; Nokia AI-RAN.¶

That architecture creates several distinct consequence boundaries. AI may infer channel conditions, select radio parameters, optimize spectrum use, allocate shared compute, schedule real-time workloads, invoke an application through an open interface, expose RAN data to a third-party application, or prepare a sensing or Physical-AI action. Each of those computations can be valid while the resulting live-network transition is still treated as a Candidate Act.¶

At the accelerated-compute layer, the proposed architecture complements Nokia's software-defined and merchant-silicon strategy by separating permission to execute an AI/RAN workload from authority to make a resulting hardware or network transition effective. A GPU, accelerator, CPU, memory system, DMA path, NIC, DPU, or baseband resource may execute the computation, while a protected effectuation boundary separately verifies whether the exact scheduling, memory, packet, radio, or control-state change is currently authorized.¶

This distinction is useful where AI and RAN workloads share accelerated resources. Allocation of compute slices, memory bandwidth, queue priority, accelerator contexts, interconnect capacity, DMA mappings, power headroom, or preemption priority may itself affect deterministic radio behavior. Execution finality does not replace the scheduler or resource manager; it permits selected resource-changing operations to remain non-effective until the exact change is bound to current state, scope, freshness, and the effectuation boundary that will make the change real.¶

At the radio layer, a Finality Sink could be colocated with a scheduler, RAN accelerator, baseband control path, radio-control interface, fronthaul or packet-egress path, or another component that has mandatory control over the live radio transition. At the cloud-native layer, it could be associated with a hypervisor, container or orchestration boundary, accelerator runtime, IOMMU or DMA boundary, SmartNIC/DPU, network interface, or protected service that controls deployment and resource activation.¶

Nokia's open and programmable direction also increases the importance of delegation and third-party software boundaries. An authorized dApp, rApp, AI application, or exposed API can be permitted to compute or propose an operation without inheriting unrestricted authority over the radio, sensing output, network resource, or downstream service. The scoped non-bearer capability and Finality Sink model provide a way to preserve openness while making the exact consequence independently verifiable.¶

Nokia also describes AI-native 6G as a broader architectural shift in which AI becomes foundational rather than an overlay, and its current AI-RAN work explicitly targets open, programmable, software-speed evolution toward that destination. See Nokia: AI-native 6G is now the industry direction; Nokia: The era of AI-native RAN starts now.¶

The proposed architecture therefore complements Nokia's direction by defining a consequence-bound authorization invariant that can sit below or beside AI-native software: computation, optimization, prediction, or accelerator execution may succeed, but authority for the exact externally consequential operation is established only at the protected boundary controlling that effect.¶

Nothing in this comparison asserts that Nokia products lack equivalent isolation, attestation, scheduling, authorization, safety, or replay controls. The public architecture is cited to identify concrete places where execution-finality semantics could be mapped into AI-native RAN, merchant-silicon, cloud-RAN, sensing, and autonomous-network deployments.¶

2.4. NVIDIA

NVIDIA is relevant to this document far beyond AI-RAN alone. Its public architecture now spans accelerated AI factories, Blackwell-class GPU computing, Grace CPUs, NVLink scale-up fabrics, InfiniBand and Spectrum-X scale-out networking, BlueField DPUs, confidential computing, AI software and agents, and AI-RAN infrastructure that converges radio and AI workloads on shared accelerated systems.¶

NVIDIA describes AI factories as vertically integrated from accelerated compute through DPUs and high-speed networking to enterprise AI software, and describes AI-RAN as concurrent AI and RAN execution on shared, distributed accelerated infrastructure. Public references include NVIDIA Enterprise AI Factory, NVIDIA Networking for AI Factories, and NVIDIA AI-RAN.¶

This creates several classes of consequence boundary. One class concerns shared-resource state: accelerator cycles, GPU partitions, high-bandwidth memory, cache, memory bandwidth, fabric capacity, queue occupancy, DMA or RDMA mappings, interconnect routes, power and thermal envelopes, and preemption priority can affect whether another workload, including a latency-critical RAN function, obtains the resources it requires. A second class concerns data movement: model outputs, tensors, KV state, memory regions, storage operations, network packets, and protected data can cross GPU, CPU, DPU, NIC, fabric, storage, or external-service boundaries. A third class concerns autonomous software: models and agents can generate tool calls, code, configuration changes, network actions, or other externally consequential outputs.¶

The execution-finality profiles therefore do not treat NVIDIA hardware merely as a place where computation occurs. Where a resource allocation, memory transition, DMA mapping, packet release, fabric operation, model-output release, or autonomous action is capable of producing a protected consequence, that operation can itself be represented as a Candidate Act.¶

A corresponding Finality Sink could be placed at, or implemented through, a GPU or accelerator scheduler, partition or resource arbiter, memory controller, DMA or RDMA engine, IOMMU-facing path, hardware queue, NVLink or other interconnect control point, DPU or SmartNIC path, network-egress controller, storage path, hypervisor, confidential-compute boundary, or another protected hardware or software point whose successful transition makes the effect possible.¶

This is particularly relevant to the deeper-silicon profiles in this catalogue: coherent-memory and ownership transitions, chiplet and die-to-die attachment, accelerator fabrics, confidential accelerator context, DMA aperture changes, HBM ownership, DPU/SuperNIC data paths, optical egress, firmware or microcode activation, and on-die resource-partition or NoC state can all be analyzed as effectuation boundaries rather than merely configuration details.¶

The complement is not an alternative to CUDA, GPU isolation, confidential computing, scheduling, networking, or NVIDIA's existing resource-management and security mechanisms. Those mechanisms determine how compute is executed, isolated, moved, and protected. Execution finality adds an operation-specific authority invariant: a particular consequential state transition is permitted only when the exact pending operation, current protected state, evidence, scope, freshness, and sink binding still correspond at the point of effect.¶

For AI-RAN specifically, this means both sides of the convergence can be protected without forcing them into the same authorization model. AI workload execution can remain autonomous and high performance; RAN execution can remain deterministic; and a resource-changing or radio-affecting operation can still require a machine-speed finality check at the boundary that actually commits the change.¶

For AI factories and agentic infrastructure, the same invariant can apply to model-output release, tool dispatch, secret or key use, memory movement, network transmission, storage admission, or infrastructure reconfiguration. Computation can therefore finish successfully while the output or requested infrastructure transition remains non-effective until its own finality conditions are satisfied.¶

Nothing here asserts that NVIDIA lacks comparable controls. The intended relationship is complementary: NVIDIA provides increasingly integrated accelerated-compute, networking, DPU, confidential-compute, and AI-RAN platforms; the proposed architecture describes a way to bind exact consequential operations on those kinds of platforms to current, non-bearer, sink-verified authority.¶

2.5. Arm

Arm is relevant at a foundational silicon and system-IP level because its architecture and compute subsystems span mobile and edge devices, cloud and data-center CPUs, AI infrastructure, networking and 5G, automotive systems, embedded systems, chiplets, coherent interconnects, memory and I/O control, and security mechanisms used by a broad semiconductor ecosystem.¶

Arm's current Neoverse Compute Subsystems combine CPU cores, CMN mesh interconnect, system IP, memory and I/O capabilities for cloud, AI, 5G, and networking workloads. Neoverse CSS N4 is positioned for agentic AI infrastructure and explicitly targets high-speed AI-accelerator interfaces and DPU designs that offload networking and security and isolate agentic workloads from the host. Arm also describes AI-native mobile compute for increasingly agentic on-device systems. Public references include Arm Neoverse Compute Subsystems, Arm Neoverse CSS N4, and Arm Mobile AI.¶

The relevance to execution finality is therefore broader than any one Arm processor. Arm-based systems contain many of the boundaries at which a computed decision becomes a hardware or externally visible consequence: CPU privilege transitions, secure-monitor and trusted-execution boundaries, memory-controller and SMMU-mediated access, cache and interconnect state, interrupt and device control, chiplet links, accelerator attachment, DPU data paths, firmware state, storage and network I/O, and device-side application or actuator interfaces.¶

The proposed architecture complements those building blocks by defining an ordering and authority relationship rather than a replacement processor architecture. A Candidate Act may be computed by software or AI running on an Arm-based CPU, GPU, NPU, DPU, or heterogeneous SoC, but the act can remain technically non-effective until a protected enforcement point verifies an act-specific descriptor, protected validation evidence, scoped non-bearer authority, current protected state, and the identity of the actual effectuation boundary.¶

At infrastructure scale, this could apply to DPU-mediated packet or storage release, SMMU or DMA-aperture changes, coherent-memory ownership, chiplet or accelerator attachment, network-control operations, confidential-workload transitions, firmware activation, or resource-partition changes. At the edge and mobile layer, it could apply to an agent-generated cross-application action, communication, sensor-derived act, device-control operation, model-output release, credential use, or another operation that crosses from inference into externally consequential device behavior.¶

A key complement is that the enforcement role can be implemented using existing architectural protection points. The Finality Sink does not need to be a new physical chip or a remote gateway. It can be a protected function associated with an operating-system or hypervisor boundary, trusted execution environment, secure monitor, memory or I/O controller, interconnect or device-control component, DPU, secure firmware path, or another component that already has mandatory control over the required effectuation resource.¶

This makes the proposal compatible with Arm's system-level direction toward heterogeneous and increasingly agentic compute. The CPU, accelerator, interconnect, memory system, and security architecture continue to perform their existing roles; execution finality adds a machine-verifiable rule for when a particular result of that computation may acquire authority to change protected state or cause an external effect.¶

The same distinction is relevant to Arm's partner ecosystem because Arm supplies architectural foundations that semiconductor vendors customize into many different SoCs. A functionally defined Finality Sink and act-bound capability can therefore be mapped to implementation-specific protection points without requiring every vendor to adopt one physical topology or one branded security block.¶

High-relevance profile map: Arm has particularly strong correspondence with Profiles 213 through 215. Profile 213 maps finality onto confidential-realm granule ownership and security-state transitions; Profile 214 addresses confidential device attachment together with SMMU stream ownership and DMA authority; and Profile 215 addresses security-domain-aware coherent-mesh agent admission and confidential I/O paths. These profiles complement Arm CCA/RME, SMMU, CMN/CHI, chiplet, CXL-attached-device, and Neoverse system-IP directions without changing those architectures.¶

Nothing in this comparison asserts that Arm architectures or Arm-based products lack equivalent security, isolation, attestation, or authorization mechanisms. The public roadmap is cited because Arm's reach across mobile, edge, infrastructure, 5G, networking, heterogeneous compute, chiplets, and security makes it a particularly broad substrate on which execution-finality enforcement could be instantiated.¶

2.6. AMD

AMD is relevant to this document because its current AI-infrastructure direction combines rack-scale accelerators, EPYC CPUs, Pensando networking, HBM, chiplet packaging, open scale-up and scale-out fabrics, partitioning, and ROCm software into systems in which data movement and resource state are as consequential as the arithmetic itself.¶

AMD describes the Instinct MI400 family and Helios rack-scale design as integrating chiplet-based accelerators, HBM4, EPYC CPUs, Pensando networking, and open infrastructure for large-scale training and inference. See AMD Instinct MI400 Series and related Helios material.¶

That direction maps directly to profiles covering tensor egress, HBM and memory ownership, accelerator partitions, collective communication, sparse-expert routing, KV-cache movement, cluster scheduling, firmware, power and cooling, and high-bandwidth interconnects.¶

The proposed execution-finality layer is complementary: ROCm, GPU firmware, fabric protocols, schedulers, partitioning, memory protection, and network controls continue to perform their existing functions, while selected consequence-bearing transitions can additionally require an act-bound capability at the exact GPU, memory, fabric, DPU, or orchestration boundary that makes the change effective.¶

Nothing in this comparison asserts that AMD products lack equivalent safeguards. The public architecture is used to identify concrete silicon and rack-scale boundaries where the profile model can be instantiated.¶

2.7. Intel

Intel is relevant across CPU control planes, data movement, Ethernet, CXL, heterogeneous AI systems, IPU-style infrastructure processing, accelerator integration, and RAN/cloud infrastructure. These layers contain many of the memory, I/O, orchestration, and effectuation boundaries described by the profiles.¶

Intel's 2026 infrastructure material emphasizes Xeon 6+ for agentic orchestration and data movement, expanded Ethernet, heterogeneous AI systems, and continuing accelerator development. Intel also documents CXL support in current Xeon platforms. See Intel agentic AI infrastructure and Intel CXL platform documentation.¶

The complementary question is not whether a CPU, NIC, accelerator, IOMMU, memory controller, or infrastructure processor can authenticate, isolate, schedule, or move data. It is whether the exact pending memory, queue, route, DMA, model, or cluster-state transition should remain non-effective until current act-specific authority is verified.¶

Potential Finality Sinks therefore include IOMMU or DMA-remapping logic, CXL transaction admission, Ethernet/NIC paths, IPU or infrastructure-processing boundaries, accelerator admission, model-loading control, and protected orchestration commits.¶

Nothing in this comparison asserts that Intel platforms lack equivalent controls; it identifies where the execution-finality invariant could complement existing isolation, CXL, networking, attestation, virtualization, and management mechanisms.¶

2.8. Broadcom

Broadcom is relevant because AI scale-up and scale-out increasingly depend on switching silicon, NICs, SerDes, PCIe, custom accelerators, and co-packaged optics. Those components directly mediate packet, tensor, route, lane, wavelength, and optical-effect transitions.¶

Broadcom publicly describes Tomahawk 6, Jericho, Thor NICs, PCIe infrastructure, custom XPU technologies, and co-packaged optics as parts of an end-to-end AI-infrastructure platform. See Broadcom AI Infrastructure and its co-packaged-optics material.¶

The relevant complement is especially direct for the fabric and photonic profiles. A switch or optical subsystem may be able to route or modulate traffic at line rate, while the profile asks whether the exact route, tenant, tensor class, lane, wavelength, destination, data class, or protected-state epoch is authorized at the hardware boundary that performs the forwarding or optical conversion.¶

Finality Sinks can therefore map to switch-table commit paths, congestion or route controllers, NIC/DPU egress, optical-lane enable gates, modulator drivers, wavelength-selection logic, CPO interfaces, or destination admission points.¶

Nothing in this comparison asserts that Broadcom products lack equivalent authorization, security, or reliability mechanisms. The comparison identifies where hardware-local finality semantics could complement high-speed AI networking and optical I/O.¶

2.9. Marvell

Marvell is relevant to this profile set through custom AI silicon, multi-chip packaging, PCIe/CXL connectivity, high-speed SerDes, die-to-die interconnect, switching, DPU-class infrastructure, and optical connectivity.¶

Marvell describes custom AI infrastructure ASICs using multi-chip packaging, advanced SerDes, PCIe Gen 6 / CXL-class connectivity, Arm subsystems, and high-bandwidth die-to-die interconnect. See Marvell Custom ASICs and its AI infrastructure portfolio.¶

These technologies create concrete boundaries for chiplet admission, die-to-die tensor transfer, memory-window activation, CXL/PCIe routing, optical egress, firmware activation, and cross-domain data movement. The profiles treat those transitions as Candidate Acts when they can create an externally meaningful or cross-protection-domain consequence.¶

The execution-finality layer does not replace SerDes, CXL, PCIe, switch, security, or packaging protocols. It supplies an additional rule under which the exact consequential transition is admitted only when the current descriptor, protected evidence, protected state, permitted scope, and sink identity correspond.¶

Nothing in this comparison asserts that Marvell implementations lack equivalent controls. The public architecture provides a representative multi-chip and connectivity substrate for the proposed enforcement model.¶

2.10. Google

Google is directly relevant to the silicon and AI-factory profiles because its public AI Hypercomputer architecture spans custom TPU accelerators, NVIDIA GPUs, HBM, large-scale accelerator interconnects, data-center fabrics, optical circuit switching, storage, Kubernetes orchestration, and agent-serving infrastructure. The result is a vertically integrated environment in which model execution, memory movement, collective communication, routing, scheduling, and output release can each become consequence-bearing operations.¶

Google Cloud describes AI Hypercomputer as a systems-level architecture integrating performance-optimized compute, storage, networking, software, and orchestration. Its current infrastructure includes TPU systems connected through high-bandwidth inter-chip interconnects, large shared-memory domains, Virgo and Jupiter networking, optical circuit switching, RDMA-capable infrastructure, and GKE-based orchestration. Public references include Google Cloud AI Infrastructure, Google Cloud AI Hypercomputer at Next 2026, and Google Virgo Network.¶

The proposed execution-finality model does not replace TPU scheduling, cluster management, optical switching, access control, workload identity, confidential-computing mechanisms, or Google network protocols. It adds a narrower invariant: successful inference, scheduling, route calculation, collective planning, or agent reasoning need not itself authorize the exact hardware or external transition that follows.¶

For TPU and GPU systems, Candidate Acts may include tensor or activation transfer, KV-cache movement, HBM page exposure, collective communication, accelerator-to-accelerator transfer, model-state movement, output emission, or a change in accelerator scheduling. For the network, Candidate Acts may include fabric-route changes, congestion or traffic-class state, RDMA admission, optical-circuit selection, or destination egress. For agentic infrastructure, a generated tool call or infrastructure change can remain non-effective until the exact action is verified at its consequence boundary.¶

Relevant Finality Sinks could therefore include a TPU or accelerator egress gate, ICI or collective-communication controller, memory or HBM controller, DMA/RDMA admission boundary, network-interface controller, fabric switch, optical-circuit control point, cluster scheduler, storage gateway, GKE admission or workload-effectuation boundary, or a protected output-release path.¶

The complement is especially important in highly integrated AI systems because optimization increasingly crosses layer boundaries. A scheduler may alter compute placement because of memory pressure; a network controller may reroute accelerator traffic; an optical fabric may reconfigure around a failure; an agent may trigger an external service. Execution finality allows those systems to remain autonomous while requiring current act-bound authority only at the state transition that makes the selected consequence real.¶

High-relevance profile map: Google has particularly strong correspondence with Profiles 210 through 212. Profile 210 maps execution finality onto reconfigurable optical-circuit and accelerator-pod topology changes; Profile 211 addresses accelerator membership and collective-domain admission; and Profile 212 addresses activation of compiler-produced device programs, kernels, sharding plans, and executable graphs. These profiles are intended to complement, rather than reproduce, Google technologies such as TPU pods and superpods, ICI, optical circuit switching, XLA/Pallas-style compilation, and AI Hypercomputer orchestration.¶

Nothing in this comparison asserts that Google systems lack equivalent controls. Google's public infrastructure is cited because it provides a clear example of AI compute, memory, networking, optics, and orchestration converging into one machine-scale execution environment.¶

2.11. Microsoft

Microsoft is relevant across the full silicon-to-systems path. Azure now combines first-party Maia AI accelerators, Cobalt CPUs, Azure Boost DPUs and networking hardware, integrated HSM technology, high-performance storage and networking, partner GPUs, and large-scale agent and model-serving infrastructure.¶

Microsoft describes Maia 200 as a purpose-built inference accelerator with a redesigned memory subsystem and scalable Ethernet networking, while its broader Azure silicon strategy includes Cobalt processors, Azure Boost DPUs, and Azure Integrated HSMs. Azure Boost also offloads networking, storage, and host-management functions from the host CPU into purpose-built hardware. Public references include Microsoft Silicon to Systems, Microsoft Maia 200, and Azure Boost.¶

Execution finality complements this architecture by separating a successful Azure workload, model inference, virtualization decision, network/storage offload, cryptographic operation, or agent decision from authority for the exact consequential transition. The architecture is not a replacement for Azure isolation, attestation, virtualization, HSM controls, or responsible-AI mechanisms; it can use those mechanisms as inputs to a later act-specific decision.¶

Candidate Acts may include Maia tensor or output egress, HBM or accelerator-memory exposure, DMA/RDMA transfer, Azure Boost network or storage operations, virtual-network attachment, storage commit, cryptographic key use, accelerator allocation, model activation, or an agent-generated infrastructure action. The Finality Sink can be placed where the effect is actually controlled rather than at an arbitrary remote policy service.¶

Possible sink locations include an accelerator memory or egress controller, Azure Boost DPU, MANA/network-adapter path, storage-offload engine, Integrated HSM or key-use boundary, virtual-switch or network-interface gate, scheduler, model-serving runtime, cluster admission controller, or protected API-effectuation point.¶

This complements Microsoft's systems approach by allowing high-performance local verification. A remote policy engine can establish or refresh authority, while the latency-critical hardware or platform boundary verifies a compact act-bound capability and protected state before the transition. That model is compatible with heterogeneous fleets in which first-party Maia and Cobalt silicon coexist with NVIDIA, AMD, and other accelerators.¶

High-relevance profile map: Microsoft has particularly strong correspondence with Profiles 216 through 218. Profile 216 addresses attested infrastructure-offload devices joining a confidential workload trust boundary; Profile 217 addresses activation of network and storage offload queues together with their encryption and memory contexts; and Profile 218 addresses isolated management-SoC servicing commands that can mutate a wire-speed data path. These profiles map naturally onto the publicly described Azure Boost, MANA, confidential-device, Maia, and silicon-to-systems architecture.¶

Nothing here asserts that Microsoft lacks equivalent enforcement. The public Azure architecture is used to illustrate how AI accelerators, DPUs, HSMs, networking, storage, and agents create multiple places where computation and consequence can be technically separated.¶

2.12. Amazon Web Services

Amazon Web Services is relevant because it operates AI-factory-scale infrastructure combining Trainium and Inferentia accelerators, NVIDIA GPUs, the Nitro System, Elastic Fabric Adapter networking, RDMA-capable cluster fabrics, large-scale storage, orchestration, and AI services. These components create consequence boundaries in silicon, network offload, memory movement, storage, and cloud control.¶

AWS publicly describes AI Factories and EC2 UltraClusters using Trainium accelerators and NVIDIA GPUs connected by EFA at petabit scale. The Nitro System moves virtualization, security, networking, storage, and management functions into dedicated hardware and firmware, while EFA provides high-performance encrypted RDMA-capable networking for distributed training. See AWS AI Factories and AWS Nitro and Generative AI Security.¶

The proposed model is complementary because it can treat Nitro, EFA, accelerator, storage, or service boundaries as places to enforce act-specific finality without replacing AWS identity, Nitro isolation, IAM, encryption, attestation, or scheduling. An authenticated workload can be allowed to compute while a specific egress, DMA transfer, storage mutation, key use, model-state movement, or external service action remains separately non-effective.¶

Candidate Acts may include Trainium tensor transfer, accelerator collective communication, EFA/RDMA memory exposure, Nitro network or storage offload, model checkpoint movement, cluster placement, object-storage commit, confidential-workload migration, or agentic service invocation. A scoped non-bearer capability can bind the exact transfer, destination, resource, tenant, policy epoch, protected state, and permitted effect.¶

Possible Finality Sinks include an accelerator egress path, EFA or RDMA controller, Nitro card or offload engine, storage controller, virtual-network path, key-use boundary, cluster scheduler, model-serving gateway, or service/API commit point.¶

The significance is that AWS already separates many infrastructure duties from the host CPU. Execution finality can exploit that separation: a host or accelerator may be compromised or simply over-authorized for computation while the Nitro/EFA/storage boundary still refuses the exact externally consequential transition unless the act-specific authority state matches.¶

High-relevance profile map: Amazon Web Services has particularly strong correspondence with Profiles 219 through 221. Profile 219 binds authenticated cloud administrative intent to the exact host-independent hardware effect; Profile 220 addresses accelerator membership and topology transitions in UltraServer-scale fabrics; and Profile 221 addresses EFA/RDMA endpoint, peer-set, and distributed-job fabric admission. These profiles complement the Nitro System, Trainium/Neuron fabric, EFA, UltraServer, and UltraCluster architecture while leaving AWS identity, isolation, encryption, scheduling, and network protocols intact.¶

Nothing in this comparison asserts that AWS lacks equivalent safeguards. AWS is cited because its hardware-offloaded cloud architecture provides a particularly clear environment for testing whether computation authority and final effectuation authority can be represented as different machine-verifiable states.¶

2.13. Meta

Meta is relevant because its AI infrastructure now spans hyperscale GPU clusters, custom MTIA accelerators, advanced packaging, large-scale RDMA and Ethernet fabrics, custom software stacks, and increasingly automated low-level kernel and infrastructure optimization. Its recent MTIA direction also integrates communication more deeply into accelerator hardware.¶

Meta has publicly described MTIA as an in-house accelerator family deployed at scale, and MTIA 300 as a training accelerator with built-in NIC chiplets and communication-offload engines. Meta also operates large NVIDIA- and AMD-based AI clusters and custom network fabrics for training and inference. See Meta MTIA 300, Meta Infrastructure Evolution and AI, and Building Meta's GenAI Infrastructure.¶

Execution finality complements this direction by moving authority checks toward the points where communication, memory, scheduling, and output state actually become effective. A model kernel, scheduler, collective library, or communication engine can compute a valid operation while the corresponding NIC-chiplet transfer, memory exposure, collective communication, output release, or cross-tenant state transition remains separately gated.¶

Potential Candidate Acts include MTIA or GPU tensor transfer, collective operations, embedding movement, HBM or cache exposure, model-weight or checkpoint loading, NIC-chiplet transmission, RDMA flow admission, kernel activation, telemetry export, or infrastructure reconfiguration. Candidate Acts generated by automated kernel or infrastructure agents can be treated in the same way as human-generated control operations.¶

Relevant Finality Sinks can include an accelerator egress controller, built-in NIC or communication-offload engine, HBM/memory controller, RDMA or RoCE admission gate, switch or fabric controller, model-loader boundary, scheduler, kernel-execution admission point, storage gate, or output-release path.¶

This approach is complementary to Meta's hardware/software co-design: the co-designed communication path remains optimized for throughput, while a bounded local finality check can determine whether the exact transfer or state change has current authority. The model does not require a synchronous centralized authorization round trip for every flit, packet, or tensor fragment.¶

Nothing in this comparison asserts that Meta lacks equivalent safeguards. Meta's public work is relevant because it shows accelerator, NIC, memory, network, and software control becoming increasingly co-designed, making the placement of a consequence-boundary check an architectural rather than purely application-layer question.¶

2.14. Cisco

Cisco is relevant primarily at the AI-fabric and network-silicon layer. Its Silicon One family, high-capacity switching systems, optics, congestion and telemetry functions, and AI-data-center networking place programmable forwarding and optical/electrical egress directly on the path between accelerators, racks, storage systems, and external networks.¶

Cisco's 2026 Silicon One G300 announcement describes 102.4-Tbps switch silicon and systems intended for gigawatt-scale AI clusters, together with advanced optics and management functions for training, inference, and agentic workloads. See Cisco Silicon One G300.¶

Execution finality complements forwarding silicon by distinguishing route computation from authority to apply the resulting route or forwarding state. A controller can calculate a congestion response, path change, traffic-class transition, optical-lane selection, ACL update, telemetry export, or AI-fabric flow while the switch ASIC or optical boundary keeps that exact change non-effective until the act descriptor and protected state are verified.¶

Relevant Candidate Acts include forwarding-table updates, ECMP or adaptive-route changes, congestion-control mutations, queue/priority changes, lossless-flow admission, RDMA path changes, telemetry exposure, optical-lane activation, wavelength or port changes, and hardware egress of tensors or model state.¶

Possible Finality Sinks include the Silicon One forwarding pipeline, route-table commit gate, ACL or policy-table commit point, queue/credit controller, packet scheduler, fabric-switch egress, NIC/DPU boundary, optical serializer/modulator controller, or destination admission interface.¶

The architecture therefore complements high-speed AI networking rather than asking the network to slow down for remote authorization. Authority formation can occur off the hot path, while the switch or interface verifies a compact capability, epoch, route or flow binding, and protected state locally at line rate.¶

Nothing in this comparison asserts that Cisco lacks equivalent mechanisms. Cisco is cited because increasingly capable switch silicon and optics move consequential AI-fabric decisions into precisely the type of low-latency hardware boundary addressed by these profiles.¶

2.15. Hewlett Packard Enterprise

Hewlett Packard Enterprise is relevant because HPE Cray systems integrate CPUs, accelerators, high-performance interconnects, storage, orchestration, direct liquid cooling, and multi-tenant management into tightly coupled AI and HPC systems. This creates both digital and physical effectuation boundaries inside an AI factory.¶

HPE publicly describes Cray Supercomputing as a unified AI/HPC platform with multi-tenant capabilities, Slingshot-class networking, storage, and direct liquid cooling. Its current systems integrate heterogeneous CPUs and accelerators with system-level power and cooling infrastructure. See HPE Cray Supercomputing and Sovereign AI and HPE Cray GX5000.¶

Execution finality complements that system integration by allowing scheduler, fabric, storage, management, and physical-infrastructure decisions to remain distinct from the actual commit. A workload-placement recommendation, storage migration, network-route update, accelerator assignment, power-limit change, or cooling action can be computed centrally while the corresponding local controller performs the final act-bound verification.¶

Potential Candidate Acts include Slingshot or fabric-flow admission, job placement, accelerator allocation, cross-node memory or checkpoint movement, storage commit, telemetry/debug export, firmware activation, rack power changes, liquid-cooling valve or pump commands, and maintenance or digital-twin-derived actuation.¶

Finality Sinks can therefore include a fabric switch or NIC, scheduler admission gate, storage controller, BMC, firmware-update controller, power shelf, rack controller, cooling manifold controller, pump or fan controller, or protected management interface. For sovereign AI installations, the same mechanism can bind data or model-state egress to an approved destination, tenant, jurisdiction, and retention scope.¶

The architectural complement is that HPE can continue to optimize the complete AI/HPC system while finality remains a narrow invariant at selected consequence boundaries. It does not require every operation to traverse a central policy engine, and it can preserve fail-safe local behavior for power, cooling, and reliability-critical functions.¶

Nothing in this comparison asserts that HPE lacks equivalent controls. HPE is included because its integrated compute, fabric, storage, power, cooling, and sovereign-AI systems illustrate that AI-factory finality is not only an application-security problem but also a systems and physical-infrastructure problem.¶

2.16. Samsung Networks

Samsung Networks is relevant across AI RAN, software-driven vRAN, autonomous network operations, Integrated Sensing and Communication (ISAC), CPU/GPU-based network compute, Open RAN, cloud-native core, and the transition toward AI-native 6G. Its current direction spans both real-time radio intelligence and higher-layer autonomous control.¶

Samsung's current AI RAN material describes intelligence across physical-layer signal recovery, resource optimization, and network automation, while its vRAN roadmap supports CPU and GPU integration as the compute foundation for AI-native and 6G-ready networks. Samsung has also validated vRAN and AI-RAN functions on Intel Xeon 6 SoC, NVIDIA accelerated computing, and AMD CPU platforms. Public references include Samsung AI RAN; Samsung vRAN and AI/6G roadmap; Samsung vRAN on Intel Xeon 6 SoC.¶

The execution-finality architecture complements this software-driven and heterogeneous-compute direction rather than replacing it. Samsung's AI algorithms, vRAN scheduler, CPU/GPU runtime, RIC, orchestration system, or autonomous-network logic can continue to determine the best operation. The additional question is whether the exact resulting Candidate Act has current machine-verifiable authority to cross the live effectuation boundary.¶

At the silicon and accelerator layer, this can apply to operations such as CPU/GPU resource reassignment, accelerator-context activation, memory-region access, DMA or IOMMU changes, packet or tensor egress, network-interface release, or a hardware-assisted radio operation. The relevant control can be located where the system already has mandatory control over the consequence, rather than requiring a separate remote gateway.¶

At the RAN layer, AI-based channel estimation, beamforming, link adaptation, scheduler decisions, power changes, cell activation/deactivation, and handover or topology changes can be represented as Candidate Acts. The model may infer correctly and the control software may be authorized to operate, while the actual radio-state transition remains non-effective until current traffic, coverage, interference, policy, emergency, freshness, and protected-state conditions are verified.¶

Samsung's 2026 work with NVIDIA validates multi-cell vRAN on accelerated computing, while Samsung describes its software-driven architecture as able to use CPUs for rapid AI deployment and GPUs for heavier workloads. This creates a natural separation between the compute plane that produces a radio decision and the hardware or network boundary that makes the decision effective. See Samsung and NVIDIA AI-RAN; Samsung MWC 2026 software-driven networks.¶

ISAC broadens the same problem from actuation to sensing. Samsung describes ISAC as a candidate 6G capability built on radio and AI foundations, with sensing intelligence embedded in the network architecture. The execution-finality model can separately control acquisition, fusion, retention, precision, disclosure, model ingestion, external API exposure, or actuation derived from sensing information. See Samsung ISAC.¶

This means the same physical infrastructure can support both communication and sensing without treating successful sensing computation as automatic authority to disclose or operationally use the resulting information. A sensing result can remain non-effective for a particular downstream use even though the radio system validly measured it.¶

For autonomous-network functions, the complement is similar: Samsung's automation logic can remain machine-speed and closed-loop, while the protected finality state binds the exact remediation, destination, live topology, policy epoch, relevant measurements, and effectuation point. The architecture therefore adds consequence-specific authority without requiring a human approval step.¶

Nothing in this comparison asserts that Samsung products lack equivalent safety, isolation, authorization, or hardware controls. The public architecture is used to show how execution-finality semantics could complement software-defined RAN, heterogeneous silicon, AI-native radio, autonomous operations, and sensing as those capabilities move toward 6G.¶

2.17. Ericsson

Ericsson is particularly relevant because its current AI-native RAN direction reaches directly into radios, purpose-built RAN Compute, Cloud RAN, management and orchestration, and custom Ericsson Silicon. The execution-finality question therefore exists not only at an application or orchestration layer but also at microsecond radio and silicon-adjacent boundaries.¶

Ericsson's AI in RAN software places telco-grade AI models in basebands and radios, supports continuous learning and agentic automation, and is designed for microsecond-level real-time inference. Ericsson states that the software runs on RAN Compute and radios powered by Ericsson Silicon and can also be ported to partner platforms through Cloud RAN. See Ericsson AI in RAN; Ericsson AI-native RAN announcement.¶

The silicon-level relevance is unusually concrete. Ericsson publicly describes purpose-built ASICs with large numbers of DSP cores, on-chip memory for deterministic microsecond behavior, and next-generation Ericsson Silicon with programmable neural-network accelerator matrix cores in AI-ready radios. Ericsson Silicon also spans RAN Compute, radio, and transport. See Ericsson: AI inference in the RAN compute fabric; Ericsson AI-ready radios and neural-network accelerators; Ericsson Silicon.¶

The proposed architecture complements that hardware/software co-design by treating the AI inference result and the resulting hardware consequence as different states. An AI-native scheduler, beamformer, link-adaptation model, positioning model, or multi-layer coordination function can execute successfully while the exact radio or resource-changing operation remains a Candidate Act until the consequence boundary verifies current authority.¶

At a purpose-built silicon boundary, a Finality Sink need not be an external service. It can be realized as a protected state machine, register-controlled release, firmware or microcode gate, queue or scheduler commit point, radio-control primitive, packet-egress gate, protected memory transition, or another function with mandatory control over the operation. The critical property is not physical separation; it is that the compute path cannot independently complete the protected effect without successful finality verification.¶

This is particularly relevant for microsecond paths. The proposal does not require a slow policy round trip or human approval. A hot-path implementation can use pre-established protected state, bounded descriptors, compact evidence, monotonic state, and hardware-local verification, while slower policy, audit, revocation distribution, or external anchoring can remain off the critical path where appropriate.¶

At the Cloud RAN layer, Ericsson software can run on COTS platforms and partner accelerators. That portability makes execution-finality semantics useful as a hardware-neutral invariant: the same Candidate Act and scope can be enforced whether the effectuation point is implemented in Ericsson Silicon, Intel Xeon-class infrastructure, NVIDIA accelerated infrastructure, or another validated platform.¶

Ericsson also describes 6G as an intelligent fabric with AI integrated across radio, RAN Compute, software, transport, OSS/BSS, management, and core. Execution finality can complement that distributed intelligence by making authority composable across those domains: an upstream agent or model can propose an act, but each protected consequence boundary can verify the exact act and current state before effectuation. See Ericsson 6G intelligent fabric.¶

This treatment also aligns with Ericsson's emphasis on deterministic execution budgets. The location and cost of a Finality Sink must be chosen according to the timing class of the operation: a lower-layer radio act may need an on-chip or tightly coupled verification path, while orchestration, deployment, configuration, or service actions can tolerate richer validation outside the microsecond data path.¶

Nothing in this comparison asserts that Ericsson Silicon, RAN Compute, Cloud RAN, radios, or automation systems lack equivalent controls. Ericsson's public architecture is cited because it provides a concrete example of AI moving all the way into custom telecom silicon and real-time radio execution, where a machine-enforced distinction between computation and authority can be evaluated technically rather than abstractly.¶

2.18. Huawei

Huawei is relevant across Agentic MBB, AI-Centric Networks, RAN Agent, Adaptive Air, Agentic Core, AI-native operations, autonomous networks, 5G-Advanced evolution toward 6G, integrated radio intelligence, and 6G native trustworthiness. Its public direction already treats intelligence, networking, sensing, compute, services, and trust as tightly integrated, so the comparison must be framed as a complementary enforcement primitive rather than as a generic claim that AI-native networks lack trust mechanisms.¶

Huawei's 2026 Agentic MBB architecture combines a RAN Agent with Adaptive Air. Huawei describes the RAN Agent as using telecom foundation models and a RAN digital twin to generate network-wide policies, while Adaptive Air integrates intelligent algorithms into radio and baseband and connects the agent's decisions toward the live radio system. See Huawei Agentic MBB.¶

That closed loop is a direct execution-finality use case. Forecasting, reasoning, digital-twin simulation, intent interpretation, and policy generation can all be successful while the resulting radio or baseband operation remains a Candidate Act. The finality layer asks whether the exact live transition still corresponds to the authorized service intent, current network state, protected policy, freshness, scope, and the actual hardware or network boundary that will make it effective.¶

At the hardware and radio boundary, the public material describes next-generation radio and baseband systems, large antenna arrays, new filters and power-amplifier technologies, and intelligent algorithms integrated into the radio path. The proposed architecture does not require knowledge of proprietary internal silicon. It can be mapped functionally to whichever protected register, scheduler, baseband control, radio-control, packet, memory, accelerator, firmware, or device boundary has mandatory control over the resulting consequence.¶

Huawei's AI-Centric Network direction extends intelligence across service, network, and network-element layers, while Agentic Core describes intent-driven customization and agent-oriented services. See Huawei AI-Centric Network; Huawei Agentic Core Networks.¶

Execution finality can complement this multi-layer intelligence by preventing an authority collapse between levels. A service-layer agent can request an outcome, a network-layer agent can derive a policy, and an NE-level controller can calculate an implementation, while no upstream artifact alone becomes bearer authority for the final radio, route, session, core-state, data-exposure, or physical effect. Each consequential boundary can verify a scoped, non-bearer authority tied to the exact operation and current state.¶

Huawei's native-trustworthiness work is also directly relevant. It treats security, privacy, and resilience as lifecycle and architectural properties of 6G. Execution finality is narrower: it specifies an ordering discipline in which a consequence remains technically non-effective until exact act descriptors, protected evidence, protected state, freshness, scope, and the effectuation boundary correspond. See Huawei 6G Native Trustworthiness.¶

This narrowness is useful because it can be inserted as a concrete enforcement primitive inside a broader trustworthy architecture. Native trustworthiness may establish identities, secure channels, resilient operation, privacy policies, and trusted lifecycle state; execution finality can consume those results as predicates but still require the final protected transition to bind them to the actual pending act.¶

The same model applies to agent-to-agent and agent-to-network communication. Huawei's Agent Communication Network direction contemplates digital identity, task sessions, and collaboration among very large numbers of agents. Execution finality can preserve that interoperability while preventing possession of an agent identity, session, or upstream intent artifact from becoming unrestricted authority to produce a downstream network or physical consequence.¶

For autonomous O&M, digital twins and predictive systems can remain fast and proactive. A remediation can be generated and simulated automatically, while the exact command that changes topology, routing, configuration, power, capacity, or service state remains subject to a current finality check at the relevant protected boundary.¶

Nothing in this comparison asserts that Huawei's products or research omit equivalent mechanisms. Huawei's public work may overlap with individual elements of this architecture. The purpose of the comparison is to show how a consequence-bound, non-bearer finality primitive could fit inside AI-centric, agentic, radio-integrated, and natively trustworthy 6G systems.¶

2.19. AT&T

AT&T is relevant not only because of Open RAN, but because its network modernization combines open-capable radio hardware, Cloud RAN, third-party rApps, RAN Intelligent Controller programmability, AI-native RAN functions, network APIs, and increasingly software-defined infrastructure. This is an operator environment in which decisions can cross vendor, software, cloud, and hardware boundaries before becoming live network effects.¶

AT&T reported in 2026 that more than half of its network traffic was already carried on open-capable hardware, that it had completed a commercial Open RAN call using Ericsson basebands and 1Finity radios, that Cloud RAN was live in two cities, and that it had deployed a third-party rApp to optimize the live production network through Ericsson's Intelligent Automation Platform. See AT&T Open RAN readiness.¶

Execution finality complements that openness by creating an operator-controlled invariant across vendor boundaries. A third-party rApp can be authorized to analyze network state and propose an optimization without the rApp itself becoming the final authority for the resulting radio or configuration change. The same applies to a vendor controller, cloud workload, or AI model.¶

The relevant Finality Sink can be placed at the point AT&T or its infrastructure actually controls the consequence: an orchestration commit, RIC-to-RAN enforcement point, scheduler or baseband transition, radio-control path, network API boundary, packet-egress path, configuration database commit, or another protected point that makes the requested state operational.¶

The silicon-level relevance is also concrete in AT&T's Cloud RAN work. AT&T and Ericsson demonstrated AI-native Link Adaptation on a Cloud RAN configuration powered by Intel Xeon 6 SoC, showing portable AI RAN software running on COTS silicon with integrated AI acceleration. See AT&T and Ericsson Cloud RAN on Intel Xeon 6 SoC.¶

In such a deployment, the execution-finality model can be implemented below the application layer. Candidate Acts may include changes to accelerator or CPU scheduling, memory/DMA access, network queues, packet release, RAN parameters, or software-controlled hardware state. The protection point may be associated with the hypervisor, kernel, driver, IOMMU, network interface, accelerator runtime, baseband interface, or another component with mandatory control over the effectuation resource.¶

This does not mean every packet or radio event should incur a heavyweight remote authorization exchange. The architecture permits hot-path enforcement using compact, pre-bound and hardware-local state for operations that must remain deterministic, while richer policy, provenance, revocation distribution, or audit mechanisms can operate off-path or at slower control intervals.¶

AT&T is also exposing network capabilities through APIs and supporting telco-specific AI models. Those developments make the distinction between access to a capability and authority for each concrete effect increasingly important. See AT&T Network APIs; AT&T Open Telco AI.¶

A network API credential, an rApp installation, a valid model, or a successful Cloud RAN inference can therefore be treated as necessary but not sufficient authority. The actual QoS change, network-data exposure, radio parameter update, route, slice, or other protected effect can remain bound to exact parameters, current operator policy, freshness, protected state, and the boundary that makes the change effective.¶

The architecture is therefore complementary to AT&T's move toward a more open, programmable, cloud-native and AI-assisted network: openness determines who and what can participate; execution finality determines the conditions under which a particular proposed consequence becomes authoritative.¶

Nothing in this comparison asserts that AT&T's network lacks equivalent policy, security, authorization, or hardware controls. The public deployments are used to identify realistic operator-controlled boundaries at which act-specific finality could be implemented across multi-vendor software and silicon.¶

2.20. Verizon

Verizon is relevant across multi-vendor Open RAN, RAN Intelligent Controller deployments, large-scale closed-loop automation, Level-4 autonomy goals, network slicing, AI infrastructure, and its 6G Innovation Forum. These directions move network decisions from human-operated configuration toward continuously generated machine actions, making the final transition from recommendation to live effect increasingly important.¶

Verizon's multi-vendor RIC deployment integrates Samsung's AI-powered Energy Saving Manager with Qualcomm's Dragonwing RAN Intelligent Controller in the commercial network. The system can automatically switch cells or transmission paths off during low traffic and restore them as demand changes. See Verizon multi-vendor RIC deployment.¶

Execution finality complements that architecture by treating the AI result, the rApp/RIC decision, and the live hardware/network state transition as separable stages. The AI may correctly predict low traffic and the RIC may validly authorize an optimization, while the exact cell, carrier, path, or radio-resource transition remains a Candidate Act until current coverage, traffic, emergency, resilience, policy, freshness, and protected-state predicates are satisfied.¶

At the silicon and hardware boundary, Verizon does not need to own the underlying chip design for finality to be operator-enforceable. The Finality Sink can be implemented in or adjacent to the vendor baseband, RAN accelerator, server CPU/GPU, NIC/DPU, virtualization layer, driver, scheduler, packet-forwarding path, or radio-control interface, provided the selected point has mandatory control over the requested consequence.¶

The relevance becomes broader in Verizon's 2026 autonomy roadmap. Verizon states that it is targeting Level-4 cognitive automation in parts of the core network and reported more than 70 million autonomous network configuration changes in 2025, with AI agents increasingly embedded in network operations. See Verizon network autonomy.¶

At that scale, execution finality is not a proposal to reinsert a human into each loop. It is a proposal to keep the loop autonomous while requiring the effectuation stage to be independently machine-verifiable. An agent can reason, diagnose, plan, and prepare an action at machine speed; the protected boundary can then verify that the exact command, target, current topology, policy epoch, scope, and replay state correspond before the live network changes.¶

Verizon's 6G Innovation Forum further places this issue in a future multi-vendor ecosystem involving network vendors and chipset/device innovators. Verizon describes the objective as an open, diversified and resilient AI-enabled 6G ecosystem. See Verizon 6G Innovation Forum.¶

A consequence-bound finality layer can complement such diversity because it is not tied to one vendor's control plane or silicon. The same act-bound descriptor and non-bearer authority semantics can be mapped to different radio, server, accelerator, transport, core, and edge implementations, allowing the operator to preserve a common invariant even where the physical enforcement mechanisms differ.¶

Network slicing illustrates another boundary. Software can dynamically configure service-specific resources and isolation, but the creation, resizing, rerouting, priority change, or teardown of a protected slice is itself a network effect. Finality can bind the exact requested slice transition to current service policy, emergency priority, tenant identity, resource limits, topology, and the control point that makes the new state active.¶

Nothing in this comparison asserts that Verizon's current automation, RIC, slicing, or 6G work lacks equivalent controls. The public deployments show that high-volume autonomous change is becoming normal network operation, which makes precise machine-enforced authority at the final effectuation boundary a practical engineering question rather than an abstract governance concept.¶

2.21. Orange

Orange is relevant across network APIs, AI-agent tool access, AI-RAN experimentation, semantic communications, AI-native communications research, network automation, digital twins, and the evolution toward more intelligent and software-defined operator infrastructure. Its work illustrates both sides of execution finality: AI controlling the network, and the network exposing capabilities to AI.¶

Orange has contributed a CAMARA MCP provider implementation that exposes CAMARA-compliant Network APIs as native tools for AI assistants, including capabilities such as device location, SIM-swap information, identity verification, and quality of service. See Orange CAMARA MCP Provider Implementation.¶

The complement is direct: MCP or API interoperability determines how an agent can discover and invoke a capability; execution finality determines whether a specific invocation is permitted to cause its requested consequence. A valid API credential, valid MCP session, and syntactically valid request can remain insufficient when the exact requested location disclosure, QoS change, identity operation, or other network effect is outside current purpose, consent, scope, quota, jurisdiction, freshness, or operator policy.¶

Orange is also collaborating with Nokia on AI-RAN use cases supported by NVIDIA AI infrastructure, including spectral-efficiency improvement, predictive radio optimization, and exploration of ISAC. See Nokia and Orange AI-RAN collaboration.¶

That creates silicon and accelerator boundaries even though Orange is an operator rather than a chip vendor. AI inference can run on GPU-accelerated or other heterogeneous infrastructure, while the effectuation authority can terminate at an operator-controlled RAN scheduler, baseband or radio-control path, GPU/accelerator runtime, DMA or network interface, orchestration commit, or packet-egress boundary. The operator can therefore preserve act-specific authority independently of which vendor supplies the underlying silicon.¶

Orange and CEA's AI-Native Communications laboratory is researching semantic communications in which networks increasingly convey or process meaning according to intended use rather than merely transporting raw bits. See Orange-CEA AI-Native Communications laboratory; Orange semantic communications roadmap.¶

Semantic and meaning-aware communication makes consequence binding more important, not less. If a system transforms, summarizes, prioritizes, suppresses, routes, or reconstructs information based on semantic intent, the relevant Candidate Act can bind not only the transport endpoint but also the permitted semantic transformation, disclosure precision, recipient, purpose, and downstream use class.¶

For sensing and digital-twin-driven operations, the same architecture can distinguish acquisition from use. A network may legitimately sense or infer a condition while disclosure, cross-domain fusion, external API release, automated remediation, or physical actuation remains separately finality-controlled.¶

At the operator-control layer, finality can be implemented through existing gateways, policy brokers, network API enforcement points, service-mesh components, RIC/SMO functions, radio-control boundaries, or hardware-adjacent egress and memory controls. It need not create a second network architecture; it gives selected existing boundaries a stronger act-specific authority contract.¶

The proposed model therefore complements Orange's API, AI-RAN, semantic-communication and autonomous-network work by preserving a distinction between capability availability, inference, and final authority for the exact consequence. This is especially relevant as AI assistants and autonomous systems become direct consumers of operator-controlled network functions.¶

Nothing in this comparison asserts that Orange systems lack equivalent safeguards. Orange's public work is cited because it shows an operator simultaneously moving toward AI-native infrastructure and exposing network capabilities to autonomous software, two trends that make machine-verifiable effectuation authority increasingly important.¶

2.22. Deutsche Telekom

Deutsche Telekom is relevant across agentic network operations, autonomous RAN and multi-domain remediation, Open RAN, AI-native and cloud-based RAN, 6G research, integrated sensing and positioning, high-performance distributed compute, Physical AI, and sovereign AI infrastructure. Its public direction is therefore a strong example of networks evolving from transport systems into machine-operated execution fabrics.¶

Deutsche Telekom's RAN Guardian Agent is already operating in Germany, and the company reported more than 100 autonomously triggered remediation actions in its first month. MINDR extends the same multi-agent approach across RAN, transport, and core using collaborative agents. See Deutsche Telekom MINDR and RAN Guardian Agent.¶

Execution finality complements this architecture by preserving autonomy while separating generation of a remediation from authority for the remediation. An agent can detect, diagnose, reason, simulate, select, and prepare an action without waiting for human approval; the exact topology, configuration, routing, radio, transport, or core-state transition can remain non-effective until a protected boundary verifies the current act descriptor, evidence, scope, freshness, policy epoch, and protected state.¶

This is especially useful in a multi-agent system. A diagnosis agent, planning agent, domain agent, and execution agent can cooperate without any intermediate message becoming a reusable bearer token for the final network effect. Delegation artifacts can remain scoped to the exact task and downstream sink, while the final network boundary independently reconstructs or verifies the pending operation.¶

Deutsche Telekom is also expanding AI-native and Open RAN work with Nokia, including Cloud RAN, open interfaces, multivendor flexibility, and autonomous RAN validation. See Nokia and Deutsche Telekom AI-native/Open RAN collaboration.¶

For such disaggregated infrastructure, the silicon-layer enforcement point may reside in vendor baseband hardware, COTS CPU/GPU infrastructure, an accelerator runtime, IOMMU/DMA boundary, NIC or DPU, hypervisor, kernel or driver, radio-control path, or packet-forwarding interface. Deutsche Telekom does not need a single proprietary chip architecture for the finality invariant to remain stable across vendors.¶

The joint T-Mobile/Deutsche Telekom 6G Innovation Hub is explicitly focused on fully AI-native 6G, autonomous networks, secure wide-area sensing and positioning, convergence of connectivity with high-performance compute, and Physical AI systems that interact with and control the physical world in real time. See Deutsche Telekom and T-Mobile 6G Innovation Hub.¶

Physical AI makes the consequence boundary especially important. A network may transport intent, context, timing, sensor data, or machine commands with deterministic latency, but the fact that the network delivered the data correctly does not by itself establish that a robot, vehicle, industrial system, or other actuator should execute the resulting command. Execution finality can bind the final act to the exact device, state, safety envelope, timing window, jurisdiction, and effectuation controller.¶

The same principle applies to sensing. Secure sensing and positioning can produce valid measurements while downstream fusion, disclosure, precision release, model ingestion, or actuation remains separately controlled. This lets the network support rich sensing without collapsing sensing authority into unrestricted use authority.¶

Deutsche Telekom also operates large AI infrastructure built with NVIDIA and other partners. Although that infrastructure is not itself a RAN product, it reinforces the broader convergence of connectivity and accelerated compute. See Deutsche Telekom Industrial AI Cloud.¶

Across these environments, execution finality can be implemented at the lowest practical consequence boundary: accelerator or memory release, network egress, orchestration commit, RAN state activation, API effectuation, storage admission, or actuator command. The architecture is therefore compatible with both centralized AI factories and distributed edge/RAN execution.¶

The key complement is not human oversight but authority separation. Deutsche Telekom's networks can remain autonomous and AI-native while selected consequences require a separately enforced, machine-speed finality condition. This converts closed-loop computation into closed-loop authority with an explicit protected boundary rather than assuming that a successful inference or agent decision is already sufficient authority.¶

Nothing in this comparison asserts that Deutsche Telekom's internal systems lack equivalent controls. Its public work demonstrates the direction of travel toward autonomous, multi-agent, sensing-aware, compute-converged 6G systems in which consequence-bound authorization can be evaluated as an engineering primitive.¶

2.23. Common Point of Comparison

The twenty-one organizations above approach future networks and compute infrastructure from different directions.¶

Qualcomm emphasizes AI-native radio infrastructure and agentic RAN management.¶

Nokia emphasizes open, programmable AI-native RAN, anyRAN software, accelerated merchant-silicon deployment paths, and a software-defined path toward 6G.¶

NVIDIA provides a common accelerated computing foundation for AI and RAN workloads.¶

Arm provides foundational CPU, system-IP, interconnect, memory/I/O, security, mobile, edge, DPU, chiplet, and infrastructure substrates across which effectuation boundaries can be implemented.¶

AMD provides rack-scale GPU, CPU, HBM, chiplet, networking, and open-fabric infrastructure for large AI systems.¶

Intel provides CPU-centered heterogeneous AI infrastructure, Ethernet, CXL-capable platforms, and infrastructure-processing components relevant to data movement and orchestration boundaries.¶

Broadcom provides AI switching, NIC, PCIe, custom-silicon, SerDes, and co-packaged-optics infrastructure spanning electrical and photonic effectuation boundaries.¶

Marvell provides custom AI silicon, multi-chip packaging, CXL/PCIe connectivity, die-to-die interconnect, switching, and optical infrastructure relevant to chiplet, memory, and fabric finality.¶

Google integrates custom TPUs, GPUs, HBM, accelerator interconnects, large-scale data-center fabrics, optical circuit switching, storage, and agent-aware orchestration in AI Hypercomputer.¶

Microsoft integrates Maia accelerators, Cobalt CPUs, Azure Boost DPUs, HSMs, networking, storage, and agent infrastructure through a silicon-to-systems architecture.¶

Amazon Web Services combines Trainium and Inferentia accelerators, Nitro hardware, EFA/RDMA networking, UltraClusters, storage, and cloud-control boundaries at AI-factory scale.¶

Meta combines custom MTIA accelerators, NIC chiplets, GPU clusters, RDMA/Ethernet fabrics, advanced packaging, and automated low-level software optimization.¶

Cisco provides high-capacity Silicon One switching, AI-fabric systems, congestion and forwarding control, and electrical/optical egress infrastructure.¶

HPE integrates Cray AI/HPC compute, Slingshot-class fabrics, storage, multi-tenant orchestration, power, and direct liquid cooling into system-level AI infrastructure.¶

Samsung develops AI RAN, software-driven vRAN across CPU/GPU platforms, ISAC, autonomous-network capabilities, and AI-native 6G infrastructure.¶

Ericsson integrates real-time AI into radios, RAN Compute, Cloud RAN, management layers, and custom Ericsson Silicon with AI acceleration.¶

Huawei combines Agentic MBB, AI-centric network elements, radio/baseband intelligence, autonomous operations, agentic core functions, and 6G native trustworthiness.¶

AT&T is introducing increasingly open, multi-vendor, and third-party-programmable RAN infrastructure.¶

Verizon has demonstrated commercial multi-vendor AI-assisted RAN control.¶

Orange is connecting AI-agent ecosystems to standardized operator Network APIs and researching AI-native communications.¶

Deutsche Telekom is deploying agentic network control and researching AI-native 6G for sensing, compute convergence, and Physical AI.¶

These developments are not presented as competing approaches to the architecture in this document.¶

They establish the environment in which the question becomes important.¶

The common technical issue is what happens after intelligence has done its job.¶

After a model has inferred.¶

After an agent has planned.¶

After a digital twin has simulated.¶

After an rApp has optimized.¶

After a scheduler has selected.¶

After an accelerator has computed.¶

After a sensing system has derived.¶

After an API request has authenticated.¶

After a policy engine has evaluated.¶

There may still remain one final distinction:¶

Has this exact Candidate Act acquired current, machine-verifiable authority to cross this exact consequence boundary?¶

The Enforcement Profiles that follow examine that question at concrete network, radio, compute, device, data, cryptographic, rendering, sensing, and physical-effect boundaries.¶

The comparison above is necessarily incomplete. Industry architectures continue to evolve rapidly, and relevant unpublished, proprietary, standards-based, or differently named mechanisms may already provide equivalent properties. Technical corrections, references to overlapping work, counterexamples, implementation experience, and criticism of the proposed boundaries are explicitly welcomed.¶

Note :¶

Technical disagreement is welcome. If any organization, standards participant, implementer, researcher, or reviewer can identify an existing mechanism that already provides the same execution-finality property, a better placement for a Finality Sink, an unaddressed bypass path, a latency or availability limitation, or an incorrect characterization of published work, that feedback is directly relevant to this document and should be raised so that the text can be corrected.¶

6. Enforcement Profiles

This section contains 83 Enforcement Profiles. Profiles 6 through 209 retain the 63 source identifiers selected from the supplied GPU, silicon, chiplet, memory-fabric, and AI-factory source set. Profiles 210 through 221 are supplementary engineering profiles that apply the common model to current hyperscale and Arm-based infrastructure directions. Profiles 222 through 229 selectively adapt non-duplicative mechanisms from companion DAS Protocols V, VI, and VIII disclosures to silicon and AI-factory boundaries. These later additions do not alter the provenance of the original 63 source-derived profiles.¶

Each profile inherits the Common Execution-Finality Model. The Feature and Problem Addressed fields are retrieval aids. The remaining paragraphs preserve the source-specific operation, descriptor, authority, protected-state, capability, and sink details in engineering rather than patent-claim syntax.¶

Original source-derived profile identifiers: 6, 10, 11, 12, 18, 25, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 59, 60, 61, 62, 94, 102, 111, 114, 130, 136, 145, 146, 148, 149, 150, 153, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 202, 203, 204, 205, 206, 208, 209.¶

6.1. Profile 6 — AI Accelerator Tensor and Output Egress Finality

Feature: Applies execution-finality enforcement to AI Accelerator Tensor and Output Egress, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents sensitive model-derived, tensor, telemetry, debug, or intermediate-compute data from leaving the protected accelerator or infrastructure domain through a valid-but-mis-scoped, stale, replayed, substituted, or alternate egress path.¶

The Candidate Act includes an artificial-intelligence tensor transfer, activation transfer, embedding transfer, model-state update, key-value cache transfer, memory-page transfer, latent-state transfer, gradient transfer, accelerator-to-accelerator data movement, or artificial-intelligence output release.¶

The applicable Finality Sink may be an artificial-intelligence accelerator egress controller, protected memory controller, high-bandwidth-memory controller, direct-memory-access controller, remote-direct-memory-access controller, on-die fabric controller, chiplet interconnect controller, accelerator-fabric switch, tensor-egress gate, SmartNIC, DPU, or protected output controller.¶

The Protected Enforcement Domain validates authority predicates including a runtime behavioral descriptor condition, semantic-drift condition, no-secondary-use condition, anti-distillation budget condition, tenant authorization condition, model authorization condition, purpose authorization condition, confidentiality condition, destination-sink authorization condition, or data-export authorization condition.¶

The Protected Enforcement Domain updates or consumes applicable protected state corresponding to the Candidate Act, commits applicable protected validation evidence representing the validated authority predicates and protected state transition, and authorizes availability or use of a scoped non-bearer capability bound to the Candidate Act, the act descriptor or digest thereof, the applicable protected validation evidence, the protected state transition, and the applicable Finality Sink.¶

The applicable Finality Sink is configured to prevent the tensor transfer, activation transfer, embedding transfer, model-state update, key-value cache transfer, memory-page transfer, latent-state transfer, gradient transfer, accelerator-to-accelerator data movement, or artificial intelligence output release unless the scoped non-bearer capability is successfully verified before egress from the accelerator, memory controller, fabric switch, interconnect boundary, protected output path, external memory interface, or network interface.¶

The Candidate Act remains non-effective if any load-bearing descriptor binding, authority predicate, protected-state condition, validation-evidence commitment, freshness or replay check, permitted scope, or sink-local verification required by this profile fails.¶

6.2. Profile 10 — Accelerator-Fabric Tensor Egress Finality

Feature: Applies execution-finality enforcement to Accelerator-Fabric Tensor Egress, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a tensor transfer, activation transfer, embedding transfer, gradient transfer, key-value cache transfer, memory-page transfer, model-state transfer, latent-state transfer, accelerator-to-accelerator data movement, or artificial intelligence output egress within an artificial-intelligence accelerator system.¶

The applicable Finality Sink may be a silicon-level memory controller, high-bandwidth-memory controller, protected memory controller, accelerator egress controller, ondie fabric switch, interposer fabric switch, chiplet interconnect controller, accelerator-toaccelerator interconnect controller, direct-memory-access controller, remote-direct-memory-access controller, SmartNIC, DPU, tensor-egress gate, or protected output controller.¶

The Protected Enforcement Domain validates authority predicates including tenant authorization, model authorization, purpose authorization, no-secondary-use authorization, no-training authorization, data-export authorization, jurisdictional authorization, confidentiality authorization, extraction-budget state, runtime-behavior state, semantic-drift state, or destination-sink authorization.¶

The scoped non-bearer capability is a scoped non-bearer tensor-transfer capability bound to the Candidate Act, the applicable machine-verifiable act descriptor or a digest thereof, the committed applicable protected validation evidence, the updated or consumed applicable protected state, the applicable execution-finality boundary, the applicable Finality Sink, and a nonce, freshness value, temporal scope, destination scope, resource scope, or permitted tensor-transfer scope.¶

The applicable Finality Sink is configured to prevent the tensor transfer, activation transfer, embedding transfer, gradient transfer, key-value cache transfer, memory-page transfer, model-state transfer, latent-state transfer, accelerator-to-accelerator data movement, or artificial intelligence output egress unless the scoped non-bearer tensor-transfer capability is successfully verified before egress from the accelerator, memory controller, fabric switch, interconnect boundary, protected output path, external memory interface, or network interface.¶

6.3. Profile 11 — Runtime Behavioral Descriptor and Protected AI Output Pending-State Finality

Feature: Applies execution-finality enforcement to Runtime Behavioral Descriptor and Protected AI Output Pending-State, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents the underlying accelerator, controller, fabric, runtime, or management plane from treating successful computation or configuration as sufficient authority for the exact consequential state transition.¶

The Candidate Act includes release of an artificial intelligence output, token stream, reasoning trace, tool instruction, code output, content output, multimodal output, latent-state decode, model-generated command, agentic action, or artificial intelligence generated informational artifact.¶

The applicable Finality Sink may be an output buffer controller, token emitter, decoder, renderer, API response gate, tool-call dispatcher, memory writer, contentdelivery path, accelerator egress controller, protected output path, latch-controlled emission buffer, FIFO output queue, protected memory page, DMA-disabled memory region, or HBM-resident provisional buffer.¶

Where, before verification of the scoped non-bearer capability, the Candidate Act is held in a noneffective pending state within at least one of a protected output buffer, output register, latchcontrolled emission buffer, FIFO queue, decoder buffer, token-emission buffer, protected memory page, HBM-resident provisional buffer, DMA-disabled memory region, or equivalent physical or logical holding structure.¶

The applicable authority predicates comprise a runtime behavioral descriptor condition generated from observed runtime behavior of the artificial-intelligence system during generation of the Candidate Act.¶

The runtime behavioral descriptor condition is evaluated against an approved behavioral envelope, constitutional envelope, safety envelope, semantic-drift envelope, model-behavior envelope, output-class envelope, policy-conformance envelope, or anti-distillation envelope before release, validation, proof, or permission of the scoped non-bearer capability.¶

The Protected Enforcement Domain updates or consumes applicable protected state, commits applicable protected validation evidence representing at least the runtime behavioral descriptor condition, the approved envelope, the decision result, and the applicable protected state transition, and authorizes availability or use of the scoped non-bearer capability.¶

The scoped non-bearer capability is a scoped non-bearer AI-output capability bound to the Candidate Act, the applicable machine-verifiable act descriptor or a digest thereof, the committed applicable protected validation evidence, the updated or consumed applicable protected state, the applicable execution-finality boundary, the applicable Finality Sink, and a nonce, freshness value, temporal scope, destination scope, resource scope, or permitted output scope.¶

The applicable Finality Sink is configured to maintain read, transmit, render, dispatch, decode, write, emit, memory-access, DMA, decoder, token-emission, or output-path enablement in a disabled state until the scoped non-bearer AI-output capability is successfully verified, and to withhold, suppress, degrade, quarantine, zeroize, overwrite, or block the Candidate Act when the runtime behavioral descriptor condition fails to satisfy the approved envelope within an applicable latency budget.¶

6.4. Profile 12 — Silicon-Backed Anti-Distillation and Extraction-Budget Finality

Feature: Applies execution-finality enforcement to Silicon-Backed Anti-Distillation and Extraction-Budget, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents repeated or aggregated output release from exceeding a protected extraction or distillation budget, including rollback, replay, destination substitution, and reasoning-trace or model-derived data siphoning.¶

The Candidate Act includes release of an artificial intelligence output, token stream, reasoning trace, embedding, activation, latent representation, logit vector, key-value cache state, synthetic-training example, model-response batch, tool-response trace, or other model-derived informational artifact.¶

The applicable protected state comprises a silicon-backed, hardware-isolated, cryptographically protected, monotonic, sealed, or rollback-resistant extraction-budget state corresponding to at least one of a user, tenant, session, model, endpoint, destination, output class, data class, reasoning class, or authority object.¶

The Protected Enforcement Domain updates or consumes the extractionbudget state before releasing or permitting the scoped non-bearer capability.¶

The scoped non-bearer capability is bound to the Candidate Act, the act descriptor or digest thereof, the extraction-budget state transition, applicable protected validation evidence, the applicable Finality Sink, nonce or freshness state, policy epoch, destination scope, and permitted output scope.¶

The applicable Finality Sink is configured to deny, drop, suppress, degrade, rate-limit, redact, quarantine, or withhold external effectuation of the Candidate Act when the extractionbudget state is exhausted, stale, mismatched, replayed, rolled back, unverifiable, or otherwise fails validation, thereby preventing unauthorized model distillation, excessive model extraction, reasoning-trace siphoning, or unauthorized model-derived data export before external effectuation occurs.¶

6.5. Profile 18 — SmartNIC, DPU, and Line-Rate Capability Verification

Feature: Applies execution-finality enforcement to SmartNIC, DPU, and Line-Rate Capability Verification, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents high-speed fabric admission, forwarding, routing, congestion, virtual-lane, credit, QoS, or line-rate egress decisions from inheriting authority merely from a valid session or cached control decision when the exact flow or tensor transfer is out of scope.¶

The applicable Finality Sink is physically instantiated within, integrated with, or operatively coupled to a Data Processing Unit, SmartNIC, programmable network switch, hardware-accelerated packet processor, network interface controller, accelerator egress controller, tensor-egress controller, packet-forwarding pipeline, or line-rate data-movement component.¶

The protected enforcement domain is configured to separate authority formation from latency-critical effectuation by compiling, deriving, caching, sealing, or pre-authorizing at least a portion of the applicable authority predicates into a cached capability entry, epoch-bound capability derivation, flow-bound capability derivation, session-bound capability derivation, route- bound capability derivation, tensor-bound capability derivation, or packet-class-bound capability derivation.¶

The scoped non-bearer capability is bound to at least one of the Candidate Act, act descriptor or digest thereof, flow identifier, packet class, tensor class, destination sink, boundary identifier, nonce, freshness value, policy epoch, protected state reference, permitted scope, or validation-evidence reference.¶

The Data Processing Unit, SmartNIC, programmable network switch, hardwareaccelerated packet processor, network interface controller, accelerator egress controller, tensor-egress controller, packet-forwarding pipeline, or line-rate data-movement component is configured to perform bounded local verification of the scoped non-bearer capability using the cached capability entry, epoch-bound capability derivation, flow-bound capability derivation, or protected state reference without requiring a synchronous remote authorization loop for each individual packet, frame, flow unit, tensor fragment, memory transfer, or forwarding event.¶

6.6. Profile 25 — AI/ML Lifecycle Management Finality

Feature: Applies execution-finality enforcement to AI/ML Lifecycle Management, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents an unapproved, revoked, provenance-defective, incompatible, or policy-mismatched model, adapter, checkpoint, container, tokenizer, or lifecycle transition from becoming active in the execution environment.¶

The Candidate Act includes an AI/ML lifecycle operation including at least one of model transfer, model provisioning, model activation, model deactivation, model update, model rollback, model selection, model switching, model retirement, federated-learning aggregation, federated-learning participation, training-data ingestion, model-telemetry export, inference-result emission, model-performance feedback, AI/ML policy update, or AI/ML deployment-state transition within a radio access network function, core network function, network data analytics function, management data analytics function, AI-RAN function, network automation function, or future radio-network AI/ML function.¶

The applicable Finality Sink may be an AI/ML lifecycle controller, model-loader boundary, modelmanagement function, NWDAF egress controller, MDAF egress controller, RAN intelligence controller, AI-RAN model controller, federated-learning aggregation boundary, inference-output boundary, or protected AI/ML deployment controller.¶

The Protected Enforcement Domain verifies a model-provenance condition, model-attestation condition, model-integrity condition, lifecycle-stage condition, training-data-jurisdiction condition, inference-purpose condition, deployment-policy condition, model-revocation condition, policy-epoch condition, descriptor-binding condition, or AI/ML safety-envelope condition.¶

The Protected Enforcement Domain updates or consumes protected lifecycle state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the AI/ML lifecycle operation, Candidate Act descriptor, policy epoch, and applicable AI/ML lifecycle Finality Sink.¶

The AI/ML lifecycle Finality Sink is configured to prevent completing the model transfer, provisioning, activation, update, rollback, switching, retirement, training-data ingestion, federated-learning aggregation, model-telemetry export, or inference-result emission in the absence of successful verification of the scoped non-bearer capability, such that the AI/ML lifecycle operation is technically non-completable rather than merely authorized, logged, audited, or policy-screened.¶

6.7. Profile 37 — Rack-Scale GPU Fabric Tensor-Egress Finality

Feature: Applies execution-finality enforcement to Rack-Scale GPU Fabric Tensor-Egress, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a rack-scale accelerator-fabric operation including at least one of inter-GPU tensor transfer, accelerator-to-accelerator memory synchronization, tensor-shard exchange, activation transfer, embedding transfer, logits transfer, KV- cache page transfer, gradient transfer, model-state transfer, or accelerator-fabric coherency update.¶

The applicable Finality Sink may be a GPU interconnect switch, accelerator-fabric controller, high-bandwidth interconnect switch, accelerator memorycoherency gate, GPU-to-GPU transfer controller, or protected tensor-egress component.¶

The Protected Enforcement Domain verifies a tenant-isolation condition, tensor-class condition, model-owner condition, no-training condition, extraction-budget condition, destination-vault condition, policy-epoch condition, revocation condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected tensor-transfer state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the tensor-transfer descriptor and applicable accelerator-fabric Finality Sink.¶

The accelerator-fabric Finality Sink is configured to prevent routing, transferring, cohering, exposing, or admitting the tensor, tensor shard, activation, embedding, logits, gradient, KV-cache page, or model-state object unless the scoped non-bearer capability is successfully verified at or before the accelerator-fabric effectuation boundary.¶

6.8. Profile 38 — Sparse Expert-Routing and Mixture-of-Experts Finality

Feature: Applies execution-finality enforcement to Sparse Expert-Routing and Mixture-of-Experts, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents sparse expert selection, dispatch, capacity assignment, routing-table changes, or expert-output merging from crossing tenant, jurisdiction, model, data-class, extraction-budget, or routing-policy boundaries.¶

The Candidate Act includes a sparse model-routing operation including at least one of Mixture-of-Experts expert selection, expert activation, expert-token dispatch, expert-output merge, sparse routing-table update, model-parallel routing, tensorparallel shard routing, pipeline-parallel stage routing, or adaptive expert-load balancing.¶

The applicable Finality Sink may be an expert-router controller, GPU fabric switch, inference scheduler, model-parallel routing controller, MoE dispatch gate, or protected sparserouting component.¶

The Protected Enforcement Domain verifies an expert-authority condition, expert-jurisdiction condition, model-tenant condition, data-class condition, routing-policy condition, extraction-budget condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected expert-routing state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the sparse-routing descriptor and applicable expert-routing Finality Sink.¶

The expert-routing Finality Sink is configured to prevent dispatching a token to an expert, activating an expert, merging an expert output, or updating a sparse routing decision unless the scoped non-bearer capability is successfully verified before the sparse model-routing operation becomes compute-effective.¶

6.9. Profile 39 — GPU Interconnect Coherency-Domain Merge Finality

Feature: Applies execution-finality enforcement to GPU Interconnect Coherency-Domain Merge, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents coherent-memory, pooled-memory, cache, page, ownership, or visibility changes from exposing or mutating memory outside the authorized workload, tenant, address range, coherence domain, retention scope, or protected state.¶

The Candidate Act includes a coherency-domain operation including at least one of merging accelerator memory domains, extending a coherent GPU memory address space, granting peer-to-peer memory visibility, establishing shared accelerator memory, enabling unified accelerator addressing, or modifying a coherency directory for a multiGPU or rack-scale accelerator fabric.¶

The applicable Finality Sink may be a coherency controller, GPU interconnect switch, memory-directory controller, accelerator-memory management unit, fabric-attached memory controller, or protected coherency gate.¶

The Protected Enforcement Domain verifies a tenant-boundary condition, memory-vault condition, workload-attestation condition, model-owner condition, address- range condition, policy-epoch condition, revocation condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected coherencydomain state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the coherency-domain descriptor and applicable coherency Finality Sink.¶

The coherency Finality Sink is configured to prevent merging, extending, exposing, sharing, or admitting the accelerator memory domain unless the scoped non-bearer capability is successfully verified.¶

6.10. Profile 40 — CPU-to-GPU Coherent Memory and Chip-to-Chip Transfer Finality

Feature: Applies execution-finality enforcement to CPU-to-GPU Coherent Memory and Chip-to-Chip Transfer, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a CPU-to-accelerator coherentmemory operation including at least one of CPU-to-GPU memory mapping, coherent chip-to-chip transfer, host-to-accelerator DMA transfer, accelerator-to-host DMA transfer, shared-memory aperture creation, page-table permission update, memory-window registration, or processor-toaccelerator address-space exposure.¶

The applicable Finality Sink may be a chip-to-chip interconnect controller, coherent-memory controller, IOMMU, DMA gate, accelerator memory-management unit, CPU memory controller, or protected host-accelerator transfer gate.¶

The Protected Enforcement Domain verifies a workload-attestation condition, memory-range authorization condition, tenant-isolation condition, data-class condition, host-process authority condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected host-accelerator transfer state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the coherent-memory descriptor and applicable host-accelerator Finality Sink.¶

The host-accelerator Finality Sink is configured to prevent mapping, transferring, registering, exposing, or granting access to the memory range unless the scoped non-bearer capability is successfully verified.¶

6.11. Profile 41 — Collective Communication and Distributed Training Finality

Feature: Applies execution-finality enforcement to Collective Communication and Distributed Training, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents collective communication or distributed-training payloads from being synchronized with an unauthorized participant, tenant, model owner, data class, training purpose, or destination, including gradient and checkpoint exfiltration.¶

The Candidate Act includes a collective accelerator communication operation including at least one of all-reduce, all-gather, reduce-scatter, broadcast, parameter synchronization, gradient synchronization, checkpoint shard exchange, tensorparallel synchronization, pipeline-parallel synchronization, or distributed-training collective communication.¶

The applicable Finality Sink may be a collective-communication library boundary, GPU fabric switch, network interface controller, DPU, SmartNIC, accelerator collective engine, or protected collective-communication gate.¶

The Protected Enforcement Domain verifies a participant-set condition, tenantisolation condition, model-owner condition, gradient-class condition, training-purpose condition, no-ex ltration condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected collective-communication state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the collective-communication descriptor and applicable collective Finality Sink.¶

The collective Finality Sink is configured to prevent transmitting, reducing, gathering, scattering, broadcasting, or synchronizing the collective communication payload unless the scoped non-bearer capability is successfully verified.¶

6.12. Profile 42 — DPU / SmartNIC Confidential Infrastructure Finality

Feature: Applies execution-finality enforcement to DPU / SmartNIC Confidential Infrastructure, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents infrastructure offload components from releasing keys, admitting remote access, migrating confidential workloads, creating channels, or exporting data when attestation, endpoint, tenant, memory, direction, or policy state is mismatched.¶

The Candidate Act includes a DPU-controlled or SmartNIC-controlled infrastructure operation including at least one of secure telemetry export, network-policy offload, storage-policy offload, cryptographic key release, confidential workload migration, enclave admission, memory-enclave unlock, secure-channel creation, remote-attestation acceptance, or rack-scale infrastructure-policy update.¶

The applicable Finality Sink may be a DPU, SmartNIC, network interface controller, hardware root of trust, infrastructure offload engine, storage offload engine, security offload engine, or protected infrastructure controller.¶

The Protected Enforcement Domain verifies an attestation condition, key-release condition, workload-integrity condition, tenant-isolation condition, infrastructure-policy condition, model-owner condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected infrastructure state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the infrastructure-operation descriptor and applicable DPU or SmartNIC Finality Sink.¶

The DPU, SmartNIC, or infrastructure controller is configured to prevent releasing a key, accepting attestation, exposing plaintext, migrating a workload, admitting an enclave, or exporting telemetry unless the scoped non-bearer capability is successfully verified.¶

6.13. Profile 43 — SuperNIC / RDMA Memory-Key Finality

Feature: Applies execution-finality enforcement to SuperNIC / RDMA Memory-Key, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents remote or direct-memory access from becoming effective with the wrong memory region, endpoint, tenant, direction, queue, key, aperture, policy epoch, or replay state.¶

The Candidate Act includes a remote direct memory access operation including at least one of RDMA memory-key creation, RDMA memory-key reuse, RDMA queue-pair activation, GPU-direct memory exposure, remote accelerator memory read, remote accelerator memory write, storage-to-GPU transfer, GPU-to-storage transfer, or network-toGPU DMA operation.¶

The applicable Finality Sink may be a SuperNIC, network interface controller, RDMA engine, DPU, SmartNIC, memory-key controller, queue-pair controller, GPU-direct transfer gate, or protected DMA admission component.¶

The Protected Enforcement Domain verifies a memory-region authority condition, endpoint-attestation condition, tenant-isolation condition, data-class condition, transferdirection condition, nonce condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected RDMA or DMA-transfer state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the RDMA descriptor and applicable RDMA Finality Sink.¶

The RDMA Finality Sink is configured to prevent creating, reusing, accepting, or applying a memory key, queue pair, DMA window, or remote memory operation unless the scoped non-bearer capability is successfully verified.¶

6.14. Profile 44 — AI-Native Ethernet / InfiniBand Fabric Flow Finality

Feature: Applies execution-finality enforcement to AI-Native Ethernet / InfiniBand Fabric Flow, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes an AI-fabric network operation including at least one of accelerator-cluster packet forwarding, congestion-control state update, adaptive routing update, telemetry-driven path update, lossless Ethernet flow admission, InfiniBand flow admission, scale-out tensor- flow routing, storage-fabric routing, or AI-factory eastwest traffic steering.¶

The applicable Finality Sink may be a fabric switch, Ethernet switch, InfiniBand switch, adaptive-routing controller, congestion-control engine, DPU, SmartNIC, or protected AI-fabric forwarding component.¶

The Protected Enforcement Domain verifies a tenant-isolation condition, traffic-class condition, destination-vault condition, model-owner condition, lawful-routing condition, network-slice condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected AI-fabric flow state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the AI-fabric flow descriptor and applicable network-fabric Finality Sink.¶

The network-fabric Finality Sink is configured to prevent forwarding, steering, admitting, prioritizing, or applying the AI-fabric flow unless the scoped non-bearer capability is successfully verified.¶

6.15. Profile 45 — KV-Cache and Inference Context-Memory Finality

Feature: Applies execution-finality enforcement to KV-Cache and Inference Context-Memory, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents inference context, KV-cache pages, long-context state, or prefill/decode handoffs from being reused, migrated, retained, exposed, or scheduled outside the authorized user, tenant, purpose, retention, model-version, or session scope.¶

The Candidate Act includes an inference context-memory operation including at least one of KV-cache creation, KV-cache page transfer, KV-cache persistence, KV-cache eviction, KV-cache retrieval, long-context state loading, context-window migration, session-memory reconstruction, user-context reuse, or inference-context storage.¶

The applicable Finality Sink may be a KV-cache controller, context-memory store, DPU-controlled storage gateway, accelerator memory controller, inference scheduler, model-serving runtime, or protected context-memory boundary.¶

The Protected Enforcement Domain verifies a user-session authority condition, contextretention condition, no-training condition, privacy-budget condition, tenant-isolation condition, purpose-binding condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected context-memory state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the context-memory descriptor and applicable context-memory Finality Sink.¶

The context-memory Finality Sink is configured to prevent storing, retrieving, reusing, migrating, reconstructing, exposing, or admitting the KV-cache or inference-context object unless the scoped non-bearer capability is successfully verified.¶

6.16. Profile 46 — Disaggregated Prefill / Decode Serving Finality

Feature: Applies execution-finality enforcement to Disaggregated Prefill / Decode Serving, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a disaggregated inference-serving operation including at least one of prefill-stage assignment, decode-stage assignment, contextphase processing, generation-phase processing, prefill-to-decode handoff, long-context batch admission, token-generation scheduling, or model-serving pipeline transition.¶

The applicable Finality Sink may be an inference scheduler, prefill controller, decode controller, model-serving runtime, accelerator admission controller, GPU scheduler, context-memory controller, or protected inference-serving boundary.¶

The Protected Enforcement Domain verifies a session-authority condition, model-version condition, data-class condition, context-retention condition, output-class condition, tenantisolation condition, policy-epoch condition, revocation condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected inference- serving state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the inference-serving descriptor and applicable serving Finality Sink.¶

The inference-serving Finality Sink is configured to prevent assigning, handing off, scheduling, admitting, decoding, generating, or releasing tokens for the disaggregated inference operation unless the scoped non-bearer capability is successfully verified.¶

6.17. Profile 47 — Model Weight / Checkpoint / Container Artifact Loading Finality

Feature: Applies execution-finality enforcement to Model Weight / Checkpoint / Container Artifact Loading, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a model-artifact loading operation including at least one of loading model weights, loading a model checkpoint, loading an adapter, loading a LoRA module, loading a quantized model artifact, loading a containerized inference service, activating a model-serving image, loading tokenizer state, loading safety-policy state, or loading a model-configuration bundle.¶

The applicable Finality Sink may be a model-loader boundary, GPU memory admission gate, container runtime, modelserving runtime, accelerator memory controller, DPU-controlled storage gateway, or protected model-artifact admission component.¶

The Protected Enforcement Domain verifies a model-provenance condition, artifact-signature condition, license-scope condition, no-training condition, tenant-authority condition, model-version condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected model-artifact state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the model-artifact descriptor and applicable model-loading Finality Sink.¶

The model-loading Finality Sink is configured to prevent loading, decrypting, mapping, admitting, activating, or exposing the model artifact unless the scoped non-bearer capability is successfully verified.¶

6.18. Profile 48 — GPU Partition / MIG / Accelerator Isolation Finality

Feature: Applies execution-finality enforcement to GPU Partition / MIG / Accelerator Isolation, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents accelerator partition, tenant-isolation, memory-aperture, arithmetic-profile, or shared-silicon reconfiguration from becoming effective under stale or mismatched tenant, workload, quota, isolation, or residual-state conditions.¶

The Candidate Act includes an accelerator-partition operation including at least one of creating a GPU partition, deleting a GPU partition, resizing a GPU partition, assigning an accelerator partition to a tenant, reassigning a partition, changing a memory-isolation domain, changing a compute-scheduler domain, enabling shared accelerator execution, or merging partitioned accelerator resources.¶

The applicable Finality Sink may be a GPU partition controller, accelerator hypervisor, GPU hardware scheduler, memory-isolation controller, accelerator resource manager, virtual-GPU controller, or protected accelerator-partition gate.¶

The Protected Enforcement Domain verifies a tenant-authority condition, memory-isolation condition, compute-quota condition, workload-attestation condition, no-cross-tenant condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected accelerator-partition state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the accelerator-partition descriptor and applicable partition Finality Sink.¶

The partition Finality Sink is configured to prevent creating, deleting, resizing, assigning, reassigning, merging, or exposing an accelerator partition unless the scoped non-bearer capability is successfully verified.¶

6.19. Profile 49 — Tensor-Core Precision / Transformer-Engine Mode Finality

Feature: Applies execution-finality enforcement to Tensor-Core Precision / Transformer-Engine Mode, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents an accelerator from switching numeric, quantization, sparsity, or arithmetic execution modes under a model, tenant, safety, or stability context different from the one that was validated.¶

The Candidate Act includes an accelerator arithmeticmode operation including at least one of enabling a tensor-core mode, changing a oatingpoint precision mode, enabling a low-precision inference mode, changing transformer-engine scaling state, selecting an adaptive quantization mode, changing matrix-multiplication accumulation mode, activating sparsity mode, enabling speculative decoding arithmetic, or applying a model-specific numeric execution profile.¶

The applicable Finality Sink may be a tensorcore control register, accelerator arithmetic-mode controller, transformer-engine controller, GPU scheduler, model-execution runtime, or protected arithmetic-mode gate.¶

The Protected Enforcement Domain verifies a model-version condition, safetyenvelope condition, numerical-stability condition, output-class condition, tenant-policy condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected arithmeticmode state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the arithmetic-mode descriptor and applicable arithmetic Finality Sink.¶

The arithmetic Finality Sink is configured to prevent applying, enabling, switching, or committing the arithmetic-mode operation unless the scoped non-bearer capability is successfully verified.¶

6.20. Profile 50 — Agentic AI Workflow Orchestration Finality in an AI Factory

Feature: Applies execution-finality enforcement to Agentic AI Workflow Orchestration, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes an agentic AI workflow operation executed in a rack-scale or data-center-scale AI factory, including at least one of tool invocation, workflow-step delegation, retrieval invocation, code execution, database query, network call, robotcontrol command, payment instruction, enterprise action, or multi-agent task handoff generated, selected, scheduled, or materially in uenced by a model-serving runtime.¶

The applicable Finality Sink may be an agentic workflow controller, inference scheduler, tooldispatch gateway, model-serving runtime, DPU-controlled egress gateway, API gateway, or protected agentic-effectuation boundary.¶

The Protected Enforcement Domain verifies a tool-authority condition, user-authority condition, enterprise-policy condition, output-class condition, purpose-binding condition, delegation-chain condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected agentic-workflow state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the agentic-operation descriptor and applicable agentic Finality Sink.¶

The agentic Finality Sink is configured to prevent invoking, dispatching, transmitting, executing, delegating, or externally effecting the agentic workflow operation unless the scoped non-bearer capability is successfully verified.¶

6.21. Profile 51 — AI Factory Scheduler / Cluster-Orchestration Finality

Feature: Applies execution-finality enforcement to AI Factory Scheduler / Cluster-Orchestration, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents workload admission, placement, migration, priority, quota, capacity reservation, or scaling changes from taking effect under the wrong tenant, workload class, accelerator state, sovereign-placement rule, or resource budget.¶

The Candidate Act includes an AI-factory scheduling operation including at least one of accelerator-job admission, distributed-training job placement, inference-job placement, model-serving replica creation, batch-scheduler admission, priority change, quota override, capacity reservation, scale-up operation, scale-down operation, or cross-rack workload migration.¶

The applicable Finality Sink may be a cluster scheduler, workload orchestrator, accelerator admission controller, GPU scheduler, rack controller, DPUcontrolled admission gateway, or protected AI-factory orchestration boundary.¶

The Protected Enforcement Domain verifies a tenant-authority condition, quota condition, accelerator-attestation condition, workload-class condition, sovereign- placement condition, power-budget condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected scheduling state, protected quota state, protected placement state, or protected capacity-reservation state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the scheduling descriptor and applicable scheduler Finality Sink.¶

The scheduler Finality Sink is configured to prevent admitting, placing, migrating, prioritizing, reserving, scaling, or executing the workload unless the scoped non-bearer capability is successfully verified.¶

6.22. Profile 52 — AI Factory Storage / Checkpoint Replication Finality

Feature: Applies execution-finality enforcement to AI Factory Storage / Checkpoint Replication, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes an AI-factory storage operation including at least one of checkpoint write, checkpoint read, checkpoint replication, model-state snapshot, tensor-shard snapshot, training-data shard read, training-data shard write, vector-store update, embedding-store update, or storage-tier migration for AI workloads.¶

The applicable Finality Sink may be a DPU-controlled storage gateway, storage offload engine, parallel le-system gate, object-store gateway, checkpoint manager, vector-store controller, embedding-store controller, or protected AI-storage boundary.¶

The Protected Enforcement Domain verifies a data-class condition, tenant-isolation condition, no-training condition, retention condition, destination-vault condition, encryption-key-release condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected storage-access state, protected retention state, protected checkpoint state, or protected data-movement state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the storage-operation descriptor and applicable storage Finality Sink.¶

The storage Finality Sink is configured to prevent reading, writing, replicating, migrating, snapshotting, exposing, or restoring the AI workload data unless the scoped non-bearer capability is successfully verified.¶

6.23. Profile 53 — AI Factory Telemetry / Observability / Debug Egress Finality

Feature: Applies execution-finality enforcement to AI Factory Telemetry / Observability / Debug Egress, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes an AI-factory observability operation including at least one of telemetry export, debug-log export, trace export, performancecounter export, GPU-error-state export, model-serving trace export, scheduler-log export, memorydump export, packet-capture export, or infrastructure-health data exposure.¶

The applicable Finality Sink may be a telemetry collector, observability agent, DPU telemetry engine, BMC telemetry gate, GPU debug interface, performance-counter gateway, logexport gateway, or protected observability boundary.¶

The Protected Enforcement Domain verifies a data-minimization condition, tenant-isolation condition, debugauthority condition, lawful-export condition, destination condition, redaction condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected telemetry-export state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the observability descriptor and applicable telemetry Finality Sink.¶

The telemetry Finality Sink is configured to prevent exporting, dumping, tracing, logging, exposing, or transmitting the telemetry, debug, trace, memory, packet, or performance-counter data unless the scoped non-bearer capability is successfully verified.¶

6.24. Profile 54 — Firmware / Driver / Microcode Update Finality for AI Factory Components

Feature: Applies execution-finality enforcement to Firmware / Driver / Microcode Update, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents privileged firmware, microcode, driver, or programmable-logic state from being installed or activated solely because update transport or signature checks succeeded, without act-specific authorization for the exact target, version, rollback state, scope, and activation boundary.¶

The Candidate Act includes a firmware, driver, or microcode update operation including at least one of updating GPU firmware, DPU firmware, SmartNIC firmware, switch firmware, accelerator microcode, baseboard-management-controller firmware, GPU driver state, interconnect-switch firmware, security-policy firmware, or acceleratorruntime privileged component.¶

The applicable Finality Sink may be a firmwareupdate controller, secure boot chain, hardware root of trust, GPU management controller, DPU management controller, switch management controller, BMC, or protected firmwareeffectuation gate.¶

The Protected Enforcement Domain verifies a firmwaresignature condition, update-authority condition, rollback-prevention condition, componentidentity condition, maintenance-window condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected firmware-version state, protected rollback state, or protected component-admission state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the firmware-update descriptor and applicable firmware Finality Sink.¶

The firmware Finality Sink is configured to prevent installing, activating, rolling back, replacing, booting, or applying the firmware, driver, microcode, or privileged runtime component unless the scoped non-bearer capability is successfully verified.¶

6.25. Profile 55 — Rack Power, Thermal, and Liquid-Cooling Actuation Finality

Feature: Applies execution-finality enforcement to Rack Power, Thermal, and Liquid-Cooling Actuation, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents AI-factory power, thermal, cooling, boost, or ramp-rate changes from being actuated when electrical, thermal, grid, emergency, maintenance, or workload-priority conditions do not authorize the exact physical transition.¶

The Candidate Act includes an AI-factory physical-infrastructure actuation operation including at least one of rack power-state transition, GPU power-limit change, accelerator boost-state change, liquid-cooling flow adjustment, coolant-valve actuation, pump-speed change, fan-speed change, thermal-throttle override, power-shelf activation, or workload-power ramp operation.¶

The applicable Finality Sink may be a rack power controller, PDU, cooling controller, liquid-cooling manifold controller, pump controller, fan controller, BMC, GPU power controller, or protected physical-infrastructure actuation gate.¶

The Protected Enforcement Domain verifies a thermalsafety condition, electrical-capacity condition, grid-stability condition, workload-priority condition, emergency-safety condition, maintenance-authority condition, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected power, thermal, cooling, or ramp-rate state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the physical infrastructure descriptor and applicable physical-infrastructure Finality Sink.¶

The physical-infrastructure Finality Sink is configured to prevent energizing, throttling, boosting, cooling, ramping, overriding, actuating, or changing the physical infrastructure state unless the scoped non-bearer capability is successfully verified.¶

6.26. Profile 56 — Digital-Twin / Simulation-to-Actuation Finality for AI Factories

Feature: Applies execution-finality enforcement to Digital-Twin / Simulation-to-Actuation, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents a simulation, digital twin, or optimization result from directly becoming an infrastructure or physical actuation without independent authorization of the exact resulting operation and current safety state.¶

The Candidate Act includes a simulation-derived, digital-twin-derived, or AI-optimization-derived infrastructure operation including at least one of rackplacement optimization, network-topology reconfiguration, workload-relocation recommendation, cooling-policy update, power-policy update, accelerator-utilization optimization, maintenance-action scheduling, supply-chain simulation output, robotics-control instruction, or factory-floor actuation instruction generated or materially in uenced by a digital twin, simulator, or AI optimization model.¶

The applicable Finality Sink may be an infrastructure orchestrator, network-fabric controller, cooling controller, power controller, robotics controller, maintenance scheduler, rack controller, or protected simulation-to-actuation boundary.¶

The Protected Enforcement Domain verifies a simulator-provenance condition, model-version condition, safety-envelope condition, infrastructure-authority condition, human-approval condition where required, revocation condition, policy-epoch condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected simulation-actuation state, commits protected validation evidence, and releases a scoped non-bearer capability bound to the simulation-derived operation descriptor and applicable simulation Finality Sink.¶

The simulation Finality Sink is configured to prevent applying, scheduling, transmitting, actuating, or externally effecting the simulationderived operation unless the scoped non-bearer capability is successfully verified.¶

6.27. Profile 59 — Hardware Sparse-Expert Dispatch Register Finality

Feature: Applies execution-finality enforcement to Hardware Sparse-Expert Dispatch Register, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a sparse expert-dispatch operation including at least one of writing an expert-activation register, writing a sparse routing-table entry, selecting an expert shard, dispatching tokens to an expert, assigning an expert-capacity bucket, updating an expert-load-balancing state, routing a model-parallel stage to an expert device, activating a jurisdiction-specific expert, merging expert outputs, or changing a sparse expert-placement map.¶

The applicable Finality Sink may be an expertactivation register, sparse-routing table, expert-router controller, MoE dispatch controller, expertcapacity arbiter, inference scheduler, accelerator admission controller, GPU fabric switch, or protected expert-dispatch hardware gate.¶

The Protected Enforcement Domain verifies an expert-authority condition, expert-jurisdiction condition, model-tenant condition, input-data-class condition, output-class condition, expert-capacity condition, routing-policy condition, extraction-budget condition, policy-epoch condition, revocation condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected expert-routing state, protected expert-capacity state, protected tenant-routing state, protected expert-jurisdiction state, protected token-dispatch state, or protected extractionbudget state, commits protected validation evidence before or atomically with capability release, and releases a scoped non-bearer capability bound to the sparse expert-dispatch descriptor and applicable expertdispatch Finality Sink.¶

The expert-dispatch Finality Sink is configured to prevent writing the expert-activation register, updating the sparse routing table, dispatching a token to an expert, activating the expert, allocating expert capacity, or merging expert output unless the scoped non-bearer capability is successfully verified before the sparse expert-dispatch operation becomes compute-effective.¶

6.28. Profile 60 — KV-Cache Physical Page-Lease and Context-Reuse Finality

Feature: Applies execution-finality enforcement to KV-Cache Physical Page-Lease and Context-Reuse, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a context-memory operation including at least one of allocating a physical KV-cache page, leasing a KV-cache page to an inference session, extending a KV-cache retention interval, transferring a KV-cache page between accelerators, pinning a KV-cache page in accelerator memory, evicting a KV-cache page, retrieving a long-context state, reconstructing a session memory, injecting retrieval memory into an inference context, reusing a user-context object, recalling agentic memory, or transferring an agentic memory object to a model-serving runtime.¶

The applicable Finality Sink may be a KV-cache page controller, accelerator memory controller, context-memory store, inference scheduler, model-serving runtime, DPU-controlled storage gateway, vector-store controller, retrieval-memory controller, memory-lease controller, or protected context-memory boundary.¶

The Protected Enforcement Domain verifies a user-session authority condition, context-retention condition, no-training condition, privacy-budget condition, memory-reuse condition, retrieval-scope condition, tenant-isolation condition, purpose-binding condition, policy-epoch condition, revocation condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected cache-lease state, protected retention state, protected privacy-budget state, protected retrieval-scope state, protected user-context state, protected memory-reuse state, or protected KV-page state, commits protected validation evidence before or atomically with capability release, and releases a scoped non-bearer capability bound to the context-memory descriptor and applicable context-memory Finality Sink.¶

The context-memory Finality Sink is configured to prevent allocating, leasing, retaining, retrieving, migrating, reconstructing, injecting, exposing, or reusing the KV-cache page, long-context state, retrieval memory, user-context object, or agentic memory object unless the scoped non-bearer capability is successfully verified.¶

6.29. Profile 61 — Accelerator-Fabric Virtual-Lane, Credit, and East-West Tensor-Flow Finality

Feature: Applies execution-finality enforcement to Accelerator-Fabric Virtual-Lane, Credit, and East-West Tensor-Flow, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes an accelerator-fabric flow-control operation including at least one of assigning a tensor transfer to a virtual lane, allocating fabric credits, updating a congestion-control state, admitting a it, packet, cell, tensor fragment, activation fragment, gradient fragment, logits fragment, or KV-cache fragment into a high-bandwidth accelerator fabric, selecting a fabric route, modifying an adaptive-routing entry, changing a quality-of-service class for accelerator east-west traffic, or admitting a scale-out tensor flow.¶

The applicable Finality Sink may be an accelerator-fabric switch, GPU interconnect switch, virtual-lane arbiter, credit manager, congestion-control engine, packet scheduler, tensor- flow router, adaptive-routing controller, or protected fabric-admission gate.¶

The Protected Enforcement Domain verifies a tenant-isolation condition, tensor-class condition, destination-accelerator condition, memory-vault condition, lawful-routing condition, extraction-budget condition, congestion-safety condition, traffic-class condition, policy-epoch condition, revocation condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected fabric-credit state, protected virtuallane state, protected routing state, protected extraction-budget state, protected traffic-class state, or protected congestion-control state, commits protected validation evidence before or atomically with capability release, and releases a scoped non-bearer capability bound to the accelerator-fabric flow descriptor and applicable accelerator-fabric Finality Sink.¶

The accelerator-fabric Finality Sink is configured to prevent admitting, routing, scheduling, crediting, prioritizing, steering, or forwarding the accelerator-fabric flow unless the scoped non-bearer capability is successfully verified at or before the fabric- flow effectuation boundary.¶

6.30. Profile 62 — DPU Attestation-Key-RDMA Atomic Finality Chain

Feature: Applies execution-finality enforcement to DPU Attestation-Key-RDMA Atomic, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a chained infrastructure- offload operation requiring at least two of remote attestation acceptance, cryptographic key release, queue-pair activation, RDMA memory-key creation, DMA-window opening, secure-channel creation, GPU-direct memory exposure, or remote accelerator memory admission.¶

The applicable Finality Sink may be a Data Processing Unit, SmartNIC, SuperNIC, network interface controller, RDMA engine, memory-key controller, queue-pair controller, GPU-direct transfer gate, storage offload engine, security offload engine, or protected infrastructureoffload controller.¶

The Protected Enforcement Domain verifies an endpoint-attestation condition, memory-region authority condition, key-release condition, workload-integrity condition, tenant-isolation condition, transfer-direction condition, data-class condition, nonce condition, policy-epoch condition, revocation condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected attestation state, protected key-release state, protected memory-key state, protected queue-pair state, protected DMA-transfer state, protected endpoint state, or protected securechannel state, commits protected validation evidence before or atomically with capability release, and releases a scoped non-bearer capability bound to a single chained infrastructure descriptor and applicable infrastructure Finality Sink.¶

The infrastructure Finality Sink is configured to prevent accepting the attestation, releasing the key, creating or reusing the memory key, activating the queue pair, opening the DMA window, exposing GPU-direct memory, or admitting the RDMA or DMA operation unless the scoped non-bearer capability is successfully verified against the same chained infrastructure descriptor.¶

6.31. Profile 94 — In-Network Compute Extraction-Budget and Hardware-Egress Finality

Feature: Applies execution-finality enforcement to In-Network Compute Extraction-Budget and Hardware-Egress, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes an in-network compute output operation generated by at least one of a programmable data-plane node, edge-compute function, DPU, SmartNIC, programmable switch, network processor, user-plane function, accelerator-adjacent compute function, or telemetry-processing function.¶

The Candidate Act includes at least one of an inference result, packet classification result, telemetry aggregation, routing decision, programmable-table update, model-update contribution, federated-learning contribution, compressed representation, transformed payload, cache update, security classification, or digital twin update.¶

The Protected Enforcement Domain maintains a protected extraction-budget state or compute-domain budget state corresponding to at least one of tenant, model, memory enclave, data class, telemetry class, packet class, destination, downstream consumer, privacy budget, routing-alteration budget, programmable-table-update budget, or hardware-transfer-size budget.¶

The Protected Enforcement Domain updates or consumes the protected budget state, commits protected validation evidence, and releases a scoped non-bearer compute-release capability only when the Candidate Act remains within the permitted compute-domain budget.¶

The applicable Finality Sink is configured to prevent forwarding, exporting, caching, storing, committing, transmitting, admitting, routing, exposing, or hardware-egressing the compute output through DMA, RDMA, hardware queue, programmable table, network interface, peer-to-peer path, or alternate egress path unless the scoped non-bearer compute-release capability is verified.¶

6.32. Profile 102 — Federated-Learning Memory-Export Latch and Contribution-Budget Finality

Feature: Applies execution-finality enforcement to Federated-Learning Memory-Export Latch and Contribution-Budget, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a federated-learning contribution operation including at least one of a gradient update, model-weight delta, embedding-space egress, local model-update contribution, federated-averaging contribution, compressed update, secure-aggregation contribution, feature-space update, representation update, or modelimprovement signal generated by a user equipment, edge node, radio node, accelerator, DPU, SmartNIC, programmable data-plane node, or in-network compute component.¶

The applicable Finality Sink may be a DPU-controlled hardware memory-export gate, SmartNIC egress latch, accelerator-egress latch, RDMA memory-key boundary, DMA export gate, programmable network-interface boundary, secure-aggregation ingress boundary, modelupdate aggregation boundary, or protected federated-learning egress component.¶

The Protected Enforcement Domain verifies a federated-contribution budget condition, per-tenant privacy-budget condition, per-node privacy-budget condition, gradient-leakage condition, data-class condition, model-owner condition, tenant-isolation condition, no-secondary-use condition, no-training-beyond-scope condition, aggregation-round condition, policy-epoch condition, revocation condition, nonce condition, or descriptor-binding condition.¶

The Protected Enforcement Domain updates or consumes protected federated contribution state, extraction-budget state, privacy-budget state, aggregation-round state, memory-export state, nonce state, policy-epoch state, revocation state, or validation-evidence commitment state, commits protected validation evidence before or atomically with capability release, and releases a scoped non-bearer federated-learning egress capability bound to the federated-learning contribution descriptor and applicable Finality Sink.¶

The applicable Finality Sink is configured to prevent exporting, transmitting, aggregating, admitting, forwarding, committing, or hardware-egressing the federated-learning contribution unless the scoped non-bearer federated-learning egress capability is successfully verified before the memory-export or aggregation boundary is unlocked.¶

6.33. Profile 111 — Accelerator-Egress Neural Waveform and AI-Generated Radio Tensor Finality

Feature: Applies execution-finality enforcement to Accelerator-Egress Neural Waveform and AI-Generated Radio Tensor, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents an AI-generated baseband, beamforming, precoding, IQ, waveform, or RF-control tensor from becoming baseband- or radiative-effective unless the exact generated object remains bound to current spectrum, power, beam, exposure, policy, and sink authority.¶

The Candidate Act includes egress of a neural-networkgenerated waveform, neural radio tensor, AI-generated baseband tensor, learned precoder tensor, learned beamforming tensor, semantic-physical-layer tensor, neural modulation tensor, model-generated IQ stream, accelerator-generated RF control tensor, or tensor-output stream from an artificial intelligence accelerator to a physical-layer radio boundary.¶

The applicable Finality Sink may be an accelerator-egress controller, tensor-egress gate, GPU-tobaseband transfer gate, DPU-controlled radio egress gate, SmartNIC-controlled radio egress gate, baseband-to-RF boundary, DAC boundary, radio-unit ingestion boundary, beamforming controller, RF front end enable boundary, antenna-excitation boundary, or protected neural-waveform effectuation boundary.¶

The protected enforcement domain is configured to perform NeuralWaveform Correlation Veri cation determining whether the neural-network-generated waveform or tensoroutput stream cryptographically matches a validated emission-authority object, radioaction descriptor, spectrum-use condition, beam-scope condition, power-scope condition, exposure-budget condition, waveform-family condition, policy-epoch condition, revocation condition, nonce condition, andfinality-receipt digest.¶

The Protected Enforcement Domain updates or consumes protected neural-waveform state, tensor-egress state, radio-emission state, exposure-budget state, nonce state, policy-epoch state, revocation state, and validation-evidence commitment state before releasing a scoped non-bearer neuralwaveform capability.¶

The applicable Finality Sink is configured to prevent exporting, transferring, ingesting, converting, beamforming, modulating, transmitting, or applying the neural-network-generated waveform or tensor-output stream to a physical-layer radio boundary unless the scoped non-bearer neural-waveform capability is successfully verified before the Candidate Act becomes radiative-effective, baseband-effective, or RF-chain-effective.¶

6.34. Profile 114 — Silicon Photonics and Optical-Compute Interconnect Finality

Feature: Applies execution-finality enforcement to Silicon Photonics and Optical-Compute Interconnect, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents data, tensor state, optical carriers, wavelength routes, modulator states, or package-to-fiber paths from becoming photonically effective outside the authorized source, destination, lane, wavelength, power, data class, tenant, or freshness scope.¶

The Candidate Act includes a chip-to-chip, board-to-board, module-to-module, rack-to-rack, accelerator-to-accelerator, memory-fabric, data-center fabric, or optical-compute data-movement operation transmitted, converted, modulated, admitted, or received through a silicon-photonics interconnect, optical-compute fabric, co-packaged optics interface, photonic integrated circuit, optical serializer-deserializer path, wavelength-multiplexed channel, or equivalent optical interconnect.¶

The Candidate Act is represented by an optical-transfer descriptor binding at least one of a source device, destination device, optical channel, wavelength, lane identifier, modulation class, data class, tensor class, memory range, transfer digest, route segment, optical interface identifier, electrical-to-optical conversion boundary, optical-to-electrical conversion boundary, policy epoch, revocation epoch, nonce, and Finality Sink identifier.¶

The applicable Finality Sink may be a photonic integrated circuit admission gate, electrical-to-optical conversion boundary, optical-to-electrical conversion boundary, optical modulator controller, laser-enablement controller, tunable micro-ring resonator controller, Mach-Zehnder interferometer bias controller, wavelength-selection controller, optical receiver admission boundary, co-packaged optics interface controller, or optical-fabric egress boundary.¶

The applicable Finality Sink is configured to prevent applying, enabling, accepting, or maintaining a bias voltage, laser-enablement signal, optical-carrier enablement state, wavelength selection state, optical-modulation state, resonator-tuning state, optical-receiver admission state, or electrical-to-optical or optical-to-electrical conversion state required to make the Candidate Act photonically effective unless the scoped non-bearer capability is successfully verified against the optical-transfer descriptor, source-destination binding, channel or wavelength scope, policy epoch, revocation epoch, freshness value, protected-state condition, and validation-evidence condition before the data-movement operation becomes photonically effective, fabric-effective, memory-effective, or network-effective.¶

6.35. Profile 130 — Local Artificial-Intelligence Model-Adapter Fusion Finality

Feature: Applies execution-finality enforcement to Local Artificial-Intelligence Model-Adapter Fusion, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes on-device merging, fusion, loading, runtime ingestion, graph compilation, activation, or execution of a model adapter, model-weight patch, personalized fine-tuning layer, Low-Rank Adaptation module, quantized adapter, prompt tuning module, safety-head modification, embedding patch, or other model-modification artifact into a base artificial-intelligence model.¶

The Candidate Act is represented by a model-adapter fusion descriptor binding at least one of a base model identifier, base model digest, adapter identifier, adapter digest, adapter provenance reference, adapter source authority, adapter safety-evaluation reference, structural compatibility result, parameter-shape compatibility result, post-fusion model digest, pre-fusion ALF, post-fusion ALF, execution graph digest, target accelerator identifier, target model-loader boundary, policy epoch, revocation epoch, freshness value, nonce, and Finality Sink identifier.¶

The applicable Finality Sink may be a neural-processing-unit graph compiler, accelerator memory-fusion boundary, model-weight loader gate, adapteringestion controller, model-runtime graph admission boundary, local inference-engine admission gate, secure model-cache loader, or protected accelerator execution boundary.¶

The applicable Finality Sink is configured to prevent fusing the adapter matrix, loading the model-weight patch, admitting the personalized fine-tuning layer, compiling the modified model graph, or executing the patched model unless the scoped non-bearer capability is successfully verified against the model-adapter fusion descriptor, adapter provenance condition, safety-alignment integrity condition, structural compatibility condition, approved ALF-transition condition, protected-state condition, policy epoch, revocation epoch, freshness value, and validation-evidence condition; thereby technically preventing an unauthorized, unsafe, provenance-defective, structurally incompatible, or policy-mismatched model adapter from becoming model-effective, accelerator-effective, inference-effective, or output-effective on the local device.¶

6.36. Profile 136 — Hidden Chain-of-Thought (CoT) and Dynamic Test-Time Compute Finality

Feature: Applies execution-finality enforcement to Hidden Chain-of-Thought (CoT) and Dynamic Test-Time Compute, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents privileged diagnostic and test interfaces from becoming an alternate path for unauthorized register access, protected-memory reads, tensor extraction, trace disclosure, or state mutation.¶

The Candidate Act includes a transition from an internal reasoning phase to an external emission or tool-dispatch phase following generation of a hidden reasoning trace, chain-of-thought token sequence, latent reasoning vector, or extended test-time compute sequence.¶

The Candidate Act is represented by a test-time-compute finality descriptor binding at least one of a dynamic reasoning-effort level, hidden-token budget, reasoning-phase telemetry digest, internal verification-loop result, safety-head evaluation score, permitted external tool scope, temporal latency bound, policy epoch, revocation epoch, freshness value, nonce, and Finality Sink identifier.¶

The applicable Finality Sink may be a token-emission boundary, tool- dispatch gateway, reasoning-to-action transition gate, accelerator egress controller, or protected inference-serving boundary.¶

The applicable Finality Sink is configured to prevent dispatching an external API call, executing an external tool, or emitting a user-facing response unless the scoped non-bearer capability is successfully verified against the test-time-compute finality descriptor; thereby providing the technical effect of structurally decoupling internal latent computation from external API actuation, and overcoming the technical de ciency of output-layer content filters by rendering the token-emission boundary physically or cryptographically impassable to prompt-injected or hallucinated hidden-token sequences that exceed the authorized safety envelope or extraction budget.¶

6.37. Profile 145 — Multi-Tenant LLM-Weight Quantization Layer and Profile-Switching Finality

Feature: Applies execution-finality enforcement to Multi-Tenant LLM-Weight Quantization Layer and Profile-Switching, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes real-time model-weight switching, dynamic low-precision quantization profile application, multi-tenant weight layer loading, or arithmetic-mode register manipulation within a shared accelerator memory array.¶

The Candidate Act is represented by an arithmetic profile descriptor binding at least a tenant isolation credential, target weight mask digest, quantization scaling matrix parameter, memory protection boundary, policy epoch, revocation epoch, freshness value, nonce, and Finality Sink identifier.¶

The applicable Finality Sink may be a silicon-level memory controller, hardware memory protection unit, tensor-core control register, accelerator arithmetic-mode controller, or high-bandwidth-memory partition gate.¶

The applicable Finality Sink is configured to prevent executing arithmetic operations or exposing the loaded weight structure to the active compute fabric unless the scoped non-bearer capability is successfully verified against the descriptor and tenant isolation credential; thereby structurally overcoming the technical de ciency of shared-silicon side-channel leakage by freezing the computational state of the accelerator prior to a multi-tenant quantization or weight-mask switch, preventing execution from resuming until the hardware memory controller veri es the new isolation boundaries are cryptographically bound to the authorized tenant.¶

6.38. Profile 146 — Direct Memory-Semantic Transaction Finality

Feature: Applies execution-finality enforcement to Direct Memory-Semantic Transaction, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

This profile inherits the common requirement that the operation remain non-effective until the applicable descriptor bindings, authority predicates, protected state, Protected Validation Evidence, scoped non-bearer capability, freshness conditions, and Finality Sink verification have been established for the exact pending act.¶

The Candidate Act includes a direct memory-semantic operation traversing a cache-coherent interconnect, memory fabric, accelerator fabric, or host-device fabric, the direct memory-semantic operation including at least one of: a memory read, a memory write, a cache-line writeback, a cache-line invalidation, a cache snoop, a coherency-directory update, an ownership transfer, an atomic memory operation, a Direct Memory Access (DMA) write, a Remote Direct Memory Access (RDMA) operation, a peer-to-peer accelerator transfer, a page migration, a memory-pool access, a host-memory overwrite, a hypervisor-memory mutation, a shared-memory update, a tensor write, an embedding export, a Key-Value (KV) cache export, an activation transfer, or a model-state transfer.¶

6.39. Profile 148 — Native Silicon Memory-Fabric Transaction Admission Finality

Feature: Applies execution-finality enforcement to Native Silicon Memory-Fabric Transaction Admission, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The execution-finality boundary is a physical or logical memory-fabric transaction boundary, and the applicable Finality Sink is a hardware-enforced transaction admission point implemented natively within silicon or semiconductor intellectual property (IP), the applicable Finality Sink including at least one of: a memory controller, a Compute Express Link (CXL) host bridge, a CXL device controller, a coherence home agent, a coherency-directory controller, a cache-coherency engine, a transaction-layer parser, a flit parser, an Input/Output Memory Management Unit (IOMMU), a System Memory Management Unit (SMMU), a DMA remapping engine, an accelerator-egress controller, a Data Processing Unit (DPU), a SmartNIC, a secure memory gateway, a fabric switch, a secure chiplet interface, or a protected memory admission controller.¶

6.40. Profile 149 — Hardware Burn-In and Replay-Resistant Capability Consumption Finality

Feature: Applies execution-finality enforcement to Hardware Burn-In and Replay-Resistant Capability Consumption, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The machine-enforced capability- validity check performed at the applicable Finality Sink comprises an ephemeral hardware burn-in or replay-resistant capability consumption.¶

Successfully verifying and consuming the scoped non-bearer capability triggers an atomic, irreversible, or rollback-resistant mutation of a hardware, firmware, or cryptographic state local to the applicable Finality Sink; the mutation including at least one of: advancing a monotonic counter, toggling a one-time hardware latch, updating a protected replay table, rotating a sink-local key, advancing a cryptographic key ratchet, updating a Physical Unclonable Function (PUF) challenge state, advancing a hardware epoch register, invalidating a capability digest within secure memory, updating a protected nonce register, or zeroizing a capability-derived verification secret, thereby strictly preventing replay of the consumed scoped non-bearer capability.¶

6.41. Profile 150 — In-Band Hardware Transaction Capability-Binding Finality

Feature: Applies execution-finality enforcement to In-Band Hardware Transaction Capability-Binding, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The scoped non-bearer capability is transported in-band with a hardware memory transaction and is verifiable by the applicable Finality Sink using a transport association including at least one of: a transaction tag, a Process Address Space ID (PASID), a Virtual Machine ID (VMID), a hardware queue identifier, a memory-window identifier, a fabric- flow identifier, a protected sideband channel, a secure transaction metadata field, a capability pointer, or a sink-local lookup reference.¶

6.42. Profile 153 — Cache-Coherence Visibility Transition Finality

Feature: Applies execution-finality enforcement to Cache-Coherence Visibility Transition, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes a memorysemantic operation attempting to mutate a cache-coherency state within a multi-processor or accelerator-to-accelerator coherent domain.¶

The applicable Finality Sink modifies a hardware cache-coherence protocol—including but not limited to MESI, MOESI, or a directory-based coherence protocol—such that a cache line or memory page associated with the Candidate Act is cryptographically pinned in an exclusive, invalid, or non-globally-observable state.¶

The applicable Finality Sink prohibits the hardware cache-coherence protocol from broadcasting a snoop response, updating a coherence directory, or transitioning the cache line or memory page to a shared or modified state observable by a downstream execution unit until the scoped non-bearer capability is successfully verified and consumed at the hardware transaction layer.¶

6.43. Profile 169 — Space-Grade Chiplet Health-Attestation Finality

Feature: Applies execution-finality enforcement to Space-Grade Chiplet Health-Attestation, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents a chiplet, die-to-die transfer, sideband command, package role, or package-level resource transition from becoming effective when component identity, health, scope, firmware state, destination, or freshness does not match the authorized act.¶

The Candidate Act includes admitting, using, re-enabling, routing through, assigning workload to, accepting output from, coupling to, or externally effectuating a space-grade chiplet, processor chiplet, memory chiplet, photonic chiplet, RF chiplet, accelerator chiplet, security chiplet, sensor-interface chiplet, or package-integrated compute component based at least in part on a chiplet health attestation.¶

The machine-verifiable act descriptor binds a chiplet identifier, package identifier, satellite identifier, chiplet class, hardware revision, radiation exposure state, health-attestation digest, fault-history commitment, current operating state, workload or data class, destination or dependent subsystem, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include chiplet identity validity, attestation validity, health-state authority, radiation-exposure authority, workload-admission authority, quarantine-state validation where applicable, replacement or counterfeit-state validation where applicable, protected chiplet-state update validation, policy-epoch validity, revocation-state validity, and freshness validation.¶

The applicable Finality Sink may be a chiplet admission gate, package interconnect gate, die-to-die link gate, workload scheduler gate, memory aperture gate, optical or RF interface gate, accelerator admission gate, output-release gate, or subsystem dependency gate configured to deny use of the chiplet unless the scoped non-bearer capability is validly bound to the chiplet health attestation, descriptor, protected validation evidence, protected-state representation, permitted workload scope, and Finality Sink.¶

6.44. Profile 170 — Die-to-Die Tensor-Transfer Finality

Feature: Applies execution-finality enforcement to Die-to-Die Tensor-Transfer, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes transfer, routing, serialization, deserialization, admission, replication, streaming, mirroring, or delivery of tensor data, activation data, embedding data, hidden-state data, key-value cache data, gradient data, model-state data, checkpoint data, optimizer-state data, inference-output data, or intermediate AI computation state across a die-to-die interconnect, chiplet package interconnect, Universal Chiplet Interconnect Express link, interposer link, package substrate link, bridge die, retimer, or equivalent chiplet-to-chiplet interface.¶

The machine-verifiable act descriptor binds a source die identifier, destination die identifier, source chiplet identifier, destination chiplet identifier, package identifier, interconnect identifier, tensor or model-state class, workload identifier, model identifier, tenant or mission scope where applicable, memory aperture, transfer direction, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include source-die authority, destination-die authority, die-to-die link authority, tensor-class authority, memory-aperture authority, workload or tenant authority, no-cross-partition restriction validation where applicable, protected-state update validation, policy-epoch validity, revocation-state validity, and replay-prevention validation.¶

The applicable Finality Sink may be a die-to-die transmit gate, die-to-die receive gate, UCIe link controller, chiplet serializer, chiplet deserializer, bridge-die routing gate, memory aperture gate, tensor-buffer admission gate, destination accelerator input gate, or package interconnect controller configured to refuse the transfer unless the scoped non-bearer capability is validly bound to the dieto-die tensor-transfer Candidate Act, descriptor, committed protected validation evidence, protected-state representation, freshness constraint, permitted transfer scope, and applicable Finality Sink.¶

6.45. Profile 171 — Third-Party Chiplet Admission and Identity Finality

Feature: Applies execution-finality enforcement to Third-Party Chiplet Admission and Identity, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes admission, initialization, activation, enumeration, authentication, routing admission, workload assignment, memory aperture grant, package-level participation, or acceptance of output from a third-party chiplet, vendor-supplied chiplet, partner chiplet, replacement chiplet, accelerator chiplet, memory chiplet, photonic chiplet, RF chiplet, security chiplet, or sensor-interface chiplet within a multi-chiplet package.¶

The machine-verifiable act descriptor binds a package identifier, chiplet identifier, chiplet vendor or provenance commitment, chiplet class, hardware revision, attestation digest, security capability commitment, permitted interface scope, permitted memory or tensor scope, dependency receipt set, policy epoch, freshness constraint, executionnality boundary, and applicable Finality Sink.¶

The authority predicates include chiplet identity validity, chiplet attestation validity, provenance authority, packageadmission authority, interface-scope authority, workload-admission authority, memory-aperture authority, counterfeit or replacement-state validation where applicable, revocation-state validation, and protected package-state update validation.¶

The applicable Finality Sink may be a package admission gate, chiplet enumeration gate, interconnect link- training gate, die-to-die receive gate, memory aperture gate, workload scheduler gate, accelerator admission gate, security-controller gate, or output-acceptance gate configured to fail closed unless the scoped non-bearer capability is valid for the admitted chiplet, its permitted package role, and the applicable Finality Sink.¶

6.46. Profile 172 — UCIe Sideband Command Finality

Feature: Applies execution-finality enforcement to UCIe Sideband Command, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes transmission, acceptance, routing, execution, privilege elevation, configuration, reset, debug activation, powerstate mutation, link-state mutation, memory-window mutation, error-handling mutation, management command execution, or control-plane command execution over a sideband, management, control, mailbox, interrupt, reset, power-management, debug, or service channel associated with a Universal Chiplet Interconnect Express interface, die-to-die interconnect, chiplet package interconnect, or package-management fabric.¶

The machine-verifiable act descriptor binds a source chiplet identifier, destination chiplet identifier, package identifier, sideband channel identifier, command digest, command arguments digest, affected resource commitment, privilege class, command scope, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include sideband command authority, source identity validity, destination identity validity, privilege-scope validation, affected-resource authority, command freshness validation, no-debug-bypass validation where applicable, no-memory-aperture-expansion validation where applicable, protected-state update validation, policy-epoch validity, and revocation-state validity.¶

The applicable Finality Sink may be a sideband command gate, mailbox gate, interrupt controller, reset controller, power-management controller, debug-command gate, link-state controller, memory-window controller, or destination chiplet management interface configured to refuse execution of the sideband command unless the scoped non-bearer capability is validly bound to the command, descriptor, protected validation evidence, protected-state representation, permitted command scope, and sink.¶

6.47. Profile 173 — Chiplet Memory-Aperture and DMA Finality

Feature: Applies execution-finality enforcement to Chiplet Memory-Aperture and DMA, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes creating, enlarging, shrinking, remapping, sharing, revoking, migrating, exposing, or using a memory aperture, direct-memory-access path, page-table entry, IOMMU mapping, chiplet memory window, tensor-buffer window, accelerator scratchpad window, HBM region, SRAM region, cache region, or package-level shared-memory region accessible by one or more chiplets.¶

The machine-verifiable act descriptor binds a source chiplet identifier, destination chiplet identifier, memory-region commitment, aperture identifier, DMA engine identifier, page-table or IOMMU commitment, permitted access mode, data class, workload or tenant scope where applicable, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include memory-aperture authority, DMA-path authority, source-chiplet authority, destination-chiplet authority, access-mode authority, tensor or data-class authority, no-cross-tenant or no-cross-domain restriction validation where applicable, protected memory-state update validation, revocation-state validation, freshness validation, and replay-prevention validation.¶

The applicable Finality Sink may be a memory aperture gate, DMA mapping gate, IOMMU gate, page-table commit gate, HBM controller, SRAM controller, cache admission gate, tensor-buffer gate, chiplet memory-window controller, or package memory-fabric interface configured to deny memory or DMA effectuation unless the scoped non-bearer capability is valid for the specific memory aperture, access mode, data class, and sink.¶

6.48. Profile 174 — Debug, JTAG, Test, and Probe-Port Finality

Feature: Applies execution-finality enforcement to Debug, JTAG, Test, and Probe-Port, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes enabling, using, routing, exposing, reading, writing, scanning, probing, tracing, dumping, or externally effectuating a debug port, JTAG port, scan chain, boundary-scan interface, test access port, probe port, manufacturing interface, engineering port, diagnostic interface, trace interface, logicanalyzer interface, package test interface, chiplet test path, or equivalent privileged hardwareaccess interface associated with a chiplet package or die-to-die system.¶

The machine-verifiable act descriptor binds a package identifier, target chiplet identifier, debug or test interface identifier, command or access digest, affected register commitment, affected memory commitment, affected tensor or model-state commitment where applicable, access mode, diagnostic purpose, time window, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include debug-authority validity, testauthority validity, target-chiplet identity validity, access-scope validation, no-key-export validation, no-protected-memory-dump validation, no-tensor-dump validation where applicable, timewindow validation, protected debug-state update validation, revocation-state validation, and policy-epoch validity.¶

The applicable Finality Sink may be a JTAG enable gate, scan-chain gate, debug-port gate, test-access-port gate, probe-port gate, register-read gate, registerwrite gate, trace-output gate, package diagnostic controller, or protected memory-read gate configured to refuse privileged access unless the scoped non-bearer capability is validly bound to the debug or test Candidate Act, descriptor, protected validation evidence, protected-state representation, permitted diagnostic scope, freshness constraint, and sink.¶

6.49. Profile 175 — Package-Level Firmware and Microcode Finality

Feature: Applies execution-finality enforcement to Package-Level Firmware and Microcode, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes loading, activating, selecting, committing, rolling back, restoring, hot-patching, or otherwise applying package-level firmware, chiplet firmware, interconnect firmware, bridge-die firmware, retimer firmware, package-management firmware, power-management firmware, securitycontroller firmware, memory-controller firmware, accelerator microcode, interface microcode, linktraining microcode, or package-controller microcode within a multi-chiplet package.¶

The machine-verifiable act descriptor binds a package identifier, target chiplet or controller identifier, firmware or microcode package digest, active version, candidate version, affected function scope, dependency receipt set, hardware revision, update authority object, anti-rollback state, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include firmware integrity validation, updateauthority validation, target-component identity validation, hardware-revision compatibility validation, antirollback validation, dependency-receipt validation, affected-function-scope authority, protected firmware-state update validation, policy-epoch validity, and revocation-state validity.¶

The applicable Finality Sink may be a firmware activation gate, microcode load gate, active-bank selector, patch-register gate, link-training controller, packagemanagement controller, security-controller gate, reset-release gate, or package execution-enable gate configured to refuse package-level firmware or microcode effectuation unless the scoped non-bearer capability is validly bound to the specific update, descriptor, validation evidence, protected-state representation, permitted activation scope, and sink.¶

6.50. Profile 176 — Counterfeit or Replacement Chiplet Quarantine Finality

Feature: Applies execution-finality enforcement to Counterfeit or Replacement Chiplet Quarantine, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes admitting, rejecting, quarantining, isolating, disabling, re-enabling, routing around, assigning limited function to, accepting output from, or permitting package participation by a suspected counterfeit chiplet, replacement chiplet, refurbished chiplet, repaired chiplet, unverified chiplet, degraded chiplet, or chiplet having incomplete, stale, revoked, or anomalous identity evidence.¶

The machine-verifiable act descriptor binds a package identifier, suspected chiplet identifier, expected chiplet identity commitment, observed chiplet identity evidence, provenance commitment, attestation result, replacement or repair record where applicable, quarantine state, permitted degraded-function scope, dependency receipt set, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include chiplet identity validation, provenance validation, counterfeit-risk validation, replacementauthority validation where applicable, quarantine-authority validation, degraded-function authority where applicable, output-acceptance authority, protected quarantine-state update validation, policy-epoch validity, revocation-state validity, and freshness validation.¶

The applicable Finality Sink may be a chiplet admission gate, quarantine gate, package interconnect gate, die-to-die link gate, workload scheduler gate, memory aperture gate, output-acceptance gate, reset-release gate, or package security-controller interface configured to fail closed, quarantine the chiplet, or restrict the chiplet to a permitted degraded-function scope unless the scoped non-bearer capability validly binds the Candidate Act to the descriptor, committed protected validation evidence, protected-state representation, quarantine state, permitted effectuation scope, and applicable Finality Sink.¶

6.51. Profile 177 — CXL Type-3 Memory-Pool Admission Finality

Feature: Applies execution-finality enforcement to CXL Type-3 Memory-Pool Admission, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes admitting, attaching, mapping, allocating, exposing, expanding, shrinking, revoking, or using a Compute Express Link Type-3 memory device, pooled memory device, coherent memory expander, disaggregated memory module, rack-scale memory pool, pooled HBM resource, persistent memory pool, or fabric-attached memory resource for use by a processor, accelerator, AI workload, satellite payload, tenant, model runtime, or memory-consuming subsystem.¶

The machine-verifiable act descriptor binds a memory-pool identifier, Type-3 device identifier, host identifier, accelerator identifier where applicable, memory region commitment, memory aperture identifier, access mode, tenant or workload scope, data class, memory-integrity epoch, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include memory-pool admission authority, device-identity authority, host or accelerator authority, memory-aperture authority, tenant or workload authority, memoryintegrity validation, poison-state validation where applicable, revocation-state validation, policy-epoch validity, protected memory-state update validation, and replay-prevention validation.¶

The applicable Finality Sink may be a CXL memory admission gate, Type-3 device controller, host memory controller, CXL switch, memory-pool allocator, page-table commit gate, IOMMU gate, HBM aperture gate, persistent-memory commit gate, or accelerator memory-ingress gate configured to refuse memory-pool admission unless the scoped non-bearer capability is validly bound to the Candidate Act, descriptor, committed protected validation evidence, protected-state representation, permitted memory scope, freshness constraint, and applicable Finality Sink.¶

6.52. Profile 178 — Coherent Page-Migration Finality

Feature: Applies execution-finality enforcement to Coherent Page-Migration, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes migrating, copying, moving, replicating, remapping, evicting, restoring, prefetching, coherently transferring, or admitting a memory page, memory line, cache line, tensor page, model-state page, gradient page, checkpoint page, key-value cache page, embedding page, persistent-memory page, or workload memory object across a coherent memory fabric, CXL fabric, PCIe fabric, rack-scale memory fabric, accelerator memory fabric, or host-to-device coherent interconnect.¶

The machine-verifiable act descriptor binds a source memory identifier, destination memory identifier, page or memory-object commitment, coherence-domain identifier, flownership state, access permissions, tenant or workload scope, data class, migration reason, freshness constraint, executionnality boundary, and applicable Finality Sink.¶

The authority predicates include source-memory authority, destination-memory authority, coherence-domain authority, page-ownership validation, access-permission validation, data-class authority, no-cross-tenant validation where applicable, dirty-state or writeback validation where applicable, protected page-state update validation, policy-epoch validity, revocation-state validity, and replayprevention validation.¶

The applicable Finality Sink may be a page-migration gate, memory-controller gate, CXL fabric switch, page-table commit gate, cachecoherence directory gate, DMA engine gate, accelerator memory admission gate, HBM controller, host memory controller, or destination memory commit interface configured to fail closed unless the scoped non-bearer capability is valid for the specific page or memory object, migration direction, coherence domain, permitted access scope, and sink.¶

6.53. Profile 179 — Memory Mirroring, Snapshot, and Checkpoint Finality

Feature: Applies execution-finality enforcement to Memory Mirroring, Snapshot, and Checkpoint, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes mirroring, snapshotting, checkpointing, cloning, duplicating, persisting, restoring, replicating, exporting, or externally storing memory contents from a coherent memory fabric, CXL memory pool, accelerator memory, HBM stack, shared memory region, persistent memory region, model-state memory, tensor memory, training-state memory, or payload memory.¶

The machine-verifiable act descriptor binds a source memory-region commitment, destination storage or mirror identifier, snapshot identifier, checkpoint identifier, memory epoch, data class, tenant or workload scope, model or payload context where applicable, retention or persistence scope, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include snapshot authority, mirror authority, checkpoint authority, destination authority, persistence authority, memory-state integrity validation, tenant or workload authority, no-unauthorized-copy validation, no-cross-domain validation where applicable, protected checkpoint-state update validation, revocation-state validation, policy-epoch validity, and freshness validation.¶

The applicable Finality Sink may be a snapshot commit gate, checkpoint writer, memory mirror controller, CXL memory switch, persistentmemory commit interface, storage controller, accelerator checkpoint gate, model-state commit gate, backup interface, or destination admission gate configured to deny snapshot, mirror, checkpoint, or clone effectuation unless the scoped non-bearer capability is validly bound to the specific memory contents, descriptor, committed protected validation evidence, protected-state representation, permitted retention or copy scope, and sink.¶

6.54. Profile 180 — Cross-Tenant AI Tensor Memory-Fabric Finality

Feature: Applies execution-finality enforcement to Cross-Tenant AI Tensor Memory-Fabric, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes permitting access, transfer, sharing, routing, admission, migration, mapping, exposure, replication, or reuse of tensor memory, activation memory, embedding memory, hidden-state memory, key-value cache memory, model-weight memory, gradient memory, optimizer-state memory, checkpoint memory, inference-output memory, or training-state memory across tenants, missions, workloads, model runtimes, accelerator partitions, memory pools, coherent fabrics, CXL fabrics, PCIe fabrics, or rack-scale AI memory fabrics.¶

The machine-verifiable act descriptor binds a source tenant or workload identifier, destination tenant or workload identifier, source memory-region commitment, destination memory aperture, tensor or model-state class, model identifier, accelerator identifier, partition identifier, linkage or correlation state where applicable, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include source authority, destination authority, tensor-class authority, cross-tenant sharing authority, memory-aperture authority, model-runtime authority, no-training or no-retention restriction validation where applicable, residual-state validation, linkage-state validation where applicable, protected memory-state update validation, policy-epoch validity, revocation-state validity, and replay-prevention validation.¶

The applicable Finality Sink may be a tensor-memory access gate, CXL memory aperture gate, accelerator memory controller, page-table commit gate, DMA mapping gate, tensor-buffer gate, model-runtime admission gate, cross-tenant transfer gate, destination partition gate, or coherent memory-fabric switch configured to refuse cross-tenant tensor-memory effectuation unless the scoped non-bearer capability is valid for the specific tensor class, source and destination scopes, memory aperture, permitted effectuation scope, and sink.¶

6.55. Profile 181 — Fabric-Switch Routing and Aperture-Control Finality

Feature: Applies execution-finality enforcement to Fabric-Switch Routing and Aperture-Control, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes configuring, updating, committing, activating, rerouting, expanding, shrinking, revoking, or using a routing rule, port mapping, memory aperture, access-control entry, path selection, fabric-switch table, virtual hierarchy, address decoder, fabric window, or access path within a CXL switch, PCIe switch, coherent memory switch, rack-scale memory-fabric switch, accelerator-fabric switch, or disaggregated memory interconnect.¶

The machine-verifiable act descriptor binds a fabric-switch identifier, source endpoint identifier, destination endpoint identifier, port identifier, route or path commitment, memory-aperture commitment, address-range commitment, access mode, tenant or workload scope, policy epoch, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include switch-configuration authority, source-endpoint authority, destination-endpoint authority, route authority, aperture authority, address-range authority, access-mode authority, no- unauthorized-path validation, no-cross-tenant validation where applicable, protected fabric-state update validation, revocation-state validation, and replay-prevention validation.¶

The applicable Finality Sink may be a fabric-switch commit gate, route-table gate, port-enable gate, address-decoder gate, memory-window gate, CXL switch controller, PCIe switch controller, fabric aperture controller, accelerator fabric gate, or endpoint admission gate configured to deny route or aperture effectuation unless the scoped non-bearer capability is validly bound to the route, aperture, descriptor, protected validation evidence, protected-state representation, freshness constraint, and sink.¶

6.56. Profile 182 — RAS Poison, ECC Fault, and Memory-Quarantine Finality

Feature: Applies execution-finality enforcement to RAS Poison, ECC Fault, and Memory-Quarantine, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

Problem Addressed: Prevents poisoned, degraded, or fault-affected memory from being released, migrated, restored, unquarantined, or consumed under stale reliability evidence or without an authorized recovery transition.¶

The Candidate Act includes consuming, releasing, migrating, restoring, clearing, quarantining, unquarantining, mirroring, checkpointing, admitting, or externally effectuating memory data, pages, cache lines, tensor buffers, model-state buffers, persistent memory objects, or accelerator memory regions affected by reliability availability-serviceability poison, ECC fault, uncorrectable error, correctable error threshold, memory-device degradation, CXL poison indication, PCIe error state, media error, coherency error, link error, or memory integrity anomaly.¶

The machine-verifiable act descriptor binds a memory device identifier, memory-region commitment, poison-state evidence, ECC evidence, RAS event identifier, correction or quarantine state, affected data class, workload or tenant scope, recovery action, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include RAS-event validity, poison-state validation, ECC-result validation, quarantine authority, unquarantine authority where applicable, recovery authority, memory-integrity authority, no-stale-error-state validation, protected quarantine-state update validation, policy-epoch validity, revocation-state validity, and freshness validation.¶

The applicable Finality Sink may be a memory-quarantine gate, poison-clear gate, memory-read gate, memory-write gate, page-migration gate, checkpoint gate, CXL memory controller, coherent fabric switch, accelerator memory admission gate, or destination memory commit interface configured to fail closed, quarantine the memory region, or restrict use to a permitted degraded or recovery scope unless the scoped non-bearer capability is validly bound to the RAS or ECC evidence, descriptor, protected validation evidence, protected-state representation, and applicable Finality Sink.¶

6.57. Profile 202 — Optical-Lane Enable Finality

Feature: Applies execution-finality enforcement to Optical-Lane Enable, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes enabling, activating, admitting, allocating, binding, routing, transmitting through, receiving through, disabling, re-enabling, or externally effectuating an optical lane of a co-packaged optical input/ output interface, silicon-photonic chiplet, optical serializer/deserializer, photonic interposer, package-integrated optical transceiver, accelerator optical fabric, rack-scale optical fabric, or AI microchip optical egress interface.¶

The machine-verifiable act descriptor binds a package identifier, chip identifier, photonic chiplet identifier, optical-lane identifier, source accelerator or memory endpoint, destination endpoint, lane direction, bandwidth class, data class, workload or tenant scope, optical carrier or wavelength identifier where applicable, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include optical-lane authority, package identity validation, photonic component identity validation, source-endpoint authority, destination-endpoint authority, bandwidth authority, data-class authority, workload or tenant authority, no-unauthorized-egress validation, protected optical-lane state update validation, policy-epoch validity, revocation-state validity, and replay-prevention validation.¶

The applicable Finality Sink may be an optical-lane enable gate, optical serializer gate, optical transmitter gate, optical receiver admission gate, photonic chiplet controller, optical lane scheduler, package optical I/O controller, optical switch interface, accelerator optical-egress gate, or destination optical-ingress interface configured to refuse optical-lane effectuation unless the scoped non-bearer capability is validly bound to the optical-lane Candidate Act, the descriptor, the committed protected validation evidence, the protected-state representation, the permitted optical-lane scope, the freshness constraint, and the applicable Finality Sink.¶

6.58. Profile 203 — Silicon-Photonic Modulator Output Finality

Feature: Applies execution-finality enforcement to Silicon-Photonic Modulator Output, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes driving, enabling, biasing, modulating, encoding, symbol-mapping, transmitting, releasing, or externally effectuating data-bearing output through a silicon-photonic modulator, Mach-Zehnder modulator, ring modulator, electro-absorption modulator, optical modulator array, photonic transmit path, optical symbol mapper, optical serializer, or package-integrated optical transmission circuit associated with an AI accelerator or co-packaged optical interface.¶

The machine-verifiable act descriptor binds a chip identifier, package identifier, photonic chiplet identifier, modulator identifier, optical-lane identifier, source data-buffer identifier, data class, modulation class, symbol stream commitment, workload or tenant scope, destination endpoint, optical carrier or wavelength identifier, permitted output window, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include modulator-output authority, source-buffer authority, data-class authority, modulation-scope authority, destination authority, optical-lane receipt validation where required, no-debug-ortest-output laundering validation where applicable, protected modulator-state update validation, policy-epoch validity, revocation-state validity, and freshness validation.¶

The applicable Finality Sink may be a modulator-drive gate, modulator-bias gate, optical symbol-mapper gate, serializer output gate, optical transmitter gate, optical-lane transmit gate, photonic chiplet output gate, package optical-egress gate, or destination optical-admission gate configured to deny data-bearing modulation unless the scoped non-bearer capability is valid for the specific modulator, data class, symbol stream, destination, permitted output scope, and sink.¶

6.59. Profile 204 — Wavelength-Retune and Optical-Switch Finality

Feature: Applies execution-finality enforcement to Wavelength-Retune and Optical-Switch, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes retuning, selecting, switching, multiplexing, demultiplexing, routing, redirecting, assigning, reassigning, enabling, disabling, or externally effectuating a wavelength, wavelength-divisionmultiplexed channel, optical carrier, optical path, optical switch state, microring state, arrayed- waveguide-grating state, wavelength-selective switch state, optical crossbar state, or photonic routing state in a co-packaged optical input/output system, silicon-photonic chiplet, accelerator optical fabric, rack-scale optical fabric, or AI microchip optical interconnect.¶

The machine-verifiable act descriptor binds a package identifier, photonic chiplet identifier, optical switch identifier, current wavelength or route state, candidate wavelength or route state, source endpoint, destination endpoint, lane identifier, data class, traffic class, tenant or workload scope, route commitment, policy epoch, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include wavelength authority, optical-switch authority, route authority, source-endpoint authority, destination-endpoint authority, lane authority, tenant or workload authority, no-unauthorized-route validation, no-cross-tenant route validation where applicable, protected wavelength or switch-state update validation, revocation-state validation, policy-epoch validity, and replay-prevention validation.¶

The applicable Finality Sink may be a wavelength-selection gate, optical-switch commit gate, microring tuning gate, WDM channel gate, optical crossbar gate, wavelength-selective switch gate, route-table gate, optical-lane controller, package optical-routing controller, or destination optical-ingress gate configured to fail closed unless the scoped non-bearer capability is validly bound to the selected wavelength or route, descriptor, committed protected validation evidence, protected-state representation, permitted route scope, and applicable Finality Sink.¶

6.60. Profile 205 — Optical Tensor Egress Finality

Feature: Applies execution-finality enforcement to Optical Tensor Egress, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes releasing, serializing, routing, transmitting, mapping, copying, exporting, sharding, streaming, mirroring, multicasting, snapshotting, or externally effectuating tensor data, activation data, embedding data, hidden-state data, key-value cache data, attention-state data, gradient data, optimizer-state data, model-weight data, checkpoint data, inference-output tensor data, training-state data, feature-vector data, retrieval-vector data, or intermediate AI computation state from an accelerator, memory fabric, tensor buffer, model runtime, or AI microchip through a co-packaged optical interface or optical fabric.¶

The machine-verifiable act descriptor binds a chip identifier, package identifier, accelerator identifier, memory or tensor-buffer identifier, tensor object digest or class, model identifier, model version or checkpoint digest, workload or tenant scope, source memory aperture, destination endpoint, optical-lane identifier, modulator identifier where applicable, wavelength or route identifier where applicable, dependency receipt set, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include tensor-egress authority, tensor-class authority, source-memory authority, workload or tenant authority, model-runtime authority, destination authority, optical-lane authority, modulator or wavelength receipt validation where required, no-cross-tenant validation, no-debug-or-telemetry laundering validation, protected tensor-egress state update validation, policy-epoch validity, revocation-state validity, and replay-prevention validation.¶

The applicable Finality Sink may be a tensor-buffer release gate, accelerator memory egress gate, HBM or SRAM controller, page-table or memory-aperture gate, DMA-to-optical gate, tensor compiler egress gate, optical serializer input gate, optical-lane transmit gate, modulator input gate, package-to-fiber gate, destination optical-receive gate, destination memory-admission gate, or destination accelerator-input gate configured to refuse tensor egress unless the scoped non-bearer capability is validly bound to the optical tensor-egress Candidate Act, descriptor, committed protected validation evidence, protected-state representation, permitted tensor and optical scope, freshness constraint, and applicable Finality Sink.¶

6.61. Profile 206 — External Laser and Package-to-Fiber Coupling Finality

Feature: Applies execution-finality enforcement to External Laser and Package-to-Fiber Coupling, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes enabling, admitting, injecting, coupling, selecting, powering, ramping, gating, aligning, switching, opening, closing, or externally effectuating an external laser source, remote laser bank, optical carrier source, laser-comb line, optical power bus, carrier-distribution path, optical shutter, optical isolator, variable optical attenuator, grating coupler, edge coupler, fiber array, optical connector, package-to-fiber interface, package-to-board optical path, optical monitor port, debug optical coupler, inbound optical receive path, or outbound optical transmit path associated with a co-packaged optical input/ output system, silicon-photonic AI package, photonic chiplet, or AI accelerator optical fabric.¶

The machine-verifiable act descriptor binds a package identifier, chip identifier, photonic chiplet identifier, external laser source identifier, carrier identifier, wavelength or comb-line identifier, optical power bound, package optical port identifier, coupler identifier, fiber endpoint identifier, coupling direction, source endpoint, destination endpoint, data class, workload or tenant scope, diagnostic or calibration scope where applicable, dependency receipt set, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include external-laser authority, carrier or wavelength authority, optical-power authority, package-port authority, coupler authority, ber-endpoint authority, coupling-direction authority, source or destination authority, no-unauthorized-monitor-tap validation, no-debug-coupling laundering validation, no-cross-tenant carrier-sharing validation where applicable, optical-lane or tensor-egress receipt validation where required, protected laser and coupling-state update validation, policy-epoch validity, revocation-state validity, and freshness validation.¶

The applicable Finality Sink may be an external laser enable gate, remote laser-bank output gate, laser-comb-line gate, carrier-distribution gate, optical shutter gate, optical isolator gate, attenuator gate, package optical port gate, gratingcoupler gate, edge-coupler gate, ber connector interlock, monitor-port gate, inbound receive coupling gate, outbound transmit coupling gate, or destination optical-admission gate configured to fail closed unless the scoped non-bearer capability is validly bound to the external laser or package-to-fiber coupling Candidate Act, descriptor, committed protected validation evidence, protected-state representation, permitted coupling scope, freshness constraint, and applicable Finality Sink.¶

6.62. Profile 208 — eFPGA Bitstream-Load Finality

Feature: Applies execution-finality enforcement to eFPGA Bitstream-Load, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes decrypting, loading, writing, activating, selecting, partially reconfiguring, committing, rolling back, restoring, scrubbing, repairing, or otherwise applying a bitstream, partial bitstream, programmable-logic overlay, routing image, configuration image, logic-region update, DSP-block configuration, block-memory initialization, RF payload logic image, baseband logic image, AI accelerator overlay, memory-interface logic update, cryptographic datapath logic update, recovery bitstream, safe-mode bitstream, emergency bitstream, debug overlay, or diagnostic overlay in an embedded FPGA, programmable logic fabric, spaceborne reconfigurable fabric, satellite payload fabric, or orbital accelerator fabric.¶

The machine-verifiable act descriptor binds a satellite identifier, target programmable fabric identifier, hardware revision, configuration controller identifier, target region identifier, current bitstream version, candidate bitstream digest, configuration bank, region-boundary commitment, routing-resource commitment, clock-domain commitment, reset-domain commitment, update class, mission-state commitment, dependency receipt set, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include target-fabric identity validation, bitstream-integrity validation, bitstream-authority validation, current-configuration validation, hardware-compatibility validation, partial-reconfiguration boundary validation, routing-isolation validation, resource-scope validation, anti-rollback validation, recovery or emergency-scope validation where applicable, protected eFPGA configuration-state update validation, policy-epoch validity, revocation-state validity, and replay-prevention validation.¶

The applicable Finality Sink may be a bitstream decryptor gate, configuration-memory write gate, partial-reconfiguration controller, region activation gate, active-bank selector, routing-matrix commit gate, LUT configuration gate, DSPblock configuration gate, clock-enable gate, reset-release gate, fabric-output gate, debugoverlay gate, recovery activation gate, rollback gate, or programmable-fabric execution-enable gate configured to fail closed unless the scoped non-bearer capability is validly bound to the eFPGA bitstream-load Candidate Act, descriptor, committed protected validation evidence, protected-state representation, permitted configuration scope, freshness constraint, and applicable Finality Sink.¶

6.63. Profile 209 — AI Accelerator Partition-Reconfiguration Finality

Feature: Applies execution-finality enforcement to AI Accelerator Partition-Reconfiguration, keeping the operation non-effective until the exact Candidate Act, machine-verifiable descriptor, protected validation evidence, protected state, scoped non-bearer capability, and applicable Finality Sink correspond at the consequence boundary.¶

The Candidate Act includes creating, deleting, resizing, merging, splitting, migrating, resetting, activating, deactivating, admitting, exposing, remapping, reallocating, reclaiming, rolling back, or otherwise reconfiguring an AI accelerator partition, GPU partition, NPU partition, tensor-core group, compute slice, accelerator queue, model-runtime partition, memory aperture, HBM partition, SRAM partition, scratchpad partition, tensor-buffer route, DMA path, page-table mapping, IOMMU aperture, debug access scope, emergency compute reassignment, or recovery partition in a satellite AI payload, orbital AI accelerator, spaceborne compute fabric, or shared mission accelerator.¶

The machine- verifiable act descriptor binds at least a satellite identifier, payload identifier, accelerator identifier, hardware revision, current partition-map digest, candidate partition-map digest, partition identifier, compute-resource commitment, memory-aperture commitment, tensor-buffer commitment, DMA-path commitment, tenant or mission identifier, workload identifier, model-runtime identifier, residual-state commitment, dependency receipt set, freshness constraint, execution-finality boundary, and applicable Finality Sink.¶

The authority predicates include accelerator identity validation, current partition-state validation, candidate partition-map authorization, compute-resource authority, memory-aperture authority, tensor-buffer authority, DMA-path authority, tenant or mission authority, workload authority, model-runtime authority, residual-state cleanup or sealing validation, emergency or debug-scope validation where applicable, protected partition-state update validation, policy-epoch validity, revocation-state validity, and replay-prevention validation.¶

The applicable Finality Sink may be a partition-map commit gate, compute-slice allocation gate, tensor-core allocation gate, accelerator scheduler gate, queue-assignment gate, HBM aperture gate, page-table commit gate, DMA mapping gate, tensor-buffer route gate, model-runtime admission gate, debugaccess gate, emergency partition gate, reset-release gate, clock-enable gate, or execution-enable gate configured to refuse partition mutation unless the scoped non-bearer capability is valid for the specific accelerator partition, resource scope, memory scope, workload, model runtime, protected validation evidence, protected-state representation, and sink.¶

6.64. Profile 210 — Reconfigurable Optical-Circuit and Accelerator-Pod Topology Finality

Feature: Applies execution-finality enforcement to dynamic accelerator-topology changes in which optical circuits, pod membership, inter-pod routes, spare accelerators, or fault-bypass paths are reconfigured while AI workloads remain active.¶

Problem Addressed: Prevents a valid fault-recovery, load-balancing, or topology-optimization decision from automatically becoming authority to connect the wrong accelerator group, tenant, destination, optical path, or replacement component.¶

The Candidate Act may include establishing, tearing down, rerouting, or replacing an optical circuit; adding or removing an accelerator cube, pod, superpod segment, or spare; changing an inter-chip or inter-pod route; bypassing a failed unit; changing a fabric-membership map; or committing a topology transition that changes which accelerators can exchange tensors, model state, gradients, or inference context.¶

The machine-verifiable descriptor binds the source and destination accelerator groups, topology epoch, optical-circuit or path identifier, affected links, health or fault evidence, workload and tenant identifiers, model or job scope, bandwidth or capacity class, permitted peer set, maintenance or failover reason, freshness state, and the designated topology Finality Sink.¶

Authority predicates may include accelerator and link health, workload ownership, tenant isolation, destination authorization, spare-component admission, topology-consistency checks, fault-domain separation, capacity or congestion limits, maintenance authority, policy epoch, revocation state, replay prevention, and confirmation that the proposed route does not create an unauthorized cross-domain path.¶

The Finality Sink may be an optical-circuit-switch controller, optical-fabric manager, accelerator-fabric gateway, pod-topology commit gate, inter-chip-interconnect edge, route-programming controller, or equivalent component that can refuse the topology change. The topology remains non-effective until the sink verifies the exact proposed membership and route against current protected state.¶

This profile is strongly relevant to Google-style TPU pod and superpod systems using high-bandwidth inter-chip interconnect and dynamically reconfigurable optical circuit switching. It is also applicable to other optical or electrical AI-factory fabrics. It differs from Profile 204, which focuses on wavelength or optical-switch state itself, by binding the higher-level accelerator membership, failover, and workload topology that the optical reconfiguration would make effective.¶

6.65. Profile 211 — Accelerator-Pod Membership and Collective-Domain Admission Finality

Feature: Controls admission, removal, replacement, or role changes of accelerators participating in a distributed training, inference, or collective-communication domain.¶

Problem Addressed: Prevents an authenticated and healthy accelerator from being treated as automatically authorized to join a particular model, tenant, shard, collective group, data class, or distributed job.¶

The Candidate Act may include adding or removing an accelerator from a pod, collective group, sharding ring, expert group, data-parallel group, tensor-parallel group, pipeline stage, or inference-serving group; replacing a failed member; changing a member role; or activating a newly discovered accelerator for a running distributed job.¶

The descriptor binds accelerator identity and attestation, hardware revision, workload or job identifier, tenant, model, shard or expert role, collective-group identifier, peer set, topology epoch, memory or data class, permitted communication scope, resource claim, freshness value, and Finality Sink.¶

Authority predicates include device integrity, job membership, model ownership, tenant isolation, shard-placement validity, peer-set consistency, data-class authority, permitted collective role, capacity and health state, revocation, policy epoch, and anti-replay. Where replacement occurs after failure, the predicates may also require protected confirmation that stale state from the removed member cannot re-enter the active group.¶

The Finality Sink may be a collective-engine admission gate, accelerator scheduler, inter-chip-interconnect controller, fabric membership table, sharding controller, runtime device-admission gate, or protected orchestration commit point. Collective traffic is not admitted merely because network connectivity exists.¶

This profile is strongly relevant to large TPU, GPU, and custom-accelerator pods. For Google-class systems it complements ICI and pod-scale collective execution by adding an act-bound membership decision before a device becomes part of the workload communication domain. It is narrower than Profile 41, which governs a collective communication operation after participants are already established.¶

6.66. Profile 212 — Compiler-to-Silicon Executable, Kernel, and Device-Graph Activation Finality

Feature: Binds activation of a compiler-produced accelerator executable, custom kernel, sharding plan, memory layout, or device graph to the exact source workload, target hardware, approved execution envelope, and current protected state.¶

Problem Addressed: Prevents authorization of a source model or high-level program from being treated as blanket authority for whatever lowered binary, kernel, graph, partition plan, precision mode, or collective schedule a compiler or optimization system produces.¶

The Candidate Act may include loading or activating a compiled accelerator program, custom kernel, executable graph, device binary, kernel bundle, sharding plan, partition map, collective schedule, memory-layout plan, tiling configuration, precision profile, or runtime-generated executable artifact.¶

The descriptor binds the source model or program digest, compiler and runtime identity, compiler version and material options, compiled-artifact digest, target accelerator identity or class, model version, memory layout, sharding or collective plan, precision and arithmetic profile, permitted input and output classes, workload or tenant, policy epoch, freshness value, and Finality Sink.¶

Authority predicates may include source-to-binary provenance, compiler attestation, artifact integrity, target-hardware compatibility, approved precision or numerical envelope, memory-range constraints, collective-participant validity, tenant isolation, safety-policy state, revocation, and confirmation that late compilation or runtime specialization has not changed a load-bearing property outside the permitted envelope.¶

The Finality Sink may be an accelerator executable loader, graph-runtime admission gate, kernel-dispatch controller, command processor, device-program cache, firmware runtime, or protected scheduler that can keep the compiled artifact non-executable until the scoped capability is verified.¶

This profile has strong relevance to Google XLA/Pallas-style compilation, AWS Neuron compiler and kernel workflows, and Microsoft Maia software stacks. The profile is deliberately vendor-neutral: the key distinction is between authority for the high-level workload and authority for the exact executable representation that will become silicon-effective.¶

6.67. Profile 213 — Confidential-Realm Granule Ownership and Security-State Transition Finality

Feature: Applies execution-finality enforcement to reassignment of physical memory granules or protected memory regions between confidential, non-confidential, secure, root-controlled, or otherwise hardware-isolated security domains.¶

Problem Addressed: Prevents a valid confidential workload or hypervisor operation from automatically authorizing a stale, incomplete, unsanitized, or misbound memory-ownership transition that exposes data across trust domains.¶

The Candidate Act may include assigning, donating, reclaiming, destroying, mapping, unmapping, delegating, or changing the security state of a physical granule, page, memory region, translation context, or protected address range associated with a confidential workload.¶

The descriptor binds the confidential-workload or Realm identity, attested initial measurement where applicable, physical granule or address range, source and destination security states, translation or protection-table epoch, ownership state, scrub or zeroization status, memory-encryption context, caller authority, freshness state, and Finality Sink.¶

Authority predicates may include workload attestation, ownership continuity, source-state validity, destination-state authorization, successful sanitization where required, no-aliasing or no-cross-domain mapping, translation consistency, rollback resistance, policy epoch, revocation, and replay prevention.¶

The Finality Sink may be a protected granule-protection-table update gate, Realm-management monitor, requester-side protection filter, completer-side filter, memory-protection engine, secure monitor, or memory-controller admission point with mandatory control over the ownership transition.¶

This profile is strongly relevant to Arm Confidential Compute Architecture and Realm Management Extension systems, where hardware security state and memory ownership are explicit architectural properties. Execution finality does not replace CCA isolation; it adds a rule that the exact ownership transition is itself a Candidate Act requiring current act-bound authority before the new security state becomes effective.¶

6.68. Profile 214 — Confidential Device Attach and SMMU Stream-Ownership Finality

Feature: Controls assignment of a physical or virtual I/O device, DMA stream, interrupt path, or translation context to a confidential workload or protected VM.¶

Problem Addressed: Prevents device identity or workload attestation alone from authorizing an incorrect SMMU stream, DMA aperture, PASID/SSID context, interrupt mapping, or device attachment that could bypass confidential-memory isolation.¶

The Candidate Act may include attaching or detaching a device, assigning a Stream ID or substream, installing or changing an SMMU translation context, opening a DMA aperture, binding a PASID or equivalent process address-space identity, routing interrupts, enabling peer-to-peer access, or granting a device access to a confidential memory region.¶

The descriptor binds confidential-workload identity and measurement, device identity and attestation, requester identifier, Stream ID and substream or PASID where applicable, I/O virtual address range, physical memory range, DMA direction, interrupt mapping, translation-table digest, protection-domain identifier, policy epoch, freshness, and Finality Sink.¶

Authority predicates include device integrity, confidential-workload ownership, stream uniqueness, IOVA and physical-range authorization, direction and access-mode constraints, requester/completer protection, translation consistency, interrupt ownership, peer-set limits, revocation, and replay prevention.¶

The Finality Sink may be an SMMU configuration commit gate, device-assignment controller, requester-side filter, completer-side filter, IOMMU/SMMU translation controller, PCIe or coherent-I/O admission point, interrupt-remapping controller, or protected device-attach manager.¶

This profile is strongly relevant to Arm CCA secure-device-attachment work and Arm-based cloud infrastructure. It is more specific than generic DMA-aperture profiles because the device, translation stream, and confidential-workload identity must be bound together before the device gains effective access to protected memory.¶

6.69. Profile 215 — Security-Domain-Aware Coherent-Mesh Agent Admission and Confidential-I/O Finality

Feature: Applies finality to adding an accelerator, chiplet, CXL-attached component, I/O requester, or other agent to a coherent mesh or shared-memory domain whose transactions cross security boundaries.¶

Problem Addressed: Prevents a component that is electrically present and protocol-compatible from automatically gaining coherent visibility, home-node reachability, cache participation, or shared-memory access beyond its authorized security domain.¶

The Candidate Act may include admitting a coherent agent, activating a chip-to-chip coherent link, assigning a home node, extending a snoop or coherency domain, exposing an address region, enabling a cache or system-level cache path, connecting a CXL-attached device, or permitting confidential I/O transactions to traverse the coherent mesh.¶

The descriptor binds agent or chiplet identity, link identity, coherent protocol or interface class, security domain, requester and destination nodes, home-node or region assignment, physical address ranges, cacheability and shareability attributes, CXL or chiplet role where applicable, protected workload identity, policy epoch, freshness, and Finality Sink.¶

Authority predicates may include component attestation, coherent-domain membership, security-state compatibility, address-range authority, no-cross-tenant or no-cross-Realm exposure, cache and snoop policy, CXL or chiplet admission, revocation, topology epoch, and consistency with protected memory ownership.¶

The Finality Sink may be a coherent-mesh admission controller, home-node controller, CHI or chip-to-chip interface gate, mesh routing commit point, CXL host or device controller, system-cache controller, or protected address-region gate that can prevent the agent from becoming globally observable.¶

This profile is strongly relevant to Arm Neoverse CMN/CHI and chiplet-ready Compute Subsystems, including systems that connect CPUs, accelerators, I/O, CXL devices, and confidential-compute components. It complements Profiles 39, 148, 153, and 181 by adding explicit security-domain-aware admission of a coherent agent rather than only controlling a transfer or route that occurs after admission.¶

6.70. Profile 216 — Attested Infrastructure-Offload Device Join to a Confidential Workload Trust Boundary Finality

Feature: Controls the point at which a purpose-built network, storage, virtualization, or security offload device is admitted into the trusted computing boundary of a confidential VM or protected workload.¶

Problem Addressed: Prevents successful device attestation or encrypted-link establishment from being treated as sufficient authority to enlarge a confidential workload trust boundary when device identity, firmware, function scope, or tenant binding is stale or mismatched.¶

The Candidate Act may include attaching an offload device to a confidential VM, accepting the device into the workload trusted computing base, establishing an encrypted PCIe or equivalent protected link, provisioning a device-specific traffic or storage key, exposing a virtual function, or enabling the device to process confidential workload I/O.¶

The descriptor binds workload or VM identity and attestation, device identity and attestation, firmware and hardware revision, PCIe function or device function, protected-link identifier, key or cryptographic-context reference, permitted network and storage roles, tenant, host, policy epoch, freshness state, and Finality Sink.¶

Authority predicates include workload integrity, device integrity, approved firmware, protected-link establishment, device-role limitation, tenant binding, no-cross-VM assignment, key freshness, revocation, policy epoch, and confirmation that the device function admitted to the trust boundary is the same function that will handle the consequential I/O.¶

The Finality Sink may be a confidential-device attach controller, PCIe IDE or protected-link key gate, offload ASIC admission point, root-of-trust controller, hypervisor or confidential-VM device manager, or DPU attachment gate with mandatory control over confidential I/O enablement.¶

This profile has especially strong relevance to Microsoft Azure Boost confidential-device architecture, where a purpose-built offload device can join a confidential VM trusted-compute base through attested hardware and protected PCIe links. The profile generalizes that boundary without assuming any Microsoft-specific protocol.¶

6.71. Profile 217 — Network/Storage Offload Queue and Encryption-Context Activation Finality

Feature: Applies finality to activation of high-rate network or storage queues together with the DMA, address, encryption, tenant, and destination contexts that make those queues operational.¶

Problem Addressed: Prevents a valid VM, driver, or offload device from activating a queue whose memory region, cryptographic context, virtual-network destination, storage namespace, or tenant binding does not match the authority under which the queue was created.¶

The Candidate Act may include creating or activating a network transmit or receive queue, RDMA queue, storage submission or completion queue, NVMe path, virtual-network offload path, storage-offload channel, queue doorbell, ring mapping, or hardware encryption context associated with wire-speed or storage-speed processing.¶

The descriptor binds VM or workload identity, offload-device identity, queue or ring identifier, DMA memory ranges, virtual-network or storage destination, disk or namespace identifier, traffic or storage class, encryption-context reference, tenant, bandwidth or IOPS scope, queue lifetime, policy epoch, freshness, and Finality Sink.¶

Authority predicates may include queue ownership, memory-range authority, device integrity, destination authorization, disk or namespace authority, encryption-key validity, traffic class, RDMA scope, tenant isolation, rate or quota state, revocation, and replay prevention.¶

The Finality Sink may be a network-adapter queue controller, offload ASIC, DPU, storage-offload engine, NVMe controller, RDMA engine, encryption engine, queue-doorbell gate, DMA controller, or protected queue-activation controller.¶

This profile maps strongly to Microsoft Azure Boost and MANA-class designs in which networking and storage are moved into purpose-built silicon, but it is not Microsoft-specific. It complements Profiles 18, 42, 43, and 62 by making the queue plus its encryption and memory context the indivisible Candidate Act rather than validating only a packet, RDMA key, or generic DPU operation.¶

6.72. Profile 218 — Isolated Management-Controller Servicing and Data-Path Mutation Finality

Feature: Controls servicing, diagnostics, failover, firmware, reset, and configuration commands issued by an isolated management processor when those commands can alter a tenant-facing high-speed data path.¶

Problem Addressed: Prevents isolation of the management processor from being confused with authorization for every data-path mutation that the management processor is technically capable of causing.¶

The Candidate Act may include resetting a network or storage offload engine, loading a data-path configuration, changing an ASIC or FPGA table, failing over a port, rotating a data-path key, enabling diagnostics, entering maintenance state, applying a servicing operation, changing an agent or firmware component, or re-enabling a tenant I/O path after repair.¶

The descriptor binds management-controller identity, target component, exact servicing command, pre-state and intended post-state, affected VM or tenant set, maintenance window, firmware or configuration digest, failover target, key or security context if relevant, rollback point, policy epoch, freshness, and Finality Sink.¶

Authority predicates may include maintenance authority, component health, tenant-impact bounds, firmware or configuration provenance, rollback safety, no-debug-data leakage, approved failover destination, cryptographic-context continuity, revocation, and confirmation that the target data path is the one described by the servicing request.¶

The Finality Sink may be an isolated management SoC, ASIC configuration gate, FPGA reconfiguration gate, reset-release controller, firmware activation gate, port-failover controller, key-programming engine, or hardware state machine that controls resumption of the tenant data path.¶

This profile has strong relevance to the publicly described next generation of Azure Boost, which separates a dedicated Arm-based management SoC from the ASIC/FPGA data path. The execution-finality addition is narrow: even an authenticated management command remains non-effective until the exact tenant-impacting hardware transition is verified at the component that performs it.¶

6.73. Profile 219 — Authenticated Cloud Administrative Intent to Host-Independent Hardware Effect Finality

Feature: Binds an authenticated and audited cloud administrative or control-plane request to the exact network, storage, virtualization, or host-management effect later performed by host-independent infrastructure hardware.¶

Problem Addressed: Prevents successful authentication of an administrative API call from being treated as bearer authority for a different, broader, replayed, stale, or misrouted hardware transition inside the offload plane.¶

The Candidate Act may include attaching or detaching a network interface, changing a route or security group implementation state, mapping or unmapping storage, modifying a virtualization resource, resetting an instance device, changing a host-management state, or applying an operator or service control-plane action that is ultimately executed by dedicated infrastructure hardware.¶

The descriptor binds the administrative request digest, authenticated principal or service identity, instance or workload identity, affected network interface, volume, device, route, resource, requested transition, tenant, region or fault domain where relevant, control-plane epoch, nonce or freshness state, and the exact hardware Finality Sink.¶

Authority predicates may include request integrity, principal scope, resource ownership, current instance state, destination and region scope, no-cross-tenant effect, maintenance or service authority, revocation, policy epoch, idempotency or replay state, and correspondence between the API-level intent and the pending hardware operation.¶

The Finality Sink may be a host-independent network or storage offload device, Nitro-class controller, minimal-hypervisor interface, virtual-network gate, storage-mapping controller, hardware management controller, or protected infrastructure state machine that can deny the effect independently of the requesting host.¶

This profile is strongly relevant to AWS Nitro-class architecture, where virtualization, storage, networking, and management functions are deliberately moved into dedicated hardware and a minimal hypervisor. The profile complements authenticated and audited administrative APIs by adding a second binding from authorized intent to the exact hardware transition that becomes effective.¶

6.74. Profile 220 — UltraServer-Scale Accelerator Fabric Membership and Topology-Claim Finality

Feature: Controls discovery, allocation, membership, and topology transitions in tightly coupled accelerator systems that expose many AI accelerators as one large serving or training resource.¶

Problem Addressed: Prevents scheduler discovery or resource availability from automatically authorizing an accelerator, fabric segment, memory domain, or replacement device to join a distributed workload with the wrong tenant, model, topology, or isolation state.¶

The Candidate Act may include discovering or admitting an accelerator, allocating an UltraServer-scale resource, enabling an inter-accelerator link, adding or removing a device from a fabric, generating or applying a topology-aware resource claim, assigning an accelerator group to an EKS or other scheduler workload, replacing a failed member, or changing the collective topology used by a running model.¶

The descriptor binds accelerator identities and attestation, server or rack identity, interconnect or switch identifiers, topology map, resource-claim digest, workload and tenant, model version, memory and data class, collective group, capacity allocation, scheduler epoch, firmware state, freshness, and Finality Sink.¶

Authority predicates may include accelerator health and integrity, resource-claim validity, topology consistency, workload ownership, tenant isolation, model authorization, memory or data-class restrictions, permitted collective membership, firmware compatibility, revocation, and replay prevention.¶

The Finality Sink may be an accelerator-fabric controller, NeuronSwitch- or equivalent scale-up switch, inter-chip link controller, scheduler/operator admission gate, accelerator-runtime controller, resource-claim commit point, or protected device-enable gate.¶

This profile maps strongly to AWS Trainium UltraServer and Neuron fabric directions, including topology-aware allocation of Trainium accelerators. It is related to Profile 211 but focuses on a cloud-exposed UltraServer resource and its scheduler resource claim, making it useful for systems where a rack-scale accelerator fabric is allocated as a managed cloud object.¶

6.75. Profile 221 — Distributed EFA/RDMA Endpoint, Peer-Set, and Job-Fabric Admission Finality

Feature: Binds a high-performance distributed-job endpoint to its accelerator or instance, authorized peer set, exposed memory, encryption state, and current distributed-job membership before the endpoint can participate in the AI-factory fabric.¶

Problem Addressed: Prevents possession of a valid high-performance network interface or RDMA capability from permitting communication with the wrong peers, exposing an unauthorized memory region, or joining a stale or cross-tenant distributed job.¶

The Candidate Act may include allocating an EFA- or RDMA-capable interface to a workload, creating or activating an endpoint, admitting a flow or communication context, exposing a remote-accessible memory region, joining a distributed training or inference job, changing an authorized peer set, enabling accelerator-to-accelerator communication, or restoring an endpoint after failover.¶

The descriptor binds instance and accelerator identity, network-device identity, distributed-job identifier, authorized peer set, memory-region identifiers and access modes, traffic or collective class, encryption state, cluster or UltraCluster epoch, tenant, route or placement scope, bandwidth or rate scope, freshness, and Finality Sink.¶

Authority predicates may include endpoint integrity, job membership, peer-set authorization, memory-region ownership, transfer direction, tenant isolation, encryption state, traffic class, route scope, congestion or safety constraints, revocation, policy epoch, and replay prevention.¶

The Finality Sink may be an EFA- or RDMA-capable network interface, Nitro-class network device, SmartNIC or DPU, RDMA admission controller, memory-key or memory-window gate, cluster-fabric edge, or protected endpoint-activation controller.¶

This profile is strongly relevant to AWS EFA and UltraCluster environments, where distributed AI workloads depend on very high-rate network communication while the Nitro System provides hardware-offloaded networking and encryption. It complements Profiles 43 and 44 by binding the endpoint, peer set, distributed-job identity, and exposed memory together before fabric participation becomes effective.¶

6.76. Profile 222 — Protected Neural-State Descriptor and Privacy-Preserving Accelerator-State Proof Finality

Feature: Allows a protected accelerator or model-serving domain to prove the load-bearing properties of current neural or runtime state without requiring unrestricted disclosure of the underlying model state, activations, context, weights, or tenant-sensitive execution details.¶

Problem Addressed: Prevents a binary model identity, static attestation result, or coarse workload measurement from being treated as proof that the exact current neural state, adapter state, execution graph, context state, or runtime configuration is suitable for a consequential output, transfer, or hardware transition.¶

The Candidate Act may include releasing an AI output, exporting a tensor or latent representation, admitting a model to an accelerator, reusing a context or KV-cache object, activating an adapter or execution graph, transferring model state, or permitting a downstream agentic or infrastructure action whose authority depends on the current protected neural or runtime state.¶

A Neural-State Descriptor may bind selected properties of the active model, adapter set, execution graph, quantization or arithmetic profile, context-memory state, relevant activation or influence class, runtime behavioral state, workload identity, tenant, policy epoch, freshness value, and intended Finality Sink. The descriptor need not expose complete raw weights, activations, reasoning traces, or proprietary model internals.¶

The Protected Enforcement Domain may establish the descriptor using protected measurements, commitments, bounded proofs, attested summaries, privacy-preserving proofs, or equivalent mechanisms that allow the required predicate to be verified while minimizing unnecessary disclosure of internal model state.¶

Authority predicates may include approved model or adapter identity, allowed execution graph, tenant isolation, permitted context reuse, runtime-state correspondence, behavioral-envelope membership, data-class restrictions, no-training or no-secondary-use state where configured, freshness, revocation, and consistency between the protected state and the exact pending Candidate Act.¶

Protected Validation Evidence commits the validated neural-state or runtime-state representation and the relevant protected-state transition before, or atomically with, availability of a scoped non-bearer capability. The capability is bound to the Candidate Act, descriptor or digest, evidence, permitted effect scope, and Finality Sink.¶

The Finality Sink may be an accelerator egress controller, model-loader gate, GPU or NPU scheduler, protected memory controller, inference-serving boundary, DPU, tool-dispatch gate, or output-release controller. If the protected neural-state proof is stale, mismatched, unverifiable, revoked, or inconsistent with the pending operation, the Candidate Act remains non-effective. This profile selectively adapts the neural-state descriptor and privacy-preserving neural-proof mechanisms described in DAS Protocols V.¶

6.77. Profile 223 — Hardware-Isolated Neural-Influence Shadow-Auditor Finality

Feature: Places an independent hardware-isolated or otherwise protected shadow-auditor beside the principal AI execution path so that consequential output or state transitions do not depend solely on the model or runtime that generated them.¶

Problem Addressed: Prevents a principal model, serving runtime, scheduler, or compromised host from both generating a consequential Candidate Act and unilaterally asserting the behavioral or influence evidence used to authorize that same act.¶

The Candidate Act may include output emission, tensor egress, tool dispatch, model-state transfer, routing or scheduling changes, adapter activation, memory reuse, or another AI-generated or AI-influenced operation that crosses a protected hardware or infrastructure boundary.¶

The shadow-auditor receives bounded observations from protected telemetry, execution metadata, influence summaries, selected activation or routing signals, runtime-behavior measurements, output classes, policy events, or other signals sufficient to evaluate configured authority predicates without requiring the auditor to become the primary model-serving path.¶

The auditor is isolated from the principal model or host to the extent required by the deployment. Its measurement inputs, code or configuration identity, policy epoch, freshness state, and observation path may be attested or otherwise protected so that the principal execution domain cannot silently rewrite the evidence used for finality.¶

Authority predicates may include behavioral-envelope conformance, unexpected model or expert influence, semantic or policy drift, extraction-budget state, tenant and destination scope, prohibited secondary use, abnormal tool or egress behavior, or correspondence between the observed runtime behavior and the declared Candidate Act.¶

The Protected Enforcement Domain commits evidence representing the independent audit result and relevant protected state before, or atomically with, capability availability. A positive result does not itself cause effectuation; it contributes to a scoped non-bearer capability bound to the exact Candidate Act and Finality Sink.¶

The Finality Sink fails closed when the auditor is unavailable where required, the observation path is stale or unverifiable, the audit result does not correspond to the pending act, or the configured envelope fails. This profile selectively adapts the hardware-isolated neural-influence shadow-auditor mechanism described in DAS Protocols V.¶

6.78. Profile 224 — Cross-Committed Collection-Time and Execution-Time Finality

Feature: Separates protected validation performed when data, state, or an input object is collected or admitted from a later independent validation of the actual pending use at execution time, with the two stages cryptographically or otherwise machine-verifiably cross-committed.¶

Problem Addressed: Prevents an earlier valid collection, ingestion, retrieval, memory admission, or data-access decision from becoming standing authority for a materially different later computation, tensor transfer, training use, inference use, export, or external effect.¶

At collection or admission time, a first protected enforcement instance may bind source or provenance, data or tensor class, permitted use scope, tenant or workload, jurisdiction or location constraints where configured, retention or no-training state, policy epoch, freshness, and a protected reference to the admitted object.¶

At execution time, a second independent protected enforcement instance evaluates the actual pending Candidate Act, including the model or workload using the object, destination, operation class, current scope, current policy epoch, protected state, and the exact Finality Sink at which the later consequence would occur.¶

The first and second stages produce mutually corroborating or cross-committed protected artifacts. The execution-time artifact references the earlier protected commitment, while the finality decision confirms that the admitted object and its original bounded scope correspond to the object actually being used in the current operation.¶

The scoped non-bearer capability becomes available only after the required corroboration succeeds. A collection-time receipt, ingestion authorization, or earlier data-access decision cannot by itself satisfy the execution-time or effectuation-time requirement.¶

The Finality Sink may be a model-loader gate, accelerator memory controller, tensor-egress gate, DPU or SmartNIC, storage or vector-memory controller, training-ingress gate, inference-output gate, DMA or RDMA boundary, or another hardware-adjacent point controlling the actual later effect.¶

If the original admitted object, current use, model, destination, policy epoch, protected state, or cross-commitment does not correspond, the later operation remains non-effective. This profile selectively adapts the two-instance collection-time and execution-time cross-committed architecture described in DAS Protocols VI.¶

6.79. Profile 225 — Attested Measurement-Path and Boundary-Observation Finality

Feature: Extends finality validation to the measurement path that observes the actual pending hardware or infrastructure operation, rather than relying only on attestation of the endpoint, accelerator, workload, or policy engine.¶

Problem Addressed: Prevents a trusted policy decision from being applied to a different physical or logical operation because the observer, telemetry source, descriptor-construction path, interconnect monitor, or boundary sensor can be bypassed, substituted, stale, or misbound.¶

The Candidate Act may be a DMA or RDMA transfer, CXL or coherent-memory transaction, tensor egress, optical-lane operation, firmware activation, queue or route update, model-output release, partition change, key release, or other operation whose exact pending form is reconstructed from boundary-local observation.¶

The machine-verifiable descriptor may bind the measurement source, measurement-path identity, observer firmware or logic measurement, protected channel or interface, source and destination components, operation digest, queue or transaction identifier, memory range, route or lane, observation time, policy epoch, freshness state, and Finality Sink.¶

The Protected Enforcement Domain validates that the observation path is authorized and sufficiently intact for the configured profile, and that the locally observed pending operation corresponds to the Candidate Act that was previously evaluated. Measurement-path attestation may be combined with protected counters, state references, challenge-response freshness, or sink-local reconstruction.¶

Protected Validation Evidence commits both the authorization result and the relevant observation or measurement-path state. The scoped non-bearer capability is therefore not only bound to an abstract requested act, but also to a trustworthy representation of what the boundary is actually about to effectuate.¶

The Finality Sink may be a transaction-admission gate, IOMMU or SMMU, DMA engine, DPU, SmartNIC, CXL controller, fabric switch, optical controller, accelerator egress gate, firmware activation controller, or protected output boundary capable of comparing the observed pending operation with the authorized descriptor.¶

Failure to establish the required measurement-path integrity, freshness, or correspondence causes denial even when the workload itself is correctly attested. This profile selectively adapts the attested measurement-path and boundary-observation mechanisms described in DAS Protocols VI.¶

6.80. Profile 226 — Distributed Partial-Share Finality Without Centralized Authority Reconstruction

Feature: Distributes load-bearing finality authority across multiple protected domains so that no single GPU, host, DPU, scheduler, service, or policy engine must reconstruct or possess complete unilateral authority for the consequential operation.¶

Problem Addressed: Reduces the risk that compromise, misconfiguration, or over-privilege of one infrastructure component can independently authorize a rack-scale, cross-tenant, cross-domain, or otherwise high-consequence act.¶

The Candidate Act may include accelerator-fabric membership, cross-rack tensor transfer, confidential workload migration, key release combined with DMA or RDMA admission, model deployment, high-value output release, cross-domain memory exposure, optical routing, or another operation for which multiple independent authority domains are required.¶

Separate protected domains evaluate distinct or overlapping predicates and create partial validation shares bound to the same Candidate Act descriptor or digest, policy epoch, freshness state, protected-state references, and designated Finality Sink. Example domains may represent workload integrity, tenant authority, data or tensor scope, infrastructure health, destination authorization, jurisdictional scope, or independent operator control.¶

The architecture need not reconstruct a master bearer credential or centralized secret. The Finality Sink may verify an aggregate proof, threshold result, conjunctive share set, or other machine-verifiable representation showing that the required partial authorities correspond to the same pending act.¶

Each share is non-transferable outside its bounded context and is invalid when replayed against another act, sink, destination, epoch, or protected-state representation. Consumption or reservation of relevant state may occur atomically with the final aggregate decision.¶

Possible Finality Sinks include DPU or SmartNIC infrastructure controllers, accelerator-fabric switches, confidential-compute admission points, key-release controllers, memory-fabric gates, optical fabric controllers, cluster schedulers, or chained hardware boundaries.¶

If the required threshold or conjunctive set is incomplete, inconsistent, stale, revoked, replayed, or bound to different Candidate Acts, finality is denied. This profile selectively adapts the distributed partial-share finality mechanism described in DAS Protocols VI.¶

6.81. Profile 227 — Separate Compute-Enable Authority and Produced-Output Finality Authority

Feature: Separates authority to initiate or perform an AI computation from authority for the particular output actually produced by that computation to become externally or systemically effective.¶

Problem Addressed: Prevents a valid workload launch, model invocation, accelerator admission, or computation-completion event from being treated as blanket authority to release whatever tokens, tensors, commands, artifacts, model state, or agentic actions the computation later produces.¶

Before computation, a Candidate Operation Descriptor may bind the request or request commitment, workload or execution identity and measurement, model or accelerator context, invocation identifier, permitted computation scope, tenant, resource scope, freshness state, and applicable computation gate.¶

A Protected Enforcement Domain evaluates pre-computation predicates, commits pre-computation evidence, and establishes a scoped non-bearer execution authority. A mandatory computation gate verifies that authority before the run proceeds. This execution authority permits computation only; it does not authorize later output release, transmission, storage, tool dispatch, signing, transaction commit, or physical actuation.¶

After computation, the actual Candidate Output remains technically non-final. A separate post-computation evaluation forms an output-specific Candidate Act Descriptor and commits output-specific Protected Validation Evidence. A distinct scoped non-bearer finality authority is then bound to the actual output or output digest, permitted effect scope, current protected state, and Finality Sink.¶

The execution-authority object and finality-authority object may use different object types, domain-separation values, cryptographic contexts, keys, state namespaces, predicates, and consumption rules so that one cannot be substituted for the other.¶

The Finality Sink verifies the output-specific authority, evidence, scope, sink identity, freshness, policy epoch, revocation, use or consumption state, and anti-replay conditions before output egress or consequence. A request-side authorization or proof of successful computation is insufficient.¶

This profile is especially relevant to AI factories in which GPU or NPU execution and network, storage, tool, or optical egress are controlled by different components. It selectively adapts the separate computation-authority and produced-output finality-authority architecture described in DAS Protocols VIII.¶

6.82. Profile 228 — Produced-Output Canonicalization and Output-Derived Digest Finality

Feature: Binds finality to a deterministic representation and digest of the actual tensor, token sequence, command, artifact, model-state object, or other output produced after computation, rather than relying only on a request digest or expected-operation identifier formed before the output existed.¶

Problem Addressed: Prevents a valid request, job identifier, prompt digest, model invocation, or expected-operation commitment from being substituted for authorization of materially different bytes, tokens, fields, tensors, commands, destinations, or fragments actually produced at runtime.¶

After production, the Candidate Output is canonicalized using a deterministic representation appropriate to the profile. Canonicalization may cover encoding, field ordering, numeric representation, binary framing, nonsemantic metadata normalization, tensor layout metadata, selected material portions, destination or effect fields, fragment manifests, or Merkle-style commitments for large or streaming objects.¶

An output-derived digest is computed from the canonicalized produced output and is kept distinct from any request identifier, request digest, prompt commitment, invocation identifier, or pre-computation authorization object.¶

The Candidate Act Descriptor may bind the output digest, canonicalization version, invocation and workload identity, model version, output class, tensor or artifact size, fragment or manifest information, destination, interface, intended effect, tenant, purpose or use scope where configured, policy epoch, protected-state reference, provenance or grounding reference, and Finality Sink.¶

Protected Validation Evidence commits the output-specific predicate result and protected-state transition. The scoped non-bearer finality authority is bound to the output-derived digest and cannot be validly applied to another output generated by the same request or model invocation.¶

Where feasible, the Finality Sink independently reconstructs the relevant Candidate Act Descriptor, canonicalizes the pending output or verifies the fragment manifest, computes or checks the local output digest, and compares it with the authorized output-specific representation before egress, storage, transmission, tool dispatch, transaction commit, or other effectuation.¶

Digest mismatch, canonicalization mismatch, fragment substitution, output mutation, stale descriptor state, or substitution of a request-side authority causes the Candidate Act to remain non-effective. This profile selectively adapts the produced-output canonicalization and output-derived digest mechanisms described in DAS Protocols VIII.¶

6.83. Profile 229 — Effectuation-Enabling Resource Withholding and Silicon Technical Non-Completability

Feature: Makes the compute substrate technically unable to complete a consequential operation by withholding at least one resource, protected operation, or hardware enablement that is necessary for effectuation and placing that dependency under Finality Sink control.¶

Problem Addressed: Addresses the bypass condition in which software policy denies an operation but the same GPU, host, runtime, driver, management processor, or privileged component still possesses the key, DMA aperture, transmit path, commit primitive, device register, or other mechanism required to complete the consequence.¶

The withheld dependency may include a cryptographic key or key-use operation, DMA or RDMA aperture, memory-window or page-table commit, network transmit enable, optical-lane or modulator enable, signing operation, transaction-commit primitive, storage-admission operation, tool or API dispatch permission, actuator enablement, protected register transition, output-buffer release, or another effectuation-enabling resource.¶

The execution substrate may compute, prepare, queue, buffer, encrypt, stage, or otherwise hold the Candidate Act, but it lacks independent access to the protected dependency required to make that act externally or systemically effective. The resulting state is technically non-completable rather than merely marked denied in software.¶

The Finality Sink controls the withheld resource and releases, applies, derives, or uses it only after verifying the Candidate Act or locally reconstructed descriptor, Protected Validation Evidence, scoped non-bearer capability, protected state, destination and effect scope, freshness, policy epoch, revocation, consumption state, and anti-replay conditions.¶

Alternate effectuation paths that would bypass the controlled dependency—including administrative, diagnostic, fallback, maintenance, unmediated network, alternate driver, alternate signing, alternate storage, debug, queue, credential, or recovery paths—must be disabled, isolated, equivalently verified, or otherwise prevented from restoring unilateral completion authority to the compute substrate.¶

Possible Finality Sinks include an IOMMU or SMMU, DPU or SmartNIC, memory or CXL controller, accelerator-egress controller, hardware queue, secure key controller, storage commit gate, firmware-controlled output latch, optical controller, network interface, transaction engine, or actuator-control boundary.¶

If the sink loses exclusive control of the required effectuation dependency, or an unverified alternate path can complete the same consequence, the profile is not satisfied. This profile selectively adapts the effectuation-enabling-resource withholding and execution-substrate technical non-completability architecture described in DAS Protocols VIII.¶