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Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

LLM decision components that can operate within agent workflows often produce action-relevant recommendations or judgements together with explanations. Operators may use the named factors to monitor a system, diagnose errors, or decide when to escalate an output. Such use assumes that the explanations agree with the component's observable decision behaviour. We test two interpretations of the named factors: necessity, meaning that changing a factor would change the output, and sufficiency, meaning that retaining it while removing other changeable information would preserve the output. We evalu

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