Harness-G: A Graph-Structured Harness for Search Agents

Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formulated at the policy-environment interface. We observe pronounced retrieval aliasing during Search-R1 training: rollouts for the same question continue to generate distinct query st

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ethics.ai (29 July 2026), “Harness-G: A Graph-Structured Harness for Search Agents,” evidence record 14890, https://ethics.ai/record/14890 (originally published by HuggingFace Daily Papers).

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