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Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

Paper recorded by Signals 4 on 2026-09-04 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-04 on arXiv · recorded by Signals 4 on 2026-09-07

Category: cs.AI · 人工智能 · first seen 2026-09-07

Abstract

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 name

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#165 most recent of 300 cs.AI papers we have recorded · ↑ newer: Multi-Step Tool-Calling over Korean Open Public APIs: A Benchmark and · ↓ older: Reflection-aware Generative Novel View Synthesis
Cite this page: Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence: the #165 most recent of 300 cs.AI papers we have recorded (as of 2026-09-04). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/necessary-or-sufficient-evaluating-llm-explanations-with-behavioural-evidence.html
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