MINT combines symbolic trees with neural uncertainty estimation and LLM query curation to achieve near-expert planning performance by asking a small number of targeted questions that close knowledge gaps.
Calibrating and rotating: A unified framework for weight conditioning in peft.arXiv preprint arXiv:2511.00051
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MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation
MINT combines symbolic trees with neural uncertainty estimation and LLM query curation to achieve near-expert planning performance by asking a small number of targeted questions that close knowledge gaps.