NonZero introduces an interaction score and bandit-formalized proposal rule for local agent deviations in multi-agent MCTS, delivering a sublinear local-regret guarantee and improved sample efficiency on game benchmarks without full joint-action enumeration.
arXiv preprint arXiv:2511.06142 , year=
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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.
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NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
NonZero introduces an interaction score and bandit-formalized proposal rule for local agent deviations in multi-agent MCTS, delivering a sublinear local-regret guarantee and improved sample efficiency on game benchmarks without full joint-action enumeration.
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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.