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REVIEW 3 major objections 6 minor 1 cited by

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks

T0 review · 3 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read This survey argues that language models show real but fragile theory of mind: they match human and child performance on selected tests, harbor internal representations of others' beliefs, and could become safety risks as those…

desk verdict A competent, cautious survey of LLM Theory of Mind that ties behavioral and representational work to safety risks, but the risk urgency rests on an unargued scaling extrapolation. read the letter →

arxiv 2502.06470 v1 pith:2OGGNQVU submitted 2025-02-10 cs.CL cs.AI

Hieu Minh "Jord" Nguyen This is my paper · ORCID
classification cs.CLcs.AI
keywords theoryofmindlargelanguagemodelsmentalstaterepresentationssafetyrisksprivacyinferencedeceptionmulti-agentsystemsinterpretability
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 paper surveys the evidence on theory of mind in large language models and argues for a three-part picture: LLMs can match human performance on specific ToM tests, that performance is not robust under simple twists, and internal representations of belief states are detectable with linear probes. Because ToM is a tool for predicting and influencing others, the paper argues that if these capabilities strengthen in near-future models, they will amplify existing harms like privacy invasion and enable new ones like sophisticated deception, collusion, and conflict escalation in multi-agent systems. The sympathetic reader should come away with a concrete research agenda: move ToM evaluation from static question-answering into deployment-like settings, and test mitigations such as unlearning, representation steering, and latent adversarial training.

What carries the argument

The load-bearing mechanism is the union of two measurement tools: behavioral ToM benchmarks (false-belief, higher-order reasoning, irony detection) and linear probes trained on LLM residual-stream activations to decode belief states. A linear probe is a simple classifier on internal activations; when it can read the agent's belief from the model's representations, and when steering along that direction changes answers, that is evidence for an internal model of others' minds. The paper uses these tools to establish the empirical pattern, then projects it forward via scaling and prompting results.

What would settle it

A falsifying result would be a longitudinal study of a frontier model family across versions: if ToM benchmark scores and belief-state probe accuracy plateau or drop as models grow, and interactive belief-tracking behavior stays at chance, then the paper's projected advanced-ToM risk scenario loses its empirical basis.

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Extended reading notes

Core claim

The paper's central claim is that LLMs already exhibit genuine but incomplete ToM: on standard false-belief, irony, and higher-order reasoning tasks some models score at or above human levels, while harder benchmarks and trivial adversarial modifications expose brittleness; interpretability studies using linear probes find representational correlates of self/other beliefs, and these representations causally affect performance when steered. From this evidence the paper derives a risk thesis: advanced ToM in LLMs is a double-edged capability that magnifies user-facing risks such as demographic inference and social engineering and enables multi-agent risks such as steganographic collusion, exploitation, and conflict escalation. The survey therefore frames ToM evaluation and mitigation as urgent safety problems rather than purely cognitive benchmarks.

Load-bearing premise

The risk argument depends on the assumption that the ToM gains seen with larger models and prompting tricks will continue into near-future systems and will transfer from benchmarks to real interactions.

Editorial extensions

If this is right

  • Static question-answering ToM benchmarks understate capability, so evaluation should move to interactive, multi-turn, and scaffolded scenarios resembling real deployment.
  • Privacy-preserving text anonymization will not protect users if models can infer beliefs, preferences, and traits from dialogue patterns.
  • Multi-agent safety frameworks must treat collusion, steganography, and conflict escalation as first-class problems, since aligning individual agents does not align interacting agents.
  • Mitigations such as unlearning, activation and representation engineering, and latent adversarial training deserve systematic study because they target the internal representations the survey finds.
  • The observed ToM gains from scaling and prompting are plausible enough to warrant precautionary research, even while current models remain brittle.

Reading between the lines

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

  • If belief states really are linearly encoded in the residual stream, then steering or removing those directions is a more direct mitigation than the paper's passing mention of activation engineering implies; a controlled test would be to ablate the belief-state direction and measure whether deception and privacy-inference behavior drop while general reasoning stays intact.
  • The benchmark evidence suggests a performative account of LLM ToM: a testable prediction is that accuracy on false-belief questions will collapse under innocuous paraphrases or role swaps even when logical content is identical, which would indicate scattered task-specific routines rather than a unified theory of mind.
  • The risk framing implicitly assumes that ToM improvements transfer to deployment; a cheap early warning would be to run the same models on interactive, multi-turn belief-tracking tasks, since interactive performance lagging static benchmarks would lower the urgency of ToM-specific mitigation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This paper surveys the empirical literature on Theory of Mind (ToM) in large language models (LLMs) across three areas: behavioral evaluation, internal representation, and safety risks. It argues that LLMs can match human performance on specific ToM tasks, that their ToM remains limited and non-robust, and that internal representations of belief states suggest emerging cognitive capabilities. The paper then describes user-facing and multi-agent safety risks that could arise from advanced LLM ToM, and closes with brief suggestions for evaluation and mitigation.

Significance. If the synthesis is accepted, the paper provides a useful, compact overview of a rapidly evolving area and performs a service by connecting ToM research to concrete safety concerns. Its balanced treatment of behavioral evidence—acknowledging both successes and failures on hard benchmarks—is accurate and well supported by citations. The representational and safety discussions, however, go beyond the cited evidence in two ways: linear probe results are described as evidence of 'genuine' ToM, and the safety-risk scenarios rely on a largely unsupported extrapolation that current ToM capability gains will continue. As a position-style survey rather than a systematic review, it is a reasonable contribution, but the central safety argument needs to be reframed as explicitly conditional on speculative capability growth.

major comments (3)
  1. [Future Developments] The sentence 'This trend seems likely to continue in the future (Sutton 2019)' is not supported by the citations that precede it. van Duijn et al. (2023) is a cross-sectional comparison of eleven models against 7–10 year-old children, not a scaling curve, and Wilf et al. (2024) demonstrates prompt-based improvements on a limited set of ToM tasks. Given the paper's own documentation of failures on BigToM, FANToM, OpenToM, Hi-ToM, and ToMBench, and the trivial adversarial failures in Shapira et al. (2024) and Ullman (2023), the paper needs to provide an argument for why these limitations will be overcome and why benchmark gains will transfer to deployment contexts before the advanced-ToM risk scenarios are presented as urgent. Please either supply direct evidence of a ToM-specific scaling trend or reframe the risk section as explicitly conditional on speculative future capabilities and soften the conclusion accordingly.
  2. [Interpreting ToM in LLMs] The claim that probe results provide 'evidence for genuine LLM ToM capabilities' overstates the cited findings. Zhu, Zhang, and Wang (2024) and Bortoletto et al. (2024) show that belief states are linearly decodable from LLM activations, but this establishes representational correlates, not that the model reasons using these representations or that they are causally involved in task performance. The later hedge in the same section—'suggest emerging cognitive capabilities'—is more appropriate. The paper should add a sentence distinguishing representational evidence from causal/reasoning evidence and note common limitations of linear probing, such as sensitivity to prompt surface features.
  3. [Safety Risks from Advanced ToM] The risk analysis mixes demonstrated harms in current models with hypothetical harms that depend on future capability gains. For example, the privacy risk of inferring demographics and other author characteristics is already demonstrated by Staab et al. (2024) and Chen et al. (2024a), whereas the extension to 'beliefs, preferences, and tendencies' is speculative and rests on the extrapolation in 'Future Developments'. Similarly, the collusion examples in Motwani et al. (2024) and Mathew et al. (2024) concern current LLM agents, not necessarily advanced ToM. The paper should explicitly label which risks are empirically demonstrated, which are projected, and which are conditional on capability growth; it should also define 'advanced ToM' concretely (e.g., robustness to adversarial perturbations and generalization to deployment settings) so that the risk claims are falsifiable.
minor comments (6)
  1. [Introduction] The phrase 'humans performance' should read 'human performance'.
  2. [Title/arXiv metadata] The word 'Evaluations' is rendered as 'Evaluati ons' in the arXiv header; this typo should be corrected.
  3. [Empirical Landscape] The sentence describing GPT-4 as 'comparable to 7-10 year-old children' is imprecise because van Duijn et al. (2023) report model-specific and test-specific results; please specify which models and tests achieve this level.
  4. [Future Developments] The reference to Sutton (2019) is a non-archival blog post; the manuscript should identify it as such when using it to support a capability-trend claim.
  5. [Safety Risks from Advanced ToM] The paper cites Kran et al. (2025), a work co-authored by the author of this manuscript; this self-citation should be disclosed according to common transparency guidelines.
  6. [References] A few references are incomplete: 'Liquid.ai. 2024' lacks author and venue information, and 'Switzky. 2020' is missing the author's first initial; please complete these entries.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the survey synthesizes external benchmarks and does not derive new results from its own inputs; the only self-citation is non-load-bearing.

full rationale

This is a survey paper. Its central claims ("LLMs can match humans performance on specific ToM tasks," "LLM ToM remains limited and non-robust," and "internal ToM representations suggest emerging cognitive capabilities") are presented as summaries of external work (van Duijn et al. 2023; Strachan et al. 2024; Zhu, Zhang, and Wang 2024; etc.), not as derivations from a model fitted by the paper. There are no equations, fitted parameters, or benchmarks constructed by the paper, so no prediction reduces to an input by construction. The one self-citation, Kran et al. 2025 (DarkBench), appears in a secondary discussion of unintended anthropomorphism: "ToM capabilities might be leveraged by an LLM or LLM developers to build unwarranted user trust, encourage emotional attachment, or exploit psychological vulnerabilities (Switzky 2020; Kran et al. 2025)." It is one of two citations in that risk example and is not load-bearing for the paper's main evaluation synthesis or safety argument. The "Future Developments" section's extrapolation ("This trend seems likely to continue in the future (Sutton 2019)") is weakly supported and arguably speculative, but an unsupported extrapolation is not circularity: the cited trend is an external claim, not an output of this paper's own construction. Accordingly, no circular step is identified.

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

The paper is a narrative survey with no new experiments. Its conclusions rest on the trustworthiness of cited studies and on an extrapolation about future LLM capabilities.

assumptions (3)
  • domain assumption Validity and faithful interpretation of cited empirical studies
    The survey's conclusions about LLM ToM performance and safety risks inherit the correctness of the cited experiments (e.g., van Duijn et al. 2023; Street et al. 2024; Staab et al. 2024). No independent verification is performed.
  • domain assumption Linear probes of activations indicate genuine belief-state representations
    The representational evidence for "internal ToM" relies on the assumption that decoding belief states with linear classifiers constitutes evidence of genuine mental-state modeling, an interpretation that is contested.
  • ad hoc to paper Scaling and architectural progress will continue to improve LLM ToM
    In "Future Developments", the paper assumes that observed scaling and prompting gains will persist into advanced ToM, which is load-bearing for the urgency of the safety risks.

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Cite this review

Pith. "Pith review of A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks." pith.science (2026). https://pith.science/paper/2OGGNQVU

@misc{pith2026250206470,
  author       = {Pith},
  title        = {Pith review of: A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2OGGNQVU}},
  note         = {Machine review of arXiv:2502.06470}
}
read the original abstract

Theory of Mind (ToM), the ability to attribute mental states to others and predict their behaviour, is fundamental to social intelligence. In this paper, we survey studies evaluating behavioural and representational ToM in Large Language Models (LLMs), identify important safety risks from advanced LLM ToM capabilities, and suggest several research directions for effective evaluation and mitigation of these risks.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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Reference graph

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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.