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This paper investigates how language models separate a character's belief from reality, finding that they use a shared value slot for attributed values and a router at the query position to select the frame (belief or reality) to read out. It identifies two routes for asserted and derived beliefs, and shows that the slot itself carries no belief-reality tag; the separation lies in dissociated routing subspaces.
MafiaScope is an open testbed that uses the social deduction game Mafia to probe LLM agents' beliefs non-invasively and in real-time, enabling fine-grained analysis of machine Theory of Mind through structured probe questions, interactive visualization, and counterfactual replay.
This paper introduces Non-Conversational Planning Theory of Mind (NCP-ToM) and a novel evaluation framework, NCP-ExploreToM, to assess whether LLMs can induce specific belief states in other agents through actions rather than conversation. Testing on frontier models and humans across 600 tasks, GPT-5 achieved ~80% success, outperforming humans, though all models struggled more with false belief states.
This paper investigates the emergence of situation modeling and mentalizing abilities in transformer language models across training stages, finding that false belief task performance depends on model size and training volume, emerges late in pretraining, and shows fragility with non-factive verbs.
Introduces a triadic variant of the Werewolf social-deduction game with a Jester role to evaluate multi-hop theory of mind in LLMs. Experiments show that current models struggle with the inverted incentives, exposing limitations in their reasoning about opponents' utilities.
The paper introduces the Theory of Mind Utility (ToM-U), a formal computational-level specification for inferring others' epistemic states by constructing Local Epistemic World Models (LEWMs). It differs from Bayesian ToM and simulation theory by providing a domain-agnostic mechanism for belief inference without commitment to algorithmic implementation.
Introducing RecToM, an inference-time framework that models nested beliefs via recursive perspective construction for Theory of Mind reasoning in LLMs, achieving state-of-the-art performance on multiple benchmarks.
A reflection on the difference between LLM theory of mind and human theory of mind, arguing that LLMs lack affective empathy due to their reliance on objective data, while humans integrate subjective experiences.
MindZero introduces a self-supervised reinforcement learning framework that trains multimodal large language models for efficient and robust online mental reasoning without requiring mental state annotations, outperforming model-based methods in accuracy and efficiency.
This paper introduces Differentiable Belief-based Opponent Shaping (D-BOS), a first-order method that treats observer beliefs as the shaped state and differentiates through belief update dynamics, allowing optimal strategies to emerge naturally from the environment's reward structure in hidden-role multi-agent settings.
OmniToM introduces a benchmark that evaluates large language models' theory of mind by requiring explicit belief structure extraction and labeling, revealing a bottleneck in tracking actor-specific beliefs despite strong performance on endpoint QA tasks.
Proposes Agent-ToM, a learning-to-monitor framework using Theory-of-Mind reasoning to detect covert malicious behavior in autonomous LLM agents by inferring beliefs and intents, outperforming baseline monitors.
This paper presents OSCToM, an RL-guided method for generating adversarial data to test nested belief conflicts in LLMs, improving Theory of Mind reasoning on benchmarks like FANToM.
This paper proposes a new interactive evaluation paradigm for Theory of Mind in LLMs, finding that improvements on static benchmarks do not translate to better performance in dynamic human-AI interactions, highlighting the need for interaction-based assessments.
This paper introduces the Instruction Inference task to evaluate Theory of Mind capabilities in LLM-based agents during human-agent collaboration with incomplete or ambiguous instructions. The authors present Tomcat, an LLM agent tested on GPT-4o, DeepSeek-R1, and Gemma-3-27B, demonstrating performance comparable to human participants in inferring unspoken intentions.
OpenAI and University of Oxford researchers present LOLA (Learning with Opponent-Learning Awareness), a reinforcement learning method that enables agents to model and account for the learning of other agents, discovering cooperative strategies in multi-agent games like the iterated prisoner's dilemma and coin game.