DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories
Pith reviewed 2026-05-10 00:08 UTC · model grok-4.3
The pith
LLMs identify mental states in dialogue but mostly fail to forecast how conversations will unfold from those states.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
DialToM reveals a clear asymmetry: large language models can accurately extract mental-state profiles from dialogue turns, yet the same models (except Gemini 3 Pro) cannot reliably select the state-consistent future trajectory when given only those profiles, and the semantic content of their inferences diverges from human judgments.
What carries the argument
Prospective Diagnostic Forecasting, a multiple-choice task that supplies only a mental-state profile and asks the model to choose which of several possible dialogue continuations is consistent with those states.
If this is right
- Current LLM ToM capabilities remain largely diagnostic rather than predictive.
- Only a subset of frontier models can translate identified mental states into forward simulation of dialogue.
- Semantic divergence between human and model inferences suggests different internal representations of social context.
- The benchmark supplies a concrete yardstick for measuring whether future training methods close the literal-to-functional gap.
Where Pith is reading between the lines
- Training objectives that reward only next-token accuracy may never produce robust functional ToM without explicit trajectory-level supervision.
- Dialogue agents that must anticipate user reactions would need separate modules or fine-tuning beyond standard instruction tuning.
- If the asymmetry persists across domains, it limits the reliability of LLM-based social simulation tools such as negotiation or therapy assistants.
Load-bearing premise
The multiple-choice options and human verification process truly require models to reason from mental states rather than exploit surface patterns or dataset regularities.
What would settle it
Construct a new test set in which correct trajectory choices require genuine state reasoning while surface cues point to the wrong answer; if models still succeed at the same rate, the functional-ToM claim is falsified.
Figures
read the original abstract
We introduce DialToM, an annotated Theory of Mind (ToM) benchmark built from naturalistic human-human dialogues using a multiple-choice evaluation framework. Concurrent with recent work showing a gap between explicit mental-state inference and applied ToM in synthetic settings~\cite{gu2024simpletom}, we establish a stricter \emph{State-Driven Diagnostic Probe} in which models must forecast state-consistent dialogue trajectories solely from isolated mental-state profiles without dialogue context. Our evaluation reveals a systematic reasoning asymmetry -- LLMs excel at inferring mental states (Literal ToM) but struggle to leverage them for social forecasting (Functional ToM). Crucially, a domain expert achieves 100\% accuracy on this task, proving its validity and establishing a stark human-AI capability gap. Further, a teacher-student reasoning injection probe shows that Gemini 3 Pro -- which establishes the leading baseline -- possesses robust Functional ToM capabilities for context-free forecasting that are transferable to weaker models. DialToM, its evaluation code, and dataset are publicly available at https://github.com/Stealth-py/DialToM.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript introduces DialToM, a human-verified benchmark for evaluating Theory of Mind (ToM) in large language models (LLMs) using natural dialogue data. It distinguishes Literal ToM (mental state prediction) from Functional ToM (forecasting dialogue trajectories from mental state profiles) via a multiple-choice Prospective Diagnostic Forecasting task. Key findings include strong performance on Literal ToM but poor performance on Functional ToM for most models (except Gemini 3 Pro), and weak semantic similarity between human and LLM-generated inferences. The dataset and code are released publicly.
Significance. If the reported asymmetry between Literal and Functional ToM holds, this work would be significant for highlighting limitations in LLMs' ability to apply mental state understanding to predict social interactions, with implications for conversational AI systems. The public availability of the DialToM dataset and evaluation code is a notable strength that supports reproducibility and further research in the field.
major comments (3)
- [Methods (Prospective Diagnostic Forecasting)] The multiple-choice setup for forecasting state-consistent trajectories does not include controls or ablations to rule out selection based on surface features, lexical overlap with the mental-state profiles, or general dialogue priors rather than genuine state-driven inference. This is central to the asymmetry claim, as the skeptic's concern about option patterns remains unaddressed.
- [Results] The exception noted for Gemini 3 Pro is presented without accompanying error analysis or qualitative comparison showing how it differs from other models in leveraging the profiles, making it difficult to confirm that its success reflects superior functional ToM rather than better artifact handling.
- [Evaluation of inferences] The claim of only weak semantic similarities between human and LLM inferences requires more detail on the measurement method (e.g., embedding model used, similarity metric) and statistical significance to assess its robustness and implications for state representation differences.
minor comments (2)
- [Abstract] The abstract mentions 'significant reasoning asymmetry' but does not specify the magnitude or statistical tests used; consider adding a brief quantitative summary.
- [Dataset construction] Ensure that the human verification process is described with inter-annotator agreement metrics to strengthen claims of benchmark quality.
Simulated Author's Rebuttal
We thank the referee for the constructive and detailed feedback. The comments highlight important areas for strengthening the evidence behind our claims of a Literal-Functional ToM asymmetry. We have revised the manuscript to incorporate additional controls, error analysis, and methodological details as outlined below.
read point-by-point responses
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Referee: [Methods (Prospective Diagnostic Forecasting)] The multiple-choice setup for forecasting state-consistent trajectories does not include controls or ablations to rule out selection based on surface features, lexical overlap with the mental-state profiles, or general dialogue priors rather than genuine state-driven inference. This is central to the asymmetry claim, as the skeptic's concern about option patterns remains unaddressed.
Authors: We agree that the absence of explicit controls leaves open the possibility that models exploit surface-level cues rather than performing state-driven inference. In the revised manuscript we have added three ablations to the Prospective Diagnostic Forecasting task: (1) a shuffled-profile control that randomly permutes the mental-state descriptions while keeping the same option set, (2) a lexical-overlap baseline that selects the trajectory option with highest unigram overlap to the profile, and (3) a no-profile control that supplies only generic dialogue priors. Results show that model accuracy drops to near-chance levels under the shuffled and lexical controls, while the original profile-conditioned setting yields the reported performance gap. These controls are now described in the Methods section and reported in a new table in Results. revision: yes
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Referee: [Results] The exception noted for Gemini 3 Pro is presented without accompanying error analysis or qualitative comparison showing how it differs from other models in leveraging the profiles, making it difficult to confirm that its success reflects superior functional ToM rather than better artifact handling.
Authors: We acknowledge that simply noting Gemini 3 Pro's higher accuracy is insufficient. We have added an error-analysis subsection that categorizes failures across all models (e.g., ignoring specific mental-state cues, defaulting to high-frequency dialogue patterns). We also include qualitative examples in the appendix contrasting Gemini's correct forecasts—which explicitly reference profile elements such as “the speaker’s desire to avoid conflict”—with other models’ selections that align with surface statistics. These additions appear in the revised Results and Appendix. revision: yes
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Referee: [Evaluation of inferences] The claim of only weak semantic similarities between human and LLM inferences requires more detail on the measurement method (e.g., embedding model used, similarity metric) and statistical significance to assess its robustness and implications for state representation differences.
Authors: We have expanded the relevant section to specify: (a) the embedding model (sentence-transformers/all-MiniLM-L6-v2), (b) the similarity metric (cosine similarity on mean-pooled embeddings), and (c) statistical tests (one-sample t-tests against a random-inference baseline, with reported p-values < 0.001). The weak similarity result remains robust under these details, and we now discuss its implications for divergent internal state representations between humans and LLMs. revision: yes
Circularity Check
No circularity: empirical benchmark evaluation without derivations or self-referential reductions
full rationale
The paper introduces DialToM as a human-verified multiple-choice benchmark for Literal ToM (mental state identification) and Functional ToM (forecasting state-consistent trajectories) using natural dialogues. All claims rest on direct empirical results from evaluating LLMs on this dataset, with no equations, parameter fittings, ansatzes, or derivation chains present. The asymmetry finding and weak semantic similarity observations are reported outcomes of the evaluation protocol rather than outputs derived from prior fitted values or self-citations. The benchmark construction and human verification steps are described as independent of the model results, rendering the work self-contained against external data without any load-bearing reduction to its own inputs.
discussion (0)
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