# Beyond Words: Evaluating and Bridging Epistemic Divergence in User-Agent Interaction via Theory of Mind arXiv: https://arxiv.org/abs/2602.13832 · HTML: https://mlsanity.com/paper/2602.13832/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. - Builds on 33 of its 53 references that are in the map. - First posted on arXiv 14 Feb 2026. ## Placed in - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 30 of 33) - [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024) - [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025) - [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024) - [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025) - [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025) - [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018) - [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024) - [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://mlsanity.com/paper/2507.21046/) (2025) - [Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs](https://mlsanity.com/paper/2502.09597/) (2025) - [Ministral 3](https://mlsanity.com/paper/2601.08584/) (2026) - [GUI Agents with Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2411.04890/) (2024) - [Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker](https://mlsanity.com/paper/2306.00924/) (2023) - [A Survey of AI Agent Protocols](https://mlsanity.com/paper/2504.16736/) (2025) - [UserBench: An Interactive Gym Environment for User-Centric Agents](https://mlsanity.com/paper/2507.22034/) (2025) - [ToMBench: Benchmarking Theory of Mind in Large Language Models](https://mlsanity.com/paper/2402.15052/) (2024) - [OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models](https://mlsanity.com/paper/2402.06044/) (2024) - [NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding](https://mlsanity.com/paper/2404.13627/) (2024) - [Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests](https://mlsanity.com/paper/2310.20320/) (2023) - [Training Proactive and Personalized LLM Agents](https://mlsanity.com/paper/2511.02208/) (2025) - [Ambig-SWE: Interactive Agents to Overcome Underspecificity in Software Engineering](https://mlsanity.com/paper/2502.13069/) (2025) - [Position: Theory of Mind Benchmarks are Broken for Large Language Models](https://mlsanity.com/paper/2412.19726/) (2024) - [Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models](https://mlsanity.com/paper/2407.06004/) (2024) - [TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models' Theory-of-Mind](https://mlsanity.com/paper/2407.01455/) (2024) - [Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind](https://mlsanity.com/paper/2106.13249/) (2021) - [Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States](https://mlsanity.com/paper/2505.17663/) (2025) - [LLM Theory of Mind and Alignment: Opportunities and Risks](https://mlsanity.com/paper/2405.08154/) (2024) - [TOM-SWE: User Mental Modeling For Software Engineering Agents](https://mlsanity.com/paper/2510.21903/) (2025) - [Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?](https://mlsanity.com/paper/2504.01698/) (2025) ## Built on by (most cited first, 2 of 2) - [Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue](https://mlsanity.com/paper/2608.08210/) (2026) - [Collaborative Theory of Mind: Learning to Self-Revise by Anticipating User Responses](https://mlsanity.com/paper/2610.12872/) (2026) ## Abstract (arXiv) Large language model (LLM) agents are increasingly capable of carrying out complex, long-horizon tasks, yet effective assistance requires more than faithfully executing user instructions. Users' requests reflect their current understanding of the problem, which may be incomplete, outdated, or incorrect. When this understanding diverges from the actual task state, literal instruction following can fail to accomplish the user's underlying goal. We introduce BASIS (emphBelief-Aware Solutions in Interactive Settings), a benchmark that evaluates whether agents can infer the mental states underlying user requests, recognize belief-reality mismatches, and use this understanding to develop appropriate solutions. Rather than assessing mental-state inference solely through explicit questions, BASIS evaluates both the utility of such inferences and the quality of downstream solutions in user-agent interactions. Evaluation results show that current models often fail to account for users' mental states when generating solutions, and that solution quality is consistently higher when such information is explicitly elicited and made available during problem solving. We further investigate the practical value of mental-state consideration through comparisons with generic Chain-of-Thought prompting, controlled interventions on mental-state information, targeted training, and multi-agent collaboration experiments under partial observability. Together, these studies highlight the importance of not merely inferring others' mental states, but also using those understandings to provide grounded, goal-directed assistance.