Harness-agnostic detection and immunization of reward hacking in self-evolving language models

The paper introduces HackProbe, a black‑box monitoring tool that can be attached to any self‑evolving language model loop without accessing internal weights or activations. HackProbe uses a fixed‑distribution comparison core and a rotated fresh layer to detect reward hacking through four statistical tests, and it can immunize the model by selecting honest candidates from the proposal pool. Experiments on a controlled host with injected hacking channels show that HackProbe achieves higher AUROC and lower false‑positive rates than the strongest baseline, and its bandwidth‑limited reselection improves true capability under hacking more than it harms clean runs.

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