Proposal Explorer

Add claims to see which ones the mechanisms address.

Built for an adversarial prover

Whole-workload recomputation (reproducible packets), Timed challenge-response and memory-occupation challenges and Bandwidth limits and compartmentalization

No new hardware needed

Timed challenge-response and memory-occupation challenges

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.

• Timed challenge-response and memory-occupation challenges: Analysis
• Bandwidth limits and compartmentalization: Analysis

Open significant failures

3 failures in 2 mechanisms

• Remote memory narrows the timing margin in Timed challenge-response and memory-occupation challenges

  Data-centre remote memory access returns in about 1–2 µs, against about 70–200 ns for local DRAM. The MIRI overview says verification of memory saturation depends on ruling out remote access by latency or physical disconnection. It names pre-staging data into local memory as the remaining evasion and proposes an unpredictable, capacity-filling challenge to close it. ⁷

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

  Related mechanism Research demo Bandwidth limits and compartmentalization: Physical disconnection is proposed to exclude remote memory between the separated groups during a challenge. It depends on the isolation boundary being enforced. A pointer, not evidence that this failure is mitigated. In the proposal.

• 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. ¹⁶

  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. ¹⁸

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

Scope limitations

• 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 2}

  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

• 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. ¹⁶

  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. ¹⁶

  Scope limitation · Open question. On the record

Open questions

• 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

• Error rates not quantified in Timed challenge-response and memory-occupation challenges

  Monfared et al. show separable timing distributions. Their acceptance rule passes a GPU when its mean time per round stays at or below a chosen maximum, and an appendix outlines statistical tests for the proof-of-work puzzle. They leave hardware-specific threshold values to future work and report no false-positive or false-negative rates. ⁸

  Open question · Open question. On the record

• 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. ^{16 19 20}

  Open question · Theoretical argument. On the record

Not yet demonstrated

Proposed Whole-workload recomputation (reproducible packets)

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
  • 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.
  • Bandwidth limits and compartmentalization waits on it. The verifier must know that all traffic leaving a pod crosses the capped, monitored links.

Missing prerequisites

• 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
• Research demo Tamper evidence for verifier devices needed by Bandwidth limits and compartmentalization Add

Shared foundations

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

Blockers

11 blockers recorded

• 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 ⁴
  • 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 4}
  • 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 ²
• Timed challenge-response and memory-occupation challenges
  • No network-level memory challenge across data-centre servers has been demonstrated. Adversarial validation ⁷
  • Challenges that fill memory displace workloads; filling a pod's volatile memory takes tens of minutes and SSDs take hours. Performance & compatibility ^{7 8}
  • Outside help, such as remote memory, must be excluded during challenges. Coverage & hidden compute. Waits on Bandwidth limits and compartmentalization ⁷
• Bandwidth limits and compartmentalization
  • No cap that a verifier can check has been implemented or red-teamed. Adversarial validation ¹⁶
  • The verifier must know that all traffic leaving a pod crosses the capped, monitored links. Coverage & hidden compute. Waits on Network taps and certifiers ⁷
  • 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 ^{16 18}
  • Advances in low-communication training could shrink the margin that the cap enforces. Capacity bounds ^{16 19 20}

From the family or selected implementation's record. ?

Model weights

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

Not involved: Timed challenge-response and memory-occupation challenges and Bandwidth limits and compartmentalization.

Inputs and outputs

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

Not involved: Timed challenge-response and memory-occupation challenges and Bandwidth limits and compartmentalization.

Training data

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

Not involved: Timed challenge-response and memory-occupation challenges and Bandwidth limits and compartmentalization.

Whole-workload recomputation (reproducible packets)

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

Timed challenge-response and memory-occupation challenges

• Proposed Data-centre memory challenging Proposed architecture, Machine Intelligence Research Institute
• Research demo GPU contention probes Research prototype
• Proposed Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• Research demo SAGE Research prototype
• Research demo VRAM-residency challenge Research prototype

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

1. Verification Plan, R. Dean (2026). Original
2. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
3. Scaling Recomputation Inference Verification, Amodo Design (2026). Original
4. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
5. Get Involved in Verification, AI Futures Project (2026). Original
6. Proof-of-Learning is Currently More Broken Than You Think, C. Fang et al. (2023). Original
7. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
8. Timing and Memory Telemetry on GPUs for AI Governance, S. K. Monfared et al. (2026). Original
9. SAGE: Software-based Attestation for GPU Execution, A. Ivanov et al. (2023). Original
10. SWATT: SoftWare-based ATTestation for Embedded Devices, A. Seshadri et al. (2004). Original
11. Proofs of Space, S. Dziembowski et al. (2015). Original
12. Secure Code Update for Embedded Devices via Proofs of Secure Erasure, D. Perito & G. Tsudik (2010). Original
13. Software-Based Memory Erasure with Relaxed Isolation Requirements, S. Bursuc et al. (2024). Original
14. On the Difficulty of Software-Based Attestation of Embedded Devices, C. Castelluccia et al. (2009). Original
15. Refutation of "On the Difficulty of Software-Based Attestation of Embedded Devices", A. Perrig & L. van Doorn (2010). Original
16. Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
17. De-risking Interconnect Limits for AI Verification, A. Scher et al. (2026). Original
18. The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
19. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
20. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original