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

Deterministic and bit-exact inference

No new hardware needed

On-chip telemetry from timing, memory and performance counters and 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.

• On-chip telemetry from timing, memory and performance counters: Red-teamed
• Deterministic and bit-exact inference: Analysis

Open significant failures

2 failures in 1 mechanism

• Adversarially disguised fine-tuning partly evades classification in On-chip telemetry from timing, memory and performance counters

  Across 20 evasion strategy families in five monitor-evader rounds, the classifier's accuracy against the most challenging disguised workloads held out from training was 43–87%. White-box LoRA fine-tuning was the only evasion family detected less than half the time. The evaluation covered single nodes, PyTorch-level evasion and NVIDIA hardware. ³

  Known failure · Demonstrated attack · Significant · Open. On the record

• Counters leak information about protected workloads in On-chip telemetry from timing, memory and performance counters

  Performance counters have been used as a side channel against TEEs, for example in CounterSEVeillance. NVIDIA disables performance counters in full confidential-computing mode, stating that they could provide an avenue for side-channel attacks. Richer counters for verification therefore pull against confidentiality. ^{4 5}

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

Scope limitations

• Software-read telemetry can be forged by the operator in On-chip telemetry from timing, memory and performance counters

  NVML-based classification assumes trustworthy telemetry. Without a tamper-resistant read path, an authenticated telemetry channel and secure boot of the monitoring software, an operator who controls the full software stack could forge counter values. Monfared et al. start from the same premise: current GPUs expose little trusted telemetry and can be modified or virtualized. ^{1 3}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors: A guarantee processor on the chip would give the tamper-resistant, authenticated telemetry path the flaw says is missing. A pointer, not evidence that this failure is mitigated. Add

• Timing challenges do not identify the individual chip in On-chip telemetry from timing, memory and performance counters

  GEMM and VDF challenges can be answered by identical GPUs elsewhere, and floating-point fingerprints distinguish GPU models, not individual devices. GPU virtualization adds timing leakage that prevents attributing compute use. ¹

  Scope limitation · Theoretical argument. 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. ⁶

  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. ^{6 8}

  Scope limitation · Open question. On the record

Open questions

• No quantified error rates or formal thresholds for timing primitives in On-chip telemetry from timing, memory and performance counters

  Monfared et al. state that false-positive and false-negative rates are not quantified and leave hardware-specific formal thresholds to future work. ¹

  Open question · Open question. On the record

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

• Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors Add
  • On-chip telemetry from timing, memory and performance counters waits on it. Shipping accelerators need a tamper-resistant, authenticated telemetry path.
• Operational use TEE remote attestation for AI workloads Add
  • On-chip telemetry from timing, memory and performance counters depends on it.

Missing prerequisites

• Operational use TEE remote attestation for AI workloads needed by On-chip telemetry from timing, memory and performance counters Add

Blockers

8 blockers recorded

• On-chip telemetry from timing, memory and performance counters
  • Shipping accelerators need a tamper-resistant, authenticated telemetry path. Hardware trust. Waits on Hardware-enabled guarantees (flexHEG) and guarantee processors ^{2 3}
  • NVIDIA's full confidential-computing mode disables the hardware performance counters its profiling tools use, so telemetry that needs them conflicts with it. Privacy & leakage ^{4 5}
  • Continuous challenge puzzles cost power and throughput on production workloads. Performance & compatibility ¹
  • Evaluation has not gone beyond single nodes, framework-level evasion and one vendor's hardware. Adversarial validation ³
• 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 ^{7 9}
  • 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 ^{6 10 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 ^{6 16}

From the family or selected implementation's record. ?

Model weights

Depends on the design for On-chip telemetry from timing, memory and performance counters and Deterministic and bit-exact inference.

Inputs and outputs

Depends on the design for On-chip telemetry from timing, memory and performance counters and Deterministic and bit-exact inference.

Training data

Depends on the design for On-chip telemetry from timing, memory and performance counters.

Not involved: Deterministic and bit-exact inference.

On-chip telemetry from timing, memory and performance counters

None on the map

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. Timing and Memory Telemetry on GPUs for AI Governance, S. K. Monfared et al. (2026). Original
2. Guaranteeable Memory: An HBM-Based Chiplet for Verifiable AI Workloads, J. Petrie (2025). Original
3. Detecting Hidden ML Training With Zero-Overhead Telemetry, R. Rahman & S. Tajdari (2026). Original
4. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
5. NVIDIA Secure AI with Blackwell and Hopper GPUs (White Paper), NVIDIA (2025). Original
6. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). Original
7. Defeating Nondeterminism in LLM Inference, H. He & Thinking Machines Lab (2025). Original
8. Hawkeye: Reproducing GPU-Level Non-Determinism, E. Badash et al. (2026). Original
9. Towards Deterministic Inference in SGLang and Reproducible RL Training, The SGLang Team (2025). Original
10. Batch Invariance (vLLM documentation), vLLM project (2026). Original
11. gensyn-ai/ree: Gensyn Reproducible Execution Environment (GitHub repository), Gensyn (2026). Original
12. EigenCloud Brings Verifiable AI to Mass Market with EigenAI and EigenCompute Launches, EigenCloud (2025). Original
13. Building Delphi: Pricing, Settlement, and Agentic Trading, D. Jedamski (2026). Original
14. Reproducible Execution Environment (REE) (Gensyn documentation), Gensyn (2026). Original
15. What is Delphi? (Delphi documentation), Gensyn (2026). Original
16. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (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