Introduces EPC-AW to mitigate epistemic miscalibration in LLM multi-agent planning via consistency-based selection and refinement, reporting 9.75% average success improvement.
arXiv preprint arXiv:2506.05675 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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A pipeline generates CoT traces that reduce causal hallucination in small LLMs on event causality tasks, paired with a new Causal Hallucination Rate metric that guides and validates the process.
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When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
Introduces EPC-AW to mitigate epistemic miscalibration in LLM multi-agent planning via consistency-based selection and refinement, reporting 9.75% average success improvement.
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Generating Effective CoT Traces for Mitigating Causal Hallucination
A pipeline generates CoT traces that reduce causal hallucination in small LLMs on event causality tasks, paired with a new Causal Hallucination Rate metric that guides and validates the process.