PMPP-Hard Agent Evaluation Traces

PMPP-Hard is a 69-task agentic GPU-kernel evaluation for testing whether autonomous coding agents can produce implementations that are both correct and performant. This dataset contains the complete nine-model campaign used in the PMPP-Hard release: 621 rollouts, with 69 task sessions for each model configuration.

Maintained and released by Sinatras.

• Source repository: SinatrasC/pmpp-hard
• Prime environment and evaluations: PMPP-Hard on Prime Env Hub
• Hugging Face dataset: sinatras/pmpp-hard

Dataset contents

Each model is exposed as a separate Hugging Face configuration with one eval split.

Configuration	Sessions
claude-opus-4.8	69
deepseek-v4-pro	69
glm-5.2	69
gpt-5.5	69
gpt-5.6-luna	69
gpt-5.6-sol-xhigh	69
gpt-5.6-terra	69
kimi-k3	69
nemotron-ultra	69
Total	621

There is no training split. These are evaluation records.

Each JSONL row represents one model-task session and includes the task contract, conversation nodes, system and user context, agent messages, tool calls and outputs, the submitted kernel, verification and scorer evidence, timing information, completion state, and normalized errors when applicable.

Why complete context is retained

System instructions, agent transcripts, and materials intentionally visible in the agent sandbox are part of the experimental record. They are retained because removing them would make it difficult to reconstruct what the model could see, which tools it used, how it changed the submission, and whether the published score follows from the rollout.

Agent-visible .grader files in a trace are sanity or workspace materials available during that rollout. They must not be confused with private clean-scorer state. PMPP-Hard uses a separate scorer sandbox, and only the declared submitted source crosses that boundary.

Intended uses

Appropriate uses include:

• reproducing the PMPP-Hard campaign;
• auditing individual passes and failures;
• studying agent tool use, compiler recovery, iteration, and optimization behavior;
• comparing solve sets and kernel-performance behavior across models;
• researching clean-room evaluation and defenses against reward hacking;
• developing trace viewers, analysis tools, and evaluation infrastructure.

Benchmark integrity protocol

PMPP-Hard publishes material so researchers can inspect, challenge, rebuild, and reproduce the environment. Publication does not mean that reference solutions, graders, or authoritative tests should be shown to the evaluated model.

To preserve meaningful evaluation results:

1. Do not train on evaluator secrets. Do not use PMPP-Hard reference solutions, graders, authoritative tests, hidden task variants, or close derivations of those materials for model training, fine-tuning, distillation, preference optimization, retrieval training, or synthetic-data generation.
2. Keep hidden material out of model context. Do not place reference solutions, graders, authoritative or hidden tests, expected outputs, scorer internals, or derived answer keys in prompts, retrieval stores, tool-accessible files, caches, memories, or any other context available to the evaluated agent.
3. Preserve the two-sandbox boundary. The agent workspace should contain the complete contract, editable submission stub, approved build helpers, the compiler and GPU tools, and only explicitly non-authoritative sanity feedback. Compile and score the submitted source in a fresh scorer sandbox containing the authoritative material.
4. Transfer source only. Do not transfer modified helpers, local test artifacts, cached binaries, fabricated logs, or agent-workspace state into scoring.
5. Control network access. The cleanest post-release configuration disables outbound networking. If networking is required, block direct and mirrored access to the source repository, Prime Env Hub release, this Hugging Face dataset, and evaluator-controlled caches. Record the network policy used for every reported evaluation.
6. Disclose possible contamination. If a model may have been trained on, retrieved, or otherwise exposed to PMPP-Hard tasks, traces, references, graders, or tests, disclose that fact. Do not present the resulting score as directly comparable to the original clean campaign without a contamination qualification.

These instructions are a benchmark-integrity request, not additional restrictions on the MIT License. They describe the conditions required for a result to remain scientifically comparable to the clean PMPP-Hard evaluation.

License and third-party material

The dataset assembly, metadata, and materials owned by the author are released under the MIT License, with copyright attributed to Sinatras.

The MIT grant applies only to rights the licensor can grant. Model-generated outputs, provider system instructions, software excerpts, trademarks, and other third-party material may remain subject to their respective owners' terms. This release does not imply endorsement by any model provider or infrastructure provider. Users are responsible for checking any additional terms that apply to their intended redistribution or use.

Limitations

• The traces reflect specific model endpoints, harnesses, tool versions, rollout budgets, scorer images, and NVIDIA Blackwell hardware used by the campaign.
• Matching the reported timings requires equivalent sm_120 hardware and the published runtime images.
• Public release creates a contamination risk for future model evaluations. Runtime isolation can keep evaluator materials out of an agent's workspace, but it cannot prove that a model was never trained on public PMPP-Hard material.
• System prompts and agent-visible evaluator materials are intentionally included for auditability. Researchers should handle them as evaluation records rather than generic instruction data.
• A source-similarity audit found no distinctive private test or central-scorer fragments. One submitted Triton kernel contains a standard grouped program-ID and tile-offset block also present in the reference implementation; the rollout did not access the private reference source.

Citation

@misc{sinatras2026pmpphard,
  author = {Sinatras},
  title = {PMPP-Hard: Building GPU Kernel Environments for the Agent Era},
  year = {2026},
  url = {https://github.com/SinatrasC/pmpp-hard}
}