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

Memory wiping and proofs of secure erasure and Deterministic and bit-exact inference

No new hardware needed

Memory wiping and proofs of secure erasure and 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.

• Memory wiping and proofs of secure erasure: Analysis
• Deterministic and bit-exact inference: Analysis

Open significant flaws

3 flaws in 2 mechanisms

• Memory the wipe cannot reach in Memory wiping and proofs of secure erasure

  Amodo's inventory of a GB200 system lists many memory stores beyond GPU HBM and host DRAM. It notes that SSD controller DRAM sits on a private bus that host commands cannot read or write, and that its optimized algorithm leaves 25 GiB of HBM unattested. It also asks how switch memory could be wiped. ^{5 6}

  Open question · Significant · Open. On the record

• Outside help during challenges in Memory wiping and proofs of secure erasure

  Classic proofs of secure erasure assume the device is isolated during the protocol. Bursuc et al. relax this to a bound on how close a helper can be, enforced by round-trip times. In data centres, remote memory access has round trips of about 1–2 µs, against about 70–200 ns for local DRAM. The MIRI overview therefore says verification depends on ruling out RDMA by latency or physical disconnection. ^{3 4}

  Theoretical argument · Significant · Open. On the record

  Related mechanism R2 Timed challenge-response and memory-occupation challenges: Timed challenges bound how far away a helper can be by how quickly it must answer. A pointer, not evidence that this flaw is mitigated. Add

  Related mechanism R2 Bandwidth limits and compartmentalization: Removing or capping links between groups of accelerators limits remote memory access during a challenge. A pointer, not evidence that this flaw is mitigated. Add

• 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. ^{9 11}

  Open question · Significant · Open. On the record

Open minor flaws

2 mechanisms with minor findings

• Gap between erased and total memory in Memory wiping and proofs of secure erasure

  Bursuc et al. note that memory left between the erased region and the device's full memory could hold data, and that their bounds are tighter only against a restricted adversary. ³

  Theoretical argument · Minor · Open. 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. ⁹

  Open question · Minor · Open. On the record

Not yet demonstrated

R1 Memory wiping and proofs of secure erasure

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

• R2 Bandwidth limits and compartmentalization Add
  • Bears on the open significant flaw “Outside help during challenges” in Memory wiping and proofs of secure erasure. Removing or capping links between groups of accelerators limits remote memory access during a challenge.
• R2 Timed challenge-response and memory-occupation challenges Add
  • Bears on the open significant flaw “Outside help during challenges” in Memory wiping and proofs of secure erasure. Timed challenges bound how far away a helper can be by how quickly it must answer.
  • Memory wiping and proofs of secure erasure waits on it. Timed challenges must exclude remote memory and other helpers.

Missing prerequisites

• R2 Timed challenge-response and memory-occupation challenges needed by Memory wiping and proofs of secure erasure Add

Blockers

7 blockers recorded

• Memory wiping and proofs of secure erasure
  • Wipes take time: tens of minutes for a pod's volatile memory and hours for SSDs, displacing work. Performance & compatibility ^{4 5 6}
  • Timed challenges must exclude remote memory and other helpers. Coverage & hidden compute. Waits on Timed challenge-response and memory-occupation challenges ^{3 4}
  • All memory stores in a system must be inventoried and wiped at the same time. Coverage & hidden compute ⁵
• 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 ^{10 12}
  • 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 ^{9 13 19}
  • Amodo's status page for the AI 2040 verification plan rates a reproducible inference stack for that plan as 'not started'. Performance & compatibility ²⁰
  • Exact replay requires the prover to disclose weights, software versions, parallelism and batch sizes to whoever recomputes. Privacy & leakage ^{4 9}

From the family or selected implementation's record. ?

Model weights

Depends on the design for Deterministic and bit-exact inference.

Not involved: Memory wiping and proofs of secure erasure.

Inputs and outputs

Depends on the design for Deterministic and bit-exact inference.

Not involved: Memory wiping and proofs of secure erasure.

Training data

Not involved: Memory wiping and proofs of secure erasure and Deterministic and bit-exact inference.

Memory wiping and proofs of secure erasure

• 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

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. Verification Plan, R. Dean (2026). Original
2. Secure Code Update for Embedded Devices via Proofs of Secure Erasure, D. Perito & G. Tsudik (2010). Original
3. Software-Based Memory Erasure with Relaxed Isolation Requirements, S. Bursuc et al. (2024). Original
4. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
5. Memory Wipes - Performance Analysis, Amodo Design (2026). Original
6. Improving Disk Wiping Speed for Memory Wipes, Amodo Design (2026). Original
7. Amodo-Design/PoSE-Memory-Wiping (GitHub repository), Amodo Design (2026). Original
8. Empirical Evaluation of Memory-Erasure Protocols, R. Gil-Pons et al. (2025). Original
9. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
10. Defeating Nondeterminism in LLM Inference, H. He & Thinking Machines Lab (2025). Original
11. Hawkeye: Reproducing GPU-Level Non-Determinism, E. Badash et al. (2026). Original
12. Towards Deterministic Inference in SGLang and Reproducible RL Training, The SGLang Team (2025). Original
13. Batch Invariance (vLLM documentation), vLLM project (2026). Original
14. gensyn-ai/ree: Gensyn Reproducible Execution Environment (GitHub repository), Gensyn (2026). Original
15. EigenCloud Brings Verifiable AI to Mass Market with EigenAI and EigenCompute Launches, EigenCloud (2025). Original
16. Building Delphi: Pricing, Settlement, and Agentic Trading, D. Jedamski (2026). Original
17. Reproducible Execution Environment (REE) (Gensyn documentation), Gensyn (2026). Original
18. What is Delphi? (Delphi documentation), Gensyn (2026). Original
19. [Feature]: Batch Invariant Feature and Performance Optimization (vLLM issue #27433), vLLM project contributors (2025). Original
20. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original