Proposal Explorer

Add claims to see which ones the mechanisms address.

Operational use

• Operational use Deterministic and bit-exact inference for reproducing open-model inference from receipts in Gensyn's information-market service

Built for an adversarial prover

Whole-workload recomputation (reproducible packets), Bandwidth limits and compartmentalization and Deterministic and bit-exact inference

No new hardware needed

Deterministic and bit-exact inference

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
• Deterministic and bit-exact inference: Analysis

Open significant failures

2 failures in 1 mechanism

• 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. ⁷

  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. ¹⁰

  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. ⁷

  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. ⁷

  Scope limitation · Open question. On the record

• Some kernels remain genuinely nondeterministic in Deterministic and bit-exact inference

  The bit-exact work separates kernels that are deterministic but not batch-invariant from truly nondeterministic ones that use atomic functions. Some integer de-quantization kernels use atomic additions and remain nondeterministic, so exact replay needs backends that avoid them. ¹³

  Scope limitation · Open question. On the record

• Cross-hardware replay relies on reverse-engineered, closed behaviour in Deterministic and bit-exact inference

  Emulating one GPU's rounding on another requires reverse-engineering tensor-core arithmetic and modelling proprietary kernel choices. Hawkeye covers a subset of NVIDIA architectures and states that attention and other higher-level operations need further reverse engineering. For the bit-exact emulator, a proprietary Hopper kernel family is an open edge case. ^{13 15}

  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

• 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. ^{7 11 12}

  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. ?

• 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

• 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

12 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
• Bandwidth limits and compartmentalization
  • No cap that a verifier can check has been implemented or red-teamed. Adversarial validation ⁷
  • The verifier must know that all traffic leaving a pod crosses the capped, monitored links. Coverage & hidden compute. Waits on Network taps and certifiers ⁸
  • 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 ^{7 10}
  • Advances in low-communication training could shrink the margin that the cap enforces. Capacity bounds ^{7 11 12}
• Deterministic and bit-exact inference
  • Batch-invariant kernels cost throughput: in Thinking Machines' Qwen3-8B test, an improved deterministic build took 42 s against 26 s for vLLM's default, and SGLang reports an average 34.35% slowdown on its FlashInfer and FlashAttention 3 backends. Performance & compatibility ^{14 16}
  • Coverage is incomplete: the bit-exact emulator targets dense blocks on NVIDIA GPUs and excludes mixture-of-experts inference and training, and vLLM's batch-invariant mode is in beta, with open work on AMD hardware and speculative decoding. Performance & compatibility ^{13 17 23}
  • Amodo's status page for the AI 2040 verification plan rates a reproducible inference stack for that plan as 'not started'. Performance & compatibility ⁴
  • Exact replay requires the prover to disclose weights, software versions, parallelism and batch sizes to whoever recomputes. Privacy & leakage ^{8 13}

From the family or selected implementation's record. ?

Model weights

Depends on the design for Whole-workload recomputation (reproducible packets) and Deterministic and bit-exact inference.

Not involved: Bandwidth limits and compartmentalization.

Inputs and outputs

Depends on the design for Whole-workload recomputation (reproducible packets) and Deterministic and bit-exact inference.

Not involved: Bandwidth limits and compartmentalization.

Training data

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

Not involved: Bandwidth limits and compartmentalization and Deterministic and bit-exact inference.

Whole-workload recomputation (reproducible packets)

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

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

Deterministic and bit-exact inference

• Research demo Batch-invariant inference kernels (Thinking Machines) Open-source project, Thinking Machines Lab
• Operational use Verde and RepOps (Gensyn) Product, Gensyn
• Proposed Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute

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. Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
8. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
9. De-risking Interconnect Limits for AI Verification, A. Scher et al. (2026). Original
10. The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
11. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
12. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original
13. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
14. Defeating Nondeterminism in LLM Inference, H. He & Thinking Machines Lab (2025). Original
15. Hawkeye: Reproducing GPU-Level Non-Determinism, E. Badash et al. (2026). Original
16. Towards Deterministic Inference in SGLang and Reproducible RL Training, The SGLang Team (2025). Original
17. Batch Invariance (vLLM documentation), vLLM project (2026). Original
18. gensyn-ai/ree: Gensyn Reproducible Execution Environment (GitHub repository), Gensyn (2026). Original
19. EigenCloud Brings Verifiable AI to Mass Market with EigenAI and EigenCompute Launches, EigenCloud (2025). Original
20. Building Delphi: Pricing, Settlement, and Agentic Trading, D. Jedamski (2026). Original
21. Reproducible Execution Environment (REE) (Gensyn documentation), Gensyn (2026). Original
22. What is Delphi? (Delphi documentation), Gensyn (2026). Original
23. [Feature]: Batch Invariant Feature and Performance Optimization (vLLM issue #27433), vLLM project contributors (2025). Original