The paper introduces PIP-ToM, a framework that addresses the evidence-admissibility problem in multimodal Theory of Mind (ToM) by ensuring that only evidence accessible to an agent and diagnostic of its mental state is used in hypothesis evaluation. PIP-ToM builds agent‑specific evidence stores, aligns them with structured mental‑state claims, and calibrates Bayesian inference scores while preserving the underlying inference process. Using GPT‑4o, the method outperforms existing ToM approaches on MuMA‑ToM, MMToM‑QA, and Hi‑ToM benchmarks, achieving accuracies of 92.56%, 95.00%, and 95.00% respectively.
arXiv:2608. 09638v1 Announce Type: new Abstract: Theory of Mind (ToM) is essential for agent interactions, yet existing evaluations either rely on static scenarios that oversimplify mental-state reasoning or interactive settings that provide limited diagnostic insight.
arXiv:2605. 24528v2 Announce Type: replace Abstract: Real world decision-making requires constructing mental models under uncertainty over evidence, over the underlying causal rules, and over the state of the world itself.
arXiv:2606. 12721v1 Announce Type: new Abstract: Inferring others' beliefs requires more than reading surface signals; it requires tracking who told them what, in what order, and how credibly.
BASIS is a benchmark that tests large language model agents on their ability to infer users’ mental states, detect belief‑reality mismatches, and use this insight to craft better solutions in interactive settings. The study shows that current models often ignore users’ mental states, leading to poorer outcomes, while explicitly eliciting and incorporating such information improves solution quality. Additional experiments compare BASIS with Chain‑of‑Thought prompting, targeted training, and multi‑agent collaboration to demonstrate the practical benefits of mental‑state awareness.
The paper investigates how children build mental models under uncertainty using a Box Task that involves interacting with a stochastic, partially observed environment. It introduces a Bayesian particle-based inference framework that maintains a small set of working hypotheses and continually generates new ones, offering two implementations: constraint-based and programmatic. The study demonstrates that accounting for subjective reliability of evidence and open-ended hypothesis spaces is essential to explain children's behavior, and shows that programmatic hypotheses can be generated via LLM-based program synthesis, robust across different prompts and LLM backends.
Theory of Mind (ToM) benchmarks for Large Language Models (LLMs) typically rely on passive question-answering formats, but the deployment of LLMs in increasingly agentic and autonomous forms demands new evaluations. In this paper we evaluate an agent's ability to induce specific belief states in other agents by taking actions rather than using conversational persuasion, a capability we call Non-Conversational Planning ToM (NCP-ToM).
arXiv:2606. 30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment.
arXiv:2608. 15687v1 Announce Type: new Abstract: Sycophancy, the tendency of a language model to change its answer to match a user's stated belief, is a common alignment failure.
arXiv:2610.17310v1 Announce Type: new Abstract: Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral pattern...
arXiv:2510.15144v4 Announce Type: replace Abstract: Simulating human reasoning in open-ended tasks has long been a central aspiration in AI and cognitive science. While large language models now appr...
The paper proposes a distributional theory explaining how large language models (LLMs) incorporate external evidence into their decision-making process. It identifies three key predictions: (1) evidence is more persuasive when it aligns with the model’s prior beliefs, (2) models more readily accept errors from their own internal processes than from external sources, and (3) the same evidence can improve weaker models while harming stronger ones. Extensive experiments across ten million trials, twelve LLMs from four families, and eight domains—including quantum mechanics, physics, genetics, and molecular biology—confirm these predictions and reveal that evidence integration occurs late in the network as a structured sequence of steps rather than through a simple trust metric.
The paper introduces a Bayesian framework for assessing intelligence in agents such as language models. It shows that an agent’s reports are consistent with Bayesian intelligence if they are not fully contradictory across prompts, and it defines an intelligence order based on the informativeness of internal experiments. The work also demonstrates the challenges of aggregating coarse reports from intelligent agents, revealing that optimal aggregation can assign arbitrary weights to unexcluded states unless the agent reports a belief about the complete state of the world.