OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind

• URL: http://arxiv.org/abs/2605.20423v1
• Date: Tue, 19 May 2026 19:19:26 GMT
• Title: OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind
• Abstract summary: OSCToM (Observer-Self Conflict Theory of Mind) is an approach for modeling nested belief conflicts in ToM tasks.<n>In our experiments, OSCToM-8B gives the best overall result among the systems tested.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these settings difficult. This paper presents OSCToM (Observer-Self Conflict Theory of Mind), an approach for modeling nested belief conflicts in LLM-based ToM tasks. The key case is one in which an observer's view of another agent conflicts with the observer's own belief state. Such cases go beyond simple perspective-taking and require recursive, multi-layered reasoning. OSCToM combines reinforcement learning (RL), an extended domain-specific language, and compositional surrogate models to generate observer-self conflicts. In our experiments, OSCToM-8B gives the best overall result among the systems tested. It improves on the reported ExploreToM results on FANToM and remains competitive on Hi-ToM and BigToM. On the information-asymmetric FANToM benchmark, OSCToM reaches 76% accuracy, compared with the 0.2% reported by ExploreToM. The data-synthesis procedure is also 6x more efficient, indicating that targeted training data can help smaller models handle advanced cognitive reasoning. The project code is available at https://github.com/sharminsrishty/osct.

Related papers

• Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL [55.05743310621117]
  Co-RL is a framework in which multiple decoupled models, sharing no parameters, are simultaneously optimized through RL using rewards derived from their peers.<n>We show that increasing cohort diversity, through heterogeneous model families, sizes, and rephrased training samples, reduces the correlated errors that drive self-reinforcing feedback loops.
  arXiv  Detail & Related papers  (2026-08-18T01:16:02Z)
• Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models [10.629439705877054]
  We study whether large language models (LLMs) exhibit genuine Theory of Mind (ToM) capabilities.<n>We introduce a handcrafted, richly annotated ToM dataset, including classic and perturbed false belief tasks.<n>We show a steep drop in ToM capabilities under task perturbation for all evaluated LLMs, questioning the notion of any robust form of ToM being present.
  arXiv  Detail & Related papers  (2026-02-25T16:24:35Z)
• Rethinking Reward Models for Multi-Domain Test-Time Scaling [91.76069784586149]
  Prior work generally assumes that process reward models (PRMs) outperform outcome reward models (ORMs) that assess only the final answer.<n>We present the first unified evaluation of four reward model variants across 14 diverse domains.<n>We attribute this to PRM-style stepwise scoring, which inherits label noise from LLM auto-labeling and has difficulty evaluating long reasoning trajectories.
  arXiv  Detail & Related papers  (2025-10-01T04:21:14Z)
• DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic [34.90622503586192]
  Theory-of-Mind (ToM) tasks pose a unique challenge for large language models.<n>We propose DEL-ToM, a framework that improves verifiable ToM reasoning through inference-time scaling.
  arXiv  Detail & Related papers  (2025-05-22T23:52:56Z)
• Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models? [14.29992535286614]
  Theory of Mind (ToM) is the ability to attribute mental states to others.<n>Recent advancements in Large Language Models have shown promising performance on ToM benchmarks.<n>Do these benchmarks necessitate explicit human-like reasoning processes, or can models succeed through alternative strategies?
  arXiv  Detail & Related papers  (2025-04-02T12:58:42Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [53.817538122688944]
  We introduce Reinforced Meta-thinking Agents (ReMA) to elicit meta-thinking behaviors from Reasoning of Large Language Models (LLMs)<n>ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.<n> Empirical results from single-turn experiments demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• Towards Reasoning Ability of Small Language Models [7.12809444398765]
  This paper introduces ThinkSLM, the first benchmark to systematically evaluate and study the reasoning abilities of SLMs.<n>We present a study evaluating 72 diverse SLMs from six major model families across 17 reasoning benchmarks.<n>Our findings challenge the assumption that scaling is the only way to achieve strong reasoning.
  arXiv  Detail & Related papers  (2025-02-17T08:59:16Z)
• Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition [2.089191490381739]
  Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others.<n>Large Language Models (LLMs) possess only a rudimentary understanding of ToM.<n>We propose Decompose-ToM'': an LLM-based inference algorithm that improves model performance on complex ToM tasks.
  arXiv  Detail & Related papers  (2025-01-15T18:44:01Z)
• Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning [88.68573198200698]
  We introduce ExploreToM, the first framework to allow large-scale generation of diverse and challenging theory of mind data.<n>Our approach leverages an A* search over a custom domain-specific language to produce complex story structures and novel, diverse, yet plausible scenarios.<n>Our evaluation reveals that state-of-the-art LLMs, such as Llama-3.1-70B and GPT-4o, show accuracies as low as 0% and 9% on ExploreToM-generated data.
  arXiv  Detail & Related papers  (2024-12-12T21:29:00Z)
• MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time [51.5039731721706]
  MindStar is a purely inference-based searching method for large language models. It formulates reasoning tasks as searching problems and proposes two search ideas to identify the optimal reasoning paths. It significantly enhances the reasoning abilities of open-source models, such as Llama-2-13B and Mistral-7B, and achieves comparable performance to GPT-3.5 and Grok-1.
  arXiv  Detail & Related papers  (2024-05-25T15:07:33Z)
• FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions [94.61530480991627]
  Theory of mind evaluations currently focus on testing models using passive narratives that inherently lack interactivity. We introduce FANToM, a new benchmark designed to stress-test ToM within information-asymmetric conversational contexts via question answering.
  arXiv  Detail & Related papers  (2023-10-24T00:24:11Z)
• Multimodal Chain-of-Thought Reasoning in Language Models [94.70184390935661]
  We propose Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework. Experimental results on ScienceQA and A-OKVQA benchmark datasets show the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2023-02-02T07:51:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.