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Jev-ToM: Evaluating Theory of Mind in Typed Decision Models

arXiv 2610.18610 ↗First posted 13 Oct 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Dialogue and collaborative assistants repeatedly judge what a partner knows, believes, or intends. Typed decision models offer a potential alternative to generative large language models for bounded Theory of Mind (ToM) selection. We present Jev-ToM, comparing five typed decision models and seven generative baselines on 11,117 English text questions from nine ToM benchmarks across six capability groups. TypeSafe Jev reaches 73.2% source-balanced accuracy including native controls and ranks first or second across all six capabilities, leading beliefs, goals/desires/intentions, and emotions. Other typed models show distinct strengths across tasks. Separate resource comparisons suggest that Jev could reduce the cost and waiting time of repeated ToM selection. Confidence-based selection remains benchmark-dependent, and supplied reasons can repair errors or overturn correct answers depending on their attachment. Candidate expansion reveals distinct selection and output-validity problems. Related-question cues account for most of Jev's downstream gains, with task-dependent effects from predicted states. The findings motivate testing Jev as a ToM selection component in assistants, with task-specific confidence calibration, candidate design, and output validation as integration priorities.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 13 Oct 2026.

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20 of 41 references.

References · 20

  1. 2023Robots That Ask For Help: Uncertainty Alignment for Large Language Model Plannerscited by 308
  2. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 292
  3. 2023Understanding Social Reasoning in Language Models with Language Modelscited by 172
  4. 2023FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactionscited by 171
  5. 2023Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilitiescited by 89
  6. 2024ToMBench: Benchmarking Theory of Mind in Large Language Modelscited by 72
  7. 2024SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMscited by 57
  8. 2024NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surroundingcited by 51
  9. 2026JEV-as-a-Judge: Accept When Confident, Escalate When Unsurecited by 30
  10. 2026Type-Safe Is Not Error-Free: Typed Decision Models Follow the Option Name, Not the Definition Bound to Itcited by 25
  11. 2025ToMATO: Verbalizing the Mental States of Role-Playing LLMs for Benchmarking Theory of Mindcited by 21
  12. 2025Large Language Models as Theory of Mind Aware Generative Agents with Counterfactual Reflectioncited by 13
8 more
  1. 2026JevAdvBench: A Benchmark and Black-Box Attacks for Reinforcement Learning for Calibrated Decisions Modelscited by 10
  2. 2025Infusing Theory of Mind into Socially Intelligent LLM Agentscited by 8
  3. 2026Probability Contracts: Accuracy, Coherence, and Decisions Across LLM Interfacescited by 3
  4. 2026Mnemon: Raw Records, Fast Judgments, Slow Thoughtscited by 2
  5. 2026MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoningcited by 1
  6. 2026PACT: Pairwise-Anchored Calibrated Tuning for Single-Token Typed Decisionscited by 1
  7. 2026Calibrated to Whom? Persona and Language Effects on Cultural Values in JEVcited by 1
  8. 2026General Decision Models: Benchmarking and Insights Beyond Jevcited by 1

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