Introduces BeliefTrack benchmark diagnosing three CBM failures in LLMs and shows RL with belief-state rewards cuts failure rates by 70.9% while representation steering cuts them by 46.1%.
Muma- tom: Multi-modalmulti-agenttheoryofmind.ProceedingsoftheAAAIConferenceonArtificialIntelligence, 39(2):1510–1519, Apr
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EnactToM is an evolving benchmark of embodied multi-agent tasks that tests functional Theory of Mind by requiring agents to act optimally on implicit beliefs in partially observable 3D environments.
citing papers explorer
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When Should Models Change Their Minds? Contextual Belief Management in Large Language Models
Introduces BeliefTrack benchmark diagnosing three CBM failures in LLMs and shows RL with belief-state rewards cuts failure rates by 70.9% while representation steering cuts them by 46.1%.
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EnactToM: An Evolving Benchmark for Functional Theory of Mind in Embodied Agents
EnactToM is an evolving benchmark of embodied multi-agent tasks that tests functional Theory of Mind by requiring agents to act optimally on implicit beliefs in partially observable 3D environments.