Misleading tool feedback produces value inversion in LLM agents, with performance dropping below matched no-feedback baselines on HotpotQA and similar tasks.
The confidence dichotomy: Analyzing and mitigating miscalibration in tool-use agents
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Action-conditioned estimation of intervention advantage via prefix branching reduces control regret over calibrated scalar risk scores in LLM agent oversight across benchmarks.
citing papers explorer
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Don't Blindly Trust It: How Unreliable Feedback Breaks Tool-Using LLM Agents
Misleading tool feedback produces value inversion in LLM agents, with performance dropping below matched no-feedback baselines on HotpotQA and similar tasks.
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Calibration Is Not Control: Why LLM-Agent Oversight Needs Intervention
Action-conditioned estimation of intervention advantage via prefix branching reduces control regret over calibrated scalar risk scores in LLM agent oversight across benchmarks.