Theory of Mind in Large Language Models: Assessment and Enhancement

Chengwei Qin; Cheston Tan; Ruirui Chen; Weifeng Jiang

REVIEW 3 major objections 5 minor 1 cited by

Theory of Mind in Large Language Models: Assessment and Enhancement

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper claims to be the first broad survey covering both the evaluation and the enhancement of theory of mind in large language models.

desk verdict A competent, well-organized survey of LLM ToM benchmarks and enhancement methods whose only real flaw is an overstated 'first broad survey' novelty claim and no search protocol. read the letter →

arxiv 2505.00026 v2 pith:KPGEHEY7 submitted 2025-04-26 cs.CL cs.AI

Ruirui Chen , Weifeng Jiang , Chengwei Qin , Cheston Tan This is my paper · ORCID

classification cs.CLcs.AI

keywords theoryofmindlargelanguagemodelsbenchmarksevaluationenhancementstrategiespromptengineeringmentalstatessurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey tries to organize a fast-moving research area: do large language models really have a theory of mind, and if not, what can be done about it? The paper's central claim is that it is the first broad survey to cover both evaluation and enhancement of LLMs' theory-of-mind capabilities, bringing the two strands under one taxonomy. For evaluation, it reviews story-based benchmarks from 2023–2024, sorting them by text-only versus multimodal input and by which mental states they cover. For enhancement, it separates prompt-only strategies from methods that add fine-tuning, symbolic reasoning, or inverse planning. A sympathetic reader would take the contribution to be the map itself: a structured comparison that makes it easy to see how benchmarks and improvement methods have evolved and where gaps remain.

What carries the argument

The paper's organizing device is the ATOMS inventory of seven mental states—beliefs, intentions, desires, emotions, knowledge, percepts, and non-literal communications—which it uses as a common yardstick to compare every benchmark. A second load-bearing device is the notion of "order" of belief attribution, from first-order (what a character believes) up to fourth-order (what one character thinks another believes about a third's belief), which lets the survey measure benchmark difficulty and track evolution. On the enhancement side, the central distinction is prompt-only methods versus methods that add fine-tuning, model checking, or inverse planning. These axes together carry the survey's main work: turning a scattered literature into comparison tables and trend statements.

What would settle it

A reader could falsify the first-survey claim by finding a published survey from before 2025 that already covers both evaluation and enhancement of theory of mind in large language models. The taxonomy's completeness could be tested by rerunning the review with purely spatial benchmarks counted in; if the claimed trends—conversation-based contexts and multimodal expansion—survive the addition, the map holds, and if not, the selection was not representative.

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Extended reading notes

Core claim

On its own terms, the paper establishes a structured map of current theory-of-mind research on LLMs. It claims that story-based benchmarks have rapidly evolved from first-order belief questions on synthetic narratives toward higher-order beliefs (up to fourth order), multi-turn conversational contexts, broader mental states, and multimodal household scenarios, and it supports this with a comparison of thirteen benchmarks. It further claims that enhancement strategies fall into two families: prompt-only methods—belief graphs, perspective-taking, perception-aware context extraction, temporal belief-state chains—and methods that add fine-tuning or external machinery such as semantic parsing with a model checker and inverse multi-agent planning. The paper's conclusion is that despite these benchmarks and methods, LLMs still lack dependable theory-of-mind abilities, and consistent assessment remains difficult because theory of mind cannot be captured by a limited set of questions.

Load-bearing premise

The load-bearing premise is that the benchmarks and enhancement methods selected for review fairly represent the field; the paper does not document a systematic search protocol or inclusion criteria, so if important work—for example, spatial-scenario benchmarks it explicitly excludes—were weighed equally, the taxonomy and conclusions could shift.

Editorial extensions

If this is right

Researchers can use the mental-state and order columns to choose benchmarks that match the capability they want to test, instead of relying on popularity.
The documented trend implies that future benchmarks will continue toward conversational and multimodal settings, so methods tested only on narrative multiple-choice questions may not transfer.
Because prompt-only enhancement methods are pipelines, their ceiling is set by the first perception-tracking step; improving that step should improve downstream answers across methods.
The paper's reading of the evidence says single-benchmark accuracies should not be read as proof of general theory of mind; robust evaluation needs multiple mental states and question formats.
Story-based passive benchmarks are, by the paper's account, insufficient; evaluating LLMs as active agents in interactive settings is a stated next step.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The comparison table implies a convergence that the paper does not state: both benchmark design and enhancement methods are gravitating to the same bottleneck—tracking what each character perceives and when—so a diagnostic benchmark that isolates perception errors could predict which enhancement method will help.
The paper's observation that methods are mostly tested on multiple-choice formats, if taken further, suggests that open-ended evaluation would likely compress the performance differences between prompt-only and fine-tuned methods.
A natural extension the authors leave implicit: apply prompt-only techniques like perspective-taking and temporal belief-state chains to emotions, desires, and non-literal communication, using a benchmark with full mental-state coverage, to test whether the methods generalize beyond beliefs.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a useful survey, well organized, and mostly accurate. The 'first broad survey' claim is the one thing I'd push back on, but it's a fixable overstatement rather than a load-bearing flaw.

What's new: the pairing of benchmark review with enhancement-strategy review in one place, with a mental-state coverage table (Table 1) and a method comparison table (Table 2). That genuinely helps someone entering the area. The benchmark descriptions are detailed and the trend analysis (order, generation, context, questions, mental states) is sensible. The enhancement section covers SYMBOLICTOM, SIMTOM, PercepToM, TIMETOM, ToM-LM, BIP-ALM, LIMP in enough depth to convey how they relate. The paper also flags real limitations of each method, which is more than many surveys do.

Soft spots: the novelty claim is not supported. No search protocol, no inclusion criteria, no databases or dates. The Limitations section explicitly excludes spatial benchmarks like BIB and relegates interactive work to an appendix, so calling it the 'first broad survey' of both evaluation and enhancement is a stretch given how fast this literature moves. I don't know of a direct competitor that covers the same 2023–2025 ground, so I'd frame it as 'first to our knowledge' with the scope caveat—which the paper mostly does, but the contribution bullet says 'broad survey' without the caveat. Also, the 'assessment' component is descriptive: no performance numbers, so it's more a map than an evaluation. That's fine for a survey, but the word 'assessment' in the title oversells it slightly. The interactive benchmarks and pre-LLM methods are relegated to an appendix; that's a scope decision, not a flaw, but it undercuts the 'broad' claim further. The self-citations (Qin et al. 2023, Parmar et al. 2024) are not load-bearing; they're context and a future-direction suggestion, so no circularity problem.

If you're new to LLM ToM and want a structured entry point, this paper is worth reading. It won't change your research agenda if you're already in the area, but it's reliable as a reference. I'd send it to a serious referee mostly to check whether the 'first' claim holds and to push for a methodology note on literature selection. That's a light revision, not a rejection.

Recommendation: accept for review; it's a solid reference with an overstatement that the authors can fix.

Referee Report

3 major / 5 minor

Summary. This survey paper reviews theory of mind (ToM) in large language models from two angles: evaluation benchmarks and enhancement strategies. For evaluation, it focuses on story-based benchmarks from 2023-2024, both text-only and multimodal, describing their design, mental state coverage, and evolution. For enhancement, it categorizes recent methods into those relying solely on prompting and those incorporating additional techniques such as fine-tuning or inverse planning, then proposes future research directions. The paper claims to be the first broad survey covering both assessment and enhancement of LLMs' ToM capabilities.

Significance. If its coverage is representative and its taxonomy sound, this survey would be a useful quick reference for researchers entering the area: it organizes eleven benchmarks in a comparative table, summarizes enhancement approaches at a glance, and flags open problems such as higher-order reasoning, multimodal evaluation, and active/agentic ToM. The mental-state coverage table and the clear separation of prompt-based and fine-tuning-based methods are helpful organizational devices. However, the paper's value depends on its novelty and coverage claims, and its 'assessment' component is descriptive rather than evaluative, containing no performance numbers or critical synthesis of empirical findings.

major comments (3)

[Section 1, Contribution 'Broad Survey' and Limitations] The claim that this is 'the first broad survey that addresses both the evaluation and enhancement of LLMs' ToM capabilities' is not supported by any reported literature search protocol. The paper does not state which databases were queried, what search strings or inclusion/exclusion criteria were used, or the date of the search. The Limitations paragraph additionally narrows the coverage to story-based benchmarks, explicitly excluding purely spatial scenarios such as BIB and relegating interactive benchmarks to an appendix. Given these restrictions, the 'broad survey' and 'first' assertions are stronger than the evidence provided. Please either report a systematic search and justify the coverage decisions, or temper the novelty claim by situating the paper relative to existing surveys (beyond Ma et al. 2023b).
[Sections 3-4 and the Abstract] The paper's abstract and title promise an assessment of LLMs' ToM capabilities, but the body never reports any evaluation results, model-family comparisons, or even a summary of accuracy findings across the benchmarks and enhancement methods. For example, Section 4 opens by stating that 'most evaluations... highlight the limitations of LLMs' without citing any specific numbers or aggregated conclusions, and the benchmark descriptions in Section 3 contain no performance data. A survey of this kind does not need a full meta-analysis, but it should at least synthesize the directional findings (e.g., order effects, false-belief vs. true-belief gaps, differences between text-only and multimodal settings). Without this, the 'assessment' component is a catalog rather than an assessment.
[Section 2 and Table 1] The paper treats 'goals' in MMToM-QA and MuMA-ToM as equivalent to 'intentions' in ATOMS. This is a reasonable decision, but it is also a substantive interpretive choice that affects the mental-state coverage table, and it is stated only in a table footnote. Please justify this mapping, or at least acknowledge that it is an assumption, because a reader comparing Table 1 across benchmarks could otherwise overinterpret the coverage comparison.

minor comments (5)

[Abstract and Section 1] The phrase 'in-depth analysis' appears in both the abstract and the contributions list; consider varying the wording to avoid redundancy.
[Section 2 and Appendix B] The appendix heading 'Abilities in Theory of Mind Space (A TOMS)' contains a spacing error; it should be 'ATOMS'.
[Appendix A.3] The sentence 'These task, with adjustments to elements like the characters, the container, or the objects involved, forms the basis...' has a subject-verb agreement error ('These task' and 'forms').
[Section 4.1, Figure 2] In the SYMBOLICTOM panel, the notation 'BBob,Alice' appears without clear subscript formatting; please use a consistent notation such as B_Bob,Alice or define it in the caption.
[Table 3] Table 3 omits all citations 'due to width constraints.' Since the table is a key reference comparison, please restore the citations or provide a companion table with full references.

Circularity Check

0 steps flagged · score 2.0 of 10

Survey contains no circular derivation; only two minor non-load-bearing self-citations, so the circularity score is 2.

full rationale

This paper is a literature survey, not a derivation of new empirical results: it contains no fitted parameters, no constructed equation that reduces to its own inputs, and no benchmark score that is predicted from the same data used to fit a model. The central 'first broad survey' claim is a coverage/gap assertion ('to the best of our knowledge, only Ma et al. (2023b) has reviewed ToM benchmarks... Yet, a detailed summary of these newly proposed strategies is still lacking'), not a result derived from the cited literature, and it is not supported by any self-citation. The only overlapping-author citations are Qin et al. (2023), used as general background ('LLMs have achieved remarkable success across various tasks, particularly in NLP'), and Parmar et al. (2024), used in a future-directions example about cartoon-based stimuli; neither is load-bearing for the survey's taxonomy, benchmark comparisons, or enhancement-strategy classification. There is no imported uniqueness theorem, no ansatz smuggled in via self-citation, and no renaming of a known result as a new contribution. The Limitations section explicitly scopes the survey to story-based benchmarks and delegates interactive and pre-LLM methods to appendices, which is an honest coverage limitation rather than a circular step. The score of 2 reflects the presence of two minor non-load-bearing self-citations, not any actual circular reduction.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities. It rests on three domain assumptions: the ATOMS taxonomy is an appropriate lens for categorizing ToM benchmarks; story-based benchmarks are representative of the field; and the secondary descriptions of cited work are accurate.

assumptions (3)

domain assumption The ATOMS framework (Beaudoin et al., 2020) provides a valid and complete taxonomy of mental states for evaluating ToM.
Used to classify benchmarks in Table 1 and to argue that beliefs are the most studied state. If the taxonomy is incomplete or inappropriate for LLMs, the coverage analysis is distorted.
domain assumption Story-based benchmarks are a representative and leading approach for evaluating ToM in LLMs.
This justifies the paper's focus on story-based benchmarks and the relegation of interactive benchmarks to the appendix. The paper cites Ma et al. (2023b) for this, but it is an assumption about research practice.
domain assumption The descriptions of the cited benchmarks and methods accurately reflect the original papers.
The survey's value depends on the fidelity of its summaries; no verification of original data or code is provided.

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Cite this review

Pith. "Pith review of Theory of Mind in Large Language Models: Assessment and Enhancement." pith.science (2026). https://pith.science/paper/KPGEHEY7

@misc{pith2026250500026,
  author       = {Pith},
  title        = {Pith review of: Theory of Mind in Large Language Models: Assessment and Enhancement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KPGEHEY7}},
  note         = {Machine review of arXiv:2505.00026}
}

read the original abstract

Theory of Mind (ToM)-the ability to reason about the mental states of oneself and others-is a cornerstone of human social intelligence. As Large Language Models (LLMs) become increasingly integrated into daily life, understanding their ability to interpret and respond to human mental states is crucial for enabling effective interactions. In this paper, we review LLMs' ToM capabilities by analyzing both evaluation benchmarks and enhancement strategies. For evaluation, we focus on recently proposed and widely used story-based benchmarks. For enhancement, we provide an in-depth analysis of recent methods aimed at improving LLMs' ToM abilities. Furthermore, we outline promising directions for future research to further advance these capabilities and better adapt LLMs to more realistic and diverse scenarios. Our survey serves as a valuable resource for researchers interested in evaluating and advancing LLMs' ToM capabilities.

Figures

Figures reproduced from arXiv: 2505.00026 by the authors.

**Figure 2.** A comparison of methods for enhancing LLMs’ ToM capabilities through different prompting techniques, [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Experimental scenario in Sally-Anne test [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

Works this paper leans on

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

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arXiv preprint arXiv:2410.13648

SimpleToM: Exposing the gap between ex- plicit ToM inference and implicit ToM application in llms. arXiv preprint arXiv:2410.13648. Guiyang Hou, Wenqi Zhang, Yongliang Shen, Linjuan Wu, and Weiming Lu. 2024. TimeToM: Temporal space is the key to unlocking the door of large lan- guage models’ theory-of-mind. In Findings of the Association for Computational...

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Joint document-level event extraction via token-token bidirectional event completed graph. In Proceedings of the 61st Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 10481–10492, Toronto, Canada. Association for Computational Linguistics. Ben Wang and Aran Komatsuzaki. 2021. Gpt-j-6b: A 6 billion parameter ...

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Where will Sally look for her marble?

A comprehensive survey of continual learn- ing: Theory, method and application. IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 46(8):5362–5383. Jason Weston, Antoine Bordes, Sumit Chopra, and Tomás Mikolov. 2016. Towards ai-complete question answering: A set of prerequisite toy tasks. In 4th In- ternational Conference on Learning Repres...

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and adopting the dataset generation proce- dure of the bAbI (Weston et al., 2016) dataset gener- ation procedure, Grant et al. (2017) took the initial step in designing benchmarks aimed at evaluat- ing the mental-state reasoning abilities of question answering models, specifically focusing on first- order beliefs (Nematzadeh et al., 2018). Building upon t...

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theory of mind

Does the autistic child have a “theory of mind” ? Cognition, 21(1):37–46. Cindy Beaudoin, Élizabel Leblanc, Charlotte Gagner, and Miriam H Beauchamp. 2020. Systematic review and inventory of theory of mind measures for young children. Frontiers in Psychology, 10:2905. Marta Białecka-Pikul, Anna Kołodziejczyk, and Sandra Bosacki. 2017. Advanced theory of m...

arXiv 2020

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