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FIRE: Failure-Informed Runtime Engineering for Reliable Language-Model Agents
Language-model agents often reach a working solution and then fail to consistently deliver it. We study runtime policies: targeted natural-language instructions and action denials applied by the agent harness at states that preceded observed failures, without changing model weights or the user promp
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