Proposal Explorer

Add claims to see which ones the mechanisms address.

In production

• R3 Deterministic and bit-exact inference for reproducing open-model inference from receipts in Gensyn's information-market service

Built for an adversarial prover

Bounding unexplained information in outputs, Side-channel suppression for isolated facilities and Deterministic and bit-exact inference

No new hardware needed

Deterministic and bit-exact inference

Published attempts to break a system, including those that found failures. Testing history does not show that open flaws are resolved.

• Bounding unexplained information in outputs: Independent red-team
• Side-channel suppression for isolated facilities: Analysis
• Deterministic and bit-exact inference: Analysis

Open significant flaws

8 flaws in 3 mechanisms

• Prompt-controlled entropy inflation widens the covert channel in Bounding unexplained information in outputs

  Gumbel-based inference verification tolerates token choices that honest GPU nondeterminism could produce, and the size of that tolerated set grows with the model's output entropy. Kezins, an independent researcher, showed that an adversary who controls the prompt distribution can raise output entropy and roughly double the bits leaked per token. Across six models of 1 to 32 billion parameters, this cut the slowdown from 146–254 times under benign prompts to 60–118 times. Kezins argues that architectures built on the same unexplained-information bound inherit this attack surface, and recommends calibrating tolerances against local token entropy rather than benign traffic. ^{2 3}

  Demonstrated attack · Significant · Open. On the record

  Related mechanism R3 Deterministic and bit-exact inference: Bit-exact replay would remove the tolerance for numerical noise that sets the size of this channel. The record notes that it needs full hardware and software metadata. A pointer, not evidence that this flaw is mitigated. In the proposal.

• Information the declared computation explains is not bounded in Bounding unexplained information in outputs

  The bound limits unexplained bits only. Outputs that the declared computation fully explains can still carry valuable information: a compression study notes that an adversary with inference access can extract more proprietary information per bit than naive transmission allows. ^{1 4}

  Theoretical argument · Significant · Open. On the record

• Channels other than checked outputs are outside the bound in Bounding unexplained information in outputs

  The inference-verification scheme treats side channels as out of scope. A low-trust system design argues that suppressing physical covert bandwidth below kilobits per second is much more achievable than aiming for zero, and that a malicious device can leak one bit of information by deliberately outputting a wrong result. ^{2 5}

  Theoretical argument · Significant · Open. On the record

  Related mechanism R1 Side-channel suppression for isolated facilities: Physical side channels need separate suppression, which is this mechanism's purpose. A pointer, not evidence that this flaw is mitigated. In the proposal.

• The facility-level design is untested in Bounding unexplained information in outputs

  The compute-verification architecture is described with protocol details, potential attacks and prototyping plans, but no prototype results have been published. ¹

  Open question · Significant · Open. On the record

• Supply-chain implants may evade inspection in Side-channel suppression for isolated facilities

  Cankaya identifies malicious hardware embedded deep in purchased components as a residual risk that visual inspection and disassembly may not catch. He notes that radiographic examination under high-security standards could mitigate it. ⁶

  Theoretical argument · Significant · Open. On the record

• Openings for airflow, power and optics weaken shielding in Side-channel suppression for isolated facilities

  Cankaya notes that keeping attenuation high while passing high-power airflow, cabling and optical links adds complexity beyond existing shielded-enclosure specifications. ⁶

  Theoretical argument · Significant · Open. On the record

• Inspection assumptions may not hold in Side-channel suppression for isolated facilities

  The design's statistical argument assumes that visual or disassembly inspection catches every flaw that is present in a sampled unit. Cankaya is unsure whether destructive teardowns are defence-dominant or offence-dominant. ⁶

  Open question · Significant · Open. 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. ^{7 9}

  Open question · Significant · Open. On the record

Open minor flaws

1 mechanism with minor findings

• 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. ⁷

  Open question · Minor · Open. On the record

Not yet demonstrated

R1 Side-channel suppression for isolated facilities

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

• R2 Bandwidth limits and compartmentalization Add
  • Bounding unexplained information in outputs waits on it. The prover's compute must be isolated so that all traffic passes through the verifier's interlock; any unmonitored path voids the bound.
• R3 Sampled inference recomputation Add
  • Bounding unexplained information in outputs depends on it.
• R1 Network taps and certifiers Add
  • Bounding unexplained information in outputs depends on it.

Missing prerequisites

• R3 Sampled inference recomputation needed by Bounding unexplained information in outputs Add
• R2 Bandwidth limits and compartmentalization needed by Bounding unexplained information in outputs Add
• R1 Network taps and certifiers needed by Bounding unexplained information in outputs Add

Blockers

11 blockers recorded

• Bounding unexplained information in outputs
  • The prover's compute must be isolated so that all traffic passes through the verifier's interlock; any unmonitored path voids the bound. Coverage & hidden compute. Waits on Bandwidth limits and compartmentalization ¹
  • Physical side channels need separate suppression, and one design treats a low residual bandwidth, rather than zero, as the realistic target. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ^{2 5}
  • Tolerance for numerical nondeterminism sets the size of the residual channel; bit-exact replay would remove it but needs full hardware and software metadata. Protocol soundness. Waits on Deterministic and bit-exact inference ^{3 5}
  • Recomputation over confidential weights and inputs needs a protected setting: prover recomputation in a verifier-controlled enclosure, verifier recomputation in a prover-controlled enclosure, or zero-knowledge proofs. Privacy & leakage ¹
  • No prototype of the facility-level architecture exists to red-team. Adversarial validation ¹
• Side-channel suppression for isolated facilities
  • No prototype or red-team exists; the design is a first-pass viability study. Adversarial validation ⁶
  • Volume costs of TEMPEST-grade power-line filters are uncertain, because existing products are mostly made to order. Performance & compatibility ⁶
• 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 ^{8 10}
  • 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 ^{7 11 17}
  • Amodo's status page for the AI 2040 verification plan rates a reproducible inference stack for that plan as 'not started'. Performance & compatibility ¹⁸
  • Exact replay requires the prover to disclose weights, software versions, parallelism and batch sizes to whoever recomputes. Privacy & leakage ^{5 7}

From the family or selected implementation's record. ?

Model weights

Depends on the design for Bounding unexplained information in outputs and Deterministic and bit-exact inference.

Not involved: Side-channel suppression for isolated facilities.

Inputs and outputs

Depends on the design for Bounding unexplained information in outputs and Deterministic and bit-exact inference.

Not involved: Side-channel suppression for isolated facilities.

Training data

Not involved: Bounding unexplained information in outputs, Side-channel suppression for isolated facilities and Deterministic and bit-exact inference.

Bounding unexplained information in outputs

None on the map

Side-channel suppression for isolated facilities

• R1 AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• R1 RAND secure inference data center (SIDC) design Proposed architecture, RAND

Deterministic and bit-exact inference

• R2 Batch-invariant inference kernels (Thinking Machines) Open-source project, Thinking Machines Lab
• R3 Verde and RepOps (Gensyn) Product, Gensyn
• R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute

1. Verifying AI Compute by Bounding Unexplained Information Exfiltration, J. Petrie & Y. Mühlhäuser (2026). Original
2. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
3. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
4. Haiku to Opus in Just 10 bits: LLMs Unlock Large Compression Gains, R. Rinberg et al. (2026). Original
5. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
6. Suppressing Side Channels in an Untrusted Data Center via Retrofitted Defenses, N. Cankaya (2026). Original
7. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
8. Defeating Nondeterminism in LLM Inference, H. He & Thinking Machines Lab (2025). Original
9. Hawkeye: Reproducing GPU-Level Non-Determinism, E. Badash et al. (2026). Original
10. Towards Deterministic Inference in SGLang and Reproducible RL Training, The SGLang Team (2025). Original
11. Batch Invariance (vLLM documentation), vLLM project (2026). Original
12. gensyn-ai/ree: Gensyn Reproducible Execution Environment (GitHub repository), Gensyn (2026). Original
13. EigenCloud Brings Verifiable AI to Mass Market with EigenAI and EigenCompute Launches, EigenCloud (2025). Original
14. Building Delphi: Pricing, Settlement, and Agentic Trading, D. Jedamski (2026). Original
15. Reproducible Execution Environment (REE) (Gensyn documentation), Gensyn (2026). Original
16. What is Delphi? (Delphi documentation), Gensyn (2026). Original
17. [Feature]: Batch Invariant Feature and Performance Optimization (vLLM issue #27433), vLLM project contributors (2025). Original
18. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original