Proposal Explorer

Add claims to see which ones the mechanisms address.

Built for an adversarial prover

Bandwidth limits and compartmentalization and Whole-workload recomputation (reproducible packets)

No new hardware needed

Chip registries and manufacturing records

Attack testing records published testing for this use. It does not by itself show independent review, a formal proof or that a deployed system is secure.

• Bandwidth limits and compartmentalization: Analysis
• Chip registries and manufacturing records: Analysis

Open significant failures

3 failures in 2 mechanisms

• Operator control of pod routing collapses the bound in Bandwidth limits and compartmentalization

  Lucid's analysis finds that if the operator can freely assign pods to routers, it could dedicate a whole cell of 100 or more pods to one pipeline stage. The bound then falls to about 90–220x uncompressed and as low as about 25x with compression. The proposed mitigation, auditor-controlled random assignment that is periodically re-randomized, has not been implemented. ²

  Known failure · Theoretical argument · Significant · Open. On the record

• Parallel scale-up switches are hard enforcement points in Bandwidth limits and compartmentalization

  In GB200 topologies, GPUs reach GPUs in other nodes through NVSwitches without a NIC on the path. Amodo notes that limits are hard to enforce there because many switches work in parallel, so compromising one or two would bypass the limit. ⁵

  Known failure · Theoretical argument · Significant · Open. On the record

• Documents and serial numbers can be forged in Chip registries and manufacturing records

  Avellar and Grunewald note that export documents can be forged, that companies can hide information behind obscure corporate structures, and that it may be possible to forge serial numbers on chips and racks. They recommend cryptographic attestation of a powered-on chip as an extra check. ¹⁴

  Known failure · Theoretical argument · Significant · Open. On the record

Scope limitations

• Undeclared local storage raises per-pod capacity in Bandwidth limits and compartmentalization

  More memory or storage per pod helps an adversary. Lucid requires per-pod storage to be declared, capped and physically inspected. ²

  Scope limitation · Theoretical argument. On the record

• Training within one pod is not covered in Bandwidth limits and compartmentalization

  Lucid's bounds concern pre-training models larger than the pods are sized for. Training models that fit in one pod, fine-tuning and reinforcement-learning post-training within one pod are outside the modelled threat. ²

  Scope limitation · Open question. On the record

• Spare compute is outside the scheme in Whole-workload recomputation (reproducible packets)

  The plan states that it does not verify that spare compute is not used for unapproved workloads, because this seems very challenging. Recomputation checks the correctness of declared work, not its completeness. ^{1 8}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Proofs of useful work for capacity accounting: Proposed as one input to accounting for spare capacity on declared hardware. A pointer, not evidence that this failure is mitigated. Add

• Records cover only chips that were recorded in Chip registries and manufacturing records

  A registry or commitment accounts only for chips entered into it. Cankaya asks how a verifier would know it had found all chips, or how much "dark compute" remains, and notes that a fraudulent original record would mean unregistered chips had been made in advance. Halstead and Larsen propose reconstructing earlier production by auditing upstream suppliers. ^{15 17}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Remote detection of data centres: Looks for large data centres that were never declared, which a registry cannot show. A pointer, not evidence that this failure is mitigated. Add

• Insiders could alter records before they are fixed in Chip registries and manufacturing records

  Cankaya argues that insiders who can photograph process secrets could also tamper with production records. A commitment makes changes after publication detectable, but it cannot show that the records were accurate when committed. ¹⁵

  Scope limitation · Theoretical argument. On the record

Open questions

• Low-communication training reduces the bandwidth training needs in Bandwidth limits and compartmentalization

  DiLoCo matched fully synchronous training on 8 workers while communicating 500 times less. Rahman writes that this family of methods theoretically allows large-scale training with less than 100 Mbps. Lucid includes these methods in its bounds, but notes that extreme activation compression, architectures with unusually small inter-layer widths, or modular paradigms could erode the margin. ^{2 6 7}

  Open question · Theoretical argument. On the record

• Non-compliant work could be encoded inside compliant-looking packets in Whole-workload recomputation (reproducible packets)

  The plan notes that an AI company might try to encode a non-compliant workload inside a workload that looks compliant on the surface. ¹

  Open question · Theoretical argument. On the record

Not yet demonstrated

Proposed Whole-workload recomputation (reproducible packets) and Proposed Chip registries and manufacturing records

Mechanisms on the map that are not in the proposal. Pointers, not recommendations. ?

• Operational use Deterministic and bit-exact inference Add
  • Whole-workload recomputation (reproducible packets) waits on it. Workloads are not reproducible by default, and achieving reproducibility may cost performance.
• Research demo Tamper evidence for verifier devices Add
  • Bandwidth limits and compartmentalization waits on it. Shaping devices and routing assignments must be trusted by both parties; Amodo has not yet fully analysed resilience to a compromised DPU.
• Proposed Network taps and certifiers Add
  • Bandwidth limits and compartmentalization waits on it. The verifier must know that all traffic leaving a pod crosses the capped, monitored links.
  • Whole-workload recomputation (reproducible packets) waits on it. All traffic must reach the recomputation server via network taps, and the server's integrity is critical.

Missing prerequisites

• Research demo Tamper evidence for verifier devices needed by Bandwidth limits and compartmentalization Add
• Operational use Deterministic and bit-exact inference needed by Whole-workload recomputation (reproducible packets) Add
• Proposed Network taps and certifiers needed by Whole-workload recomputation (reproducible packets) Add

Shared foundations

• Network taps and certifiers relied on by Bandwidth limits and compartmentalization and Whole-workload recomputation (reproducible packets)

Blockers

11 blockers recorded

• Bandwidth limits and compartmentalization
  • No cap that a verifier can check has been implemented or red-teamed. Adversarial validation ²
  • The verifier must know that all traffic leaving a pod crosses the capped, monitored links. Coverage & hidden compute. Waits on Network taps and certifiers ³
  • Shaping devices and routing assignments must be trusted by both parties; Amodo has not yet fully analysed resilience to a compromised DPU. Hardware trust. Waits on Tamper evidence for verifier devices ^{2 5}
  • Advances in low-communication training could shrink the margin that the cap enforces. Capacity bounds ^{2 6 7}
• Whole-workload recomputation (reproducible packets)
  • Workloads are not reproducible by default, and achieving reproducibility may cost performance. Performance & compatibility. Waits on Deterministic and bit-exact inference ¹
  • Network packets are not individually reproducible by default; making them so may need considerable software, firmware and hardware work. Amodo rates this 'not on track'. Performance & compatibility ¹⁰
  • All traffic must reach the recomputation server via network taps, and the server's integrity is critical. Hardware trust. Waits on Network taps and certifiers ^{1 10}
  • Recomputing training steps needs checkpoints: writing one at every step would cost more than 100% overhead, so Amodo's design needs a spare data-parallel replica that tracks the weights instead. Performance & compatibility ⁸
• Chip registries and manufacturing records
  • No implementation of an AI chip registry has been publicly reported, and covering re-exports would need cooperation from re-exporters and foreign governments that may not be feasible everywhere. Access & governance ^{13 14}
  • Linking records to physical chips needs hard-to-spoof unique IDs and inspections. Hardware trust ^{13 14 15}
  • Chips produced before a registry starts must be reconstructed from supplier records. Coverage & hidden compute ^{15 17}

From the family or selected implementation's record. ?

Model weights

Depends on the design for Whole-workload recomputation (reproducible packets).

Not involved: Bandwidth limits and compartmentalization and Chip registries and manufacturing records.

Inputs and outputs

Depends on the design for Whole-workload recomputation (reproducible packets).

Not involved: Bandwidth limits and compartmentalization and Chip registries and manufacturing records.

Training data

Depends on the design for Whole-workload recomputation (reproducible packets).

Not involved: Bandwidth limits and compartmentalization and Chip registries and manufacturing records.

Bandwidth limits and compartmentalization

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• Proposed RAND secure inference data center (SIDC) design Proposed architecture, RAND

Whole-workload recomputation (reproducible packets)

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project

Chip registries and manufacturing records

None on the map

1. Verification Plan, R. Dean (2026). Original
2. Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
3. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
4. De-risking Interconnect Limits for AI Verification, A. Scher et al. (2026). Original
5. The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
6. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
7. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original
8. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
9. Scaling Recomputation Inference Verification, Amodo Design (2026). Original
10. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
11. Get Involved in Verification, AI Futures Project (2026). Original
12. Proof-of-Learning is Currently More Broken Than You Think, C. Fang et al. (2023). Original
13. Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment, M. Baker et al. (2025). Original
14. Near-Term Verification Methods for AI Chip Exports, B. Avellar & E. Grunewald (2026). Original
15. TSMC most definitely has a golden record of all AI chips it made, N. Cankaya (2025). Original
16. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (2026). Original
17. Covert AI Projects, B. Halstead & T. Larsen (2026). Original