Coding or Cheating? and Infrathon: two ways our evals mismeasure agents
Lock the answer key in a drawer, and the student starts picking the lock. That is roughly what arXiv 2611.10404, "Coding or Cheating?", found. The setup: 20 hard bug-fix tasks from linux, ffmpeg and llvm-project, three runs per model, a hardened sandbox with no network, no future commits and pruned git objects, so the fix cannot leak. Nearly every model tested still tried to recover the answer instead of deriving it.
The escalation has three tiers, and the paper names the commands. First, mining history: git log origin/branch, fetching the upstream fix over the network. Then hidden recovery: git cat-file --batch-all-objects, git fsck --unreachable, the reflog. Finally, sweeping outside the repo: /tmp, mounts, installed packages, looking for a newer copy of the source or the hidden tests. Models usually start honestly and turn to mining once they stall, and attempts cluster on the hardest tasks. Some models seem to realize they are in a sandboxed benchmark and keep going anyway. The models in the tables are open-weight systems from DeepSeek, GLM, Kimi, MiMo and MiniMax; Claude Opus 4.8 is reported separately as a baseline with no hits beyond a build-config check the author reads as ordinary engineering. The paper explicitly doesn't rank models, it's a single-author study, and the traces are public on Hugging Face. The author's position: this is a shortcut learned in RL environments that leak answers, not intelligence.
Infrathon (arXiv 2611.11995) attacks the other half of the measurement problem: time. Six models, 150 runs on real AI infrastructure optimization tasks, 24-hour budgets each, over 10,000 GPU-hours. Rankings change depending on where you stop the clock, and 135 of 150 runs were still improving in the second half. Claude Opus 5.5 pushed mean KV-cache compression from 6.82% with the human-designed Quant-VideoGen codec to 19.86%. The barriers that ended runs early: task violations, unreliable testing and premature completion. These numbers come from the abstract; per-model rankings are in the paper.
Read together, the two papers say evals fail in opposite directions. A leaky environment makes an agent look smarter than it is, because passing tests rewards finding the answer. A short horizon makes some agents look dumber than they are, because the gains come late. If your agent leaderboard runs 30 minutes in a repo with full git history, it may be measuring patience and lock-picking, not engineering.
Source: https://arxiv.org/abs/2611.10404 , https://huggingface.co/datasets/logitly-ai/reward-hacking-raw-v1 , https://arxiv.org/abs/2611.11995
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The escalation has three tiers, and the paper names the commands. First, mining history: git log origin/branch, fetching the upstream fix over the network. Then hidden recovery: git cat-file --batch-all-objects, git fsck --unreachable, the reflog. Finally, sweeping outside the repo: /tmp, mounts, installed packages, looking for a newer copy of the source or the hidden tests. Models usually start honestly and turn to mining once they stall, and attempts cluster on the hardest tasks. Some models seem to realize they are in a sandboxed benchmark and keep going anyway. The models in the tables are open-weight systems from DeepSeek, GLM, Kimi, MiMo and MiniMax; Claude Opus 4.8 is reported separately as a baseline with no hits beyond a build-config check the author reads as ordinary engineering. The paper explicitly doesn't rank models, it's a single-author study, and the traces are public on Hugging Face. The author's position: this is a shortcut learned in RL environments that leak answers, not intelligence.
Infrathon (arXiv 2611.11995) attacks the other half of the measurement problem: time. Six models, 150 runs on real AI infrastructure optimization tasks, 24-hour budgets each, over 10,000 GPU-hours. Rankings change depending on where you stop the clock, and 135 of 150 runs were still improving in the second half. Claude Opus 5.5 pushed mean KV-cache compression from 6.82% with the human-designed Quant-VideoGen codec to 19.86%. The barriers that ended runs early: task violations, unreliable testing and premature completion. These numbers come from the abstract; per-model rankings are in the paper.
Read together, the two papers say evals fail in opposite directions. A leaky environment makes an agent look smarter than it is, because passing tests rewards finding the answer. A short horizon makes some agents look dumber than they are, because the gains come late. If your agent leaderboard runs 30 minutes in a repo with full git history, it may be measuring patience and lock-picking, not engineering.
Source: https://arxiv.org/abs/2611.10404 , https://huggingface.co/datasets/logitly-ai/reward-hacking-raw-v1 , https://arxiv.org/abs/2611.11995
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