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REVIEW 4 major objections 6 minor 44 references

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Attention heads in multimodal large language models encode other agents' beliefs in linearly decodable directions, and steering those directions at inference time improves Theory of Mind performance.

desk verdict A useful new multimodal ToM dataset, but the final-token probing setup and an appendix labeling swap leave the headline interpretability claim unestablished. read the letter →

arxiv 2506.14224 v1 pith:ABMCKEMY submitted 2025-06-17 cs.AI

Xinyang Li , Siqi Liu , Bochao Zou , Jiansheng Chen , Huimin Ma This is my paper
classification cs.AI
keywords theoryofmindmultimodallargelanguagemodelsattentionheadprobinglinearprobesinference-timeinterventionbeliefreasoninggridworldbenchmarkinterpretability
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 sets out to show that multimodal large language models do not merely answer Theory of Mind questions by pattern matching: they build internal, linearly decodable representations of what each agent believes. To test this, the authors build GridToM, a 2-D grid-world dataset of 1,296 video-text pairs with true-belief and false-belief stories, controlled door-based perception, and first- and second-order belief questions. Probing attention-head activations with logistic regression reveals many heads whose outputs separate true from false beliefs, clustered by perspective, and the probe weight vectors act as steering directions: adding $\alpha$ times the standard deviation along those directions during inference lifts belief-reasoning accuracy on both tested open models. The paper reads this as evidence that ToM-like reasoning is realized in attention structure and can be enhanced without fine-tuning.

What carries the argument

The load-bearing object is the attention-head activation and its probe-derived direction. For each head at each layer, the activation at the final token position is fed to a logistic-regression probe whose label is whether the protagonist's belief matches ground truth; heads whose probes exceed a threshold are the model's belief-sensitive heads, and their probe weight vectors supply the intervention direction $\theta^h_l$. The intervention modifies the residual update as $T_{l+1} = T_l + \sum_h (\mathrm{Attn}^h_l(P^h_l T_l) + \alpha\sigma^h_l\theta^h_l)W^o_l$, scaling the direction by $\alpha$ standard deviations after attention and before the output projection. The GridToM dataset is the controlled setting that makes this possible: door open and closed states determine whether the protagonist sees critical events, giving clean perspective-dependent labels.

What would settle it

Re-derive the ground-truth belief for a sample by simulating the caption and video (the protagonist's door state and the white agent's path determine the belief); if the published example's pairing (closed door, belief in red equals 'True Belief'; open door, belief in purple equals 'False Belief') appears in the actual dataset rather than being a typo, the TB/FB labels are inverted and the reported accuracy gap and probe labels would reverse.

Watch

Extended reading notes

Core claim

The central claim is that attention heads in MLLMs carry the model's representation of who knows what: linear probes trained on head activations classify whether a belief is true from the protagonist's perspective and from an omniscient perspective, with many heads reaching high accuracy and forming four cleanly separated clusters (true/false times protagonist/omniscient). In the paper's own terms, this shows that MLLMs develop intermediate representations reflecting multi-perspective information extraction and belief inference, and that these implicit beliefs are linearly decodable. The same probe directions are then reused as interventions: after computing attention in a selected head, the model adds $\alpha\sigma\theta$, where $\theta$ is the probe weight vector and $\sigma$ the activation standard deviation along it, which shifts reasoning toward the protagonist's perspective. On LLaVA-Next-Video-7B and Qwen2-VL-7B, this training-free steering improves first-order true-belief accuracy from 53.2% to 63.8% and 26.6% to 60.4%, respectively, with similar gains on second-order beliefs and on the joint TB-and-FB metric.

Load-bearing premise

The load-bearing premise is that GridToM's True Belief and False Belief labels are correct: the paper's own Appendix C.2 example labels a closed-door story as True Belief and an open-door story as False Belief, which would invert the protagonist's actual belief if the dataset follows that example, and every probe label and TB-versus-FB accuracy gap in the paper depends on those labels.

Editorial extensions

If this is right

  • If the claim holds, ToM failures shown by closed-source models on question-answering do not prove absence of ToM; models can encode correct belief states internally and still answer wrong.
  • Belief representations being linearly decodable means a training-free, parameter-free intervention can improve ToM behaviour at inference time on open-weight models.
  • The method transfers beyond GridToM: probing on the real-world MMToM-QA dataset also finds perspective-separating heads, so the mechanism is not an artifact of grid-world rendering.
  • The reported TB/FB asymmetry (near-perfect FB, near-chance TB) implies benchmark format and visual-textual alignment, not just reasoning ability, determine measured ToM.
  • Because the intervention works by aligning attention with the protagonist's perspective, it also offers a diagnostic: heads whose probes fire on perspective information can be used to trace when a model is using the wrong agent's viewpoint.

Reading between the lines

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

  • A natural stress test would swap the door state in a fixed story and check whether probe accuracy and intervention gains track the changed ground truth; if they do not, the reported effect may be a dataset artifact rather than a belief-direction mechanism.
  • Because the intervention is a single linear shift, it likely captures only the dominant belief feature; combining multiple probe directions or probing at intermediate tokens could reveal nested second-order belief structure that the current final-token readout misses.
  • The same probe-then-steer recipe could be applied to other social-cognitive dimensions, such as desires or intentions, whenever a dataset supplies perspective-controlled labels; GridToM's ATOMs-style design is a template for constructing them.
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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

4 major / 6 minor

Summary. The paper presents GridToM, a new multimodal theory-of-mind benchmark built on a 2D grid world, with videos and text annotations covering first- and second-order true/false belief tasks. It evaluates several MLLMs and LLMs on GridToM, reports human accuracy, then probes attention-head activations with logistic regression classifiers trained on belief-correctness labels (Yp/Yo). Selected attention heads are used in a training-free intervention (Eq. 4) that adds a scaled probe direction to attention outputs, and the paper reports accuracy gains on GridToM for LLaVA-Next-Video and Qwen2-VL. The paper also probes the MMToM-QA dataset as an external generalization check. The main claims are that MLLMs linearly encode belief states across perspectives and that steering along probe-derived directions improves ToM behavior.

Significance. If the claims hold, the paper would be a useful contribution: GridToM provides a controlled, perceptually grounded multimodal ToM benchmark with explicit perspective annotations, which is a strength relative to video datasets without ground-truth perception; the multi-layer attention-head probing offers a concrete interpretability method; and the inference-time intervention is lightweight and does not require fine-tuning. The inclusion of human baselines and multiple MLLMs/LLMs is also a strength. However, as written the core interpretability and intervention claims are not yet established because of the TB/FB label inconsistency, the absence of control probes, and unreported intervention hyperparameters.

major comments (4)
  1. [Appendix C.2, Figure 11; Section 3.1] There is a direct inconsistency between the dataset definitions and the example annotations. Section 3.1 and Section 5.1 define TB as the protagonist having full visibility (door open) and FB as limited visibility (door closed). However, in the 'True Belief' block of Figure 11 the yellow agent closes the green door and 'cannot see anything happening outside the green room', so the yellow agent's belief ('red') differs from the omniscient truth ('purple'); this is a false belief by the paper's own definition, not a true belief. The 'False Belief' block has the door open and the belief matching the omniscient end state. If the dataset follows Figure 11, the TB/FB labels are inverted and the Table 1 comparison (e.g., ChatGPT4O 6.2% TB vs 100% FB) would have the opposite interpretation; if Figure 11 is a typo, the ambiguity still makes the benchmark and the probe labels (which use these TB/FB distinctions) unreliable. This must be resolved by correcting the figure/annotations and by releasing a label audit with the dataset.
  2. [Section 4.2, Section 4.3, Appendix B.1, Figure 5(A)] The probing targets are not specified consistently. Section 4.2 defines both Yp and Yo and then states 'we only use the correct and incorrect labels from the protagonist's perspective', while Appendix B.1 (Figure 6) assigns four distinct label combinations (Yp,Yo) for TB and FB and describes different extraction strategies for first- and second-order beliefs. In addition, Figure 5(A) captions say 'on the test set' but Section 4.3 says the figure shows 'validation accuracy', and Section 4.4 says the top K heads are selected on the validation set. The paper needs to state exactly which labels and which data split were used to train the probes, to select heads, and to produce the reported accuracies; using test-set accuracy for head selection would make the reported probe accuracies optimistically biased.
  3. [Section 4.3, Eq. (4), Figure 5(C)] The confound control is missing for both the representational and the intervention claims. The probes are trained on activations extracted at the final token of the prompt, immediately before the model produces a two-choice answer, and the labels are the correctness of that answer in GridToM. Because in GridToM the correct answer color is fully determined by whether the protagonist saw the white agent's relocation, a linear probe can achieve high accuracy by decoding the answer color, the door state, or another surface feature, without encoding any belief state; the four separated clusters in Figure 5(C) would also arise if the probe read off the correct option. The intervention of Eq. (4) then amplifies exactly this direction and is evaluated only on the same GridToM benchmark, which is compatible with answer steering rather than with enhanced ToM. The MMToM-QA generalization probe in Appendix H uses the same final-token activations and text-only labels and therefore does not remove the confound. The paper should include control probes (e.g., decoding answer color or door state, probing with shuffled labels) and an evaluation of the intervention on a held-out or adversarially modified set of scenarios.
  4. [Table 1, Section 5.1, Appendix E] The intervention results in Table 1 are not reproducible as reported. Eq. (4) introduces the scale α and Section 4.4 defines the number of selected heads K, but the '+α' rows in Table 1 do not state the values of K and α used, nor whether the protagonist-perspective or omniscient-perspective direction was used for each model and task. Appendix E provides parametric plots but not the exact settings that produced the headline gains (e.g., LLaVA +21.2 on first-order Both and Qwen2-VL +33.8 on first-order TB). Please report the exact K, α, and direction for every reported row, and include a sensitivity table so the reader can verify that the chosen hyperparameters are inside the stable region.
minor comments (6)
  1. [Abstract, Section 5.2] The phrase 'adjusting in the direction of the attention head' is vague; please specify that the intervention shifts activations along the probe weight direction of the selected attention heads.
  2. [Section 3.1, Appendix C.1] Section 3.1 says each video has 'approximately 40 frames', while Figures 8 and 9 show 36-frame sequences; please unify the frame counts.
  3. [Throughout] Several typos need correction: 'GirdToM' in Table 1 and elsewhere, 'interference' for 'intervention' in Section 5.1 and Appendix E, 'demonstreted' and 'alse' in Section 5.1, and 'leds' in the Introduction.
  4. [Eq. (1)] In Equation (1), 'the n-th head' should be 'the h-th head', and the notation P_h^l is unclear; please define it explicitly as the projection matrix for head h at layer l.
  5. [Appendix E] The claim that α is effective roughly within [-50,50] appears inconsistent with Figure 17, whose x-axis spans [-100,100] for Qwen2-VL; please clarify whether the effective range is model-dependent.
  6. [Section 3.2, Appendix D] Initial-belief accuracy in Table 3 should be accompanied by valid-response rates, since the text notes that DeepSeek-VL2-small achieved 5.9% accuracy with 89.9% invalid responses; reporting accuracy alone is misleading without this context.

Circularity Check

2 steps flagged · score 6.0 of 10

The 'ToM evidence' probe and the 'ToM enhancement' steering both reduce to fitting and reapplying a decision boundary over GridToM's answer-correctness labels at the answer-position token, with no external intervention benchmark or control probe.

  1. fitted input called prediction [Section 4.4, Eq. (4); results in Section 5.1, Table 1; interpretation in Section 5.2; labels defined in Section 4.2]
    "We then intervene on these selected heads ... as computed as follows: Tl+1 = Tl + Σh(Attnh l (P h l Tl) +ασh l θh l) · W o l , (4) where σh l denotes the standard deviation of activations along the target direction, and θh l represents the intervention target direction, derived from the weight vector of the selected attention head. ..."

    Eq. (4)'s direction θ is 'derived from the weight vector of the selected attention head' — the logistic-regression boundary fit in Eq. (3) to Yp/Yo, which Section 4.2 defines as 'the correctness of the protagonist's perspective and the omniscient perspective.' In GridToM each belief statement is one of the two answer options, so Yp true/false is, by construction, the benchmark's answer correctness. Because the probe reads 'the activation at the final token position,' immediately before the answer is generated, the fitted boundary can separate correct from incorrect answer directions. Eq. (4) adds ασθ along that same boundary, and Table 1 reports TB/FB gains on the same GridToM benchmark, called in Section 5.2 'significant enhancement of ToM capabilities ...

  2. self definitional [Sections 4.2-4.3, Figure 5; abstract claim of ToM evidence; generalization probe in Appendix H]
    "We extract the output of each attention head at every layer, capturing the activation at the final token position, denoted as X ∈ R^{L×H×D}. Each attention head activation is associated with belief labels Yp and Yo, which represent the correctness of the protagonist's perspective and the omniscient perspective, respectively. ... This suggests that MLLMs indeed develop intermediate representations reflecting multi-perspective information extraction and belief inference."

    Figure 5A ('validation accuracy of the linear probe') is presented as evidence that 'attention heads in multimodal large models can distinguish cognitive information across perspectives.' The probe target Yp/Yo is defined as 'the correctness of the protagonist's perspective and the omniscient perspective,' and in GridToM each belief statement is one of the two answer options, so Yp is, by construction, the benchmark's answer correctness. The probe input is 'the activation at the final token position' — exactly where the answer distribution is formed — so a linear probe can separate the labels by decoding the answer option or a correlated surface feature (the door open/closed state fixes the correct color), without encoding any belief state.

full rationale

Derivation chain: GridToM's TB/FB answer labels define Yp/Yo (Section 4.2); logistic probes are fit to those labels on final-token attention outputs (Eq. 3, Section 4.3); top-K heads are selected by validation accuracy; the probe weight becomes the intervention direction θ (Eq. 4); accuracy gains on the same GridToM test set (Table 1) are reported as 'significant enhancement of ToM capabilities' (Section 5.2). The loop closes because the quantity fit (belief-statement correctness, which is the benchmark's answer correctness) is reused as both the steering direction and the evaluation metric; the reported gain is the expected consequence of moving activations across a boundary trained to separate correct from incorrect answers on the same distribution. This is partial circularity (score 6): the two central new claims — linearly decodable belief evidence and training-free ToM enhancement — reduce, absent controls, to answer-option decoding and reapplication of the fitted boundary. Independent content does exist: GridToM is a separately constructed dataset; the zero-shot baselines in Table 1 are standard benchmark results; probing and activation steering are established external tools (Li et al. 2024 is cited, not self-citation); and the MMToM-QA probing provides some cross-dataset evidence, though under the same final-token, binary-label design. No load-bearing self-citation or imported uniqueness theorem appears. Separately, a data-integrity risk: Appendix C.2, Figure 11 appears to invert the TB/FB definitions stated in Section 3.1 and C.1 — the 'True Belief' block has the yellow agent close the green door, cannot see the white agent move red-to-purple, yet answers 'red', which is a false-belief story; the 'False Belief' block has the door open, the yellow agent watching the move, answering 'purple', which is a true-belief story. This is a correctness risk rather than a circular step, but it underscores that the probe labels are inseparable from the benchmark's own (possibly inconsistent) answer labels. The paper's limitation paragraph (Section 5.2) does not disclose the probing confound, the lack of control probes, the intervention's confinement to GridToM, or the label mismatch. With explicit controls (shuffled-label probes, door-state and answer-option directions, and an external intervention benchmark) the claims could be substantiated; as written, they are not self-contained against the confound.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The probing and intervention rest on the dataset labels being correct, on the final token being the right readout position, and on the probe direction being a valid steering direction. The TB/FB label inconsistency in Appendix C.2 makes the first axiom fragile. No new entities are introduced; K and alpha are the main free parameters and are not reported for the headline results.

free parameters (2)
  • intervention scale alpha = not specified for Table 1 results
    Controls the strength of the activation shift in Eq. (4). Appendix E sweeps alpha over a range; the values used for the headline results in Table 1 are not reported.
  • number of top heads K = not specified for Table 1 results
    Number of attention heads selected by validation probe accuracy and steered at inference. Appendix E shows K=16/32/64 for LLaVA and 14/28/56 for Qwen2-VL; the values used in Table 1 are not stated.
assumptions (4)
  • domain assumption The final token position's attention-head activation captures the belief-relevant state of the sequence.
    Section 4.2 states activations are extracted 'at the final token position' for probing; the paper provides no analysis of positional dependence.
  • domain assumption In TB scenarios, the protagonist's perceptual information is equivalent to omniscient information, allowing the omniscient video to be substituted for the protagonist's perspective video.
    Section 4.2; this substitution underlies the Yp label construction for TB and is load-bearing for the probe design.
  • ad hoc to paper The linear probe's decision boundary can be used as a steering direction that improves belief accuracy.
    Eq. (4) adds alpha * sigma * theta, where theta is the probe weight; this is the paper's proposed mechanism, imported from Li et al. (2024) and assumed to transfer to multimodal ToM.
  • domain assumption The 2D gridworld with door open/closed manipulations accurately instantiates true and false beliefs as defined by the classic unexpected-transfer task.
    Section 3.1 and Appendix I describe the design; the correctness of this mapping is exactly what the Figure 11 inconsistency calls into question.

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Pith. "Pith review of From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models." pith.science (2026). https://pith.science/paper/ABMCKEMY

@misc{pith2026250614224,
  author       = {Pith},
  title        = {Pith review of: From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ABMCKEMY}},
  note         = {Machine review of arXiv:2506.14224}
}
read the original abstract

As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for evaluating machine ToM focus primarily on unimodal models and largely treat these models as black boxes, lacking an interpretative exploration of their internal mechanisms. In response, this study adopts an approach based on internal mechanisms to provide an interpretability-driven assessment of ToM in multimodal large language models (MLLMs). Specifically, we first construct a multimodal ToM test dataset, GridToM, which incorporates diverse belief testing tasks and perceptual information from multiple perspectives. Next, our analysis shows that attention heads in multimodal large models can distinguish cognitive information across perspectives, providing evidence of ToM capabilities. Furthermore, we present a lightweight, training-free approach that significantly enhances the model's exhibited ToM by adjusting in the direction of the attention head.

Figures

Figures reproduced from arXiv: 2506.14224 by the authors.

Figure 1
Figure 1. This illustration highlights the integration of different levels of ToM: recognizing an agent’s desire (Cooper wants to pilot), a first-order belief (he believes he can do it), and a second-order belief (he believes TARS perceives it as risky). These nested mental states are crucial in evaluating advanced ToM. interpretability-oriented methodologies (Mao et al., 2024). However, multimodal large language models (MLLM… view at source ↗
Figure 2
Figure 2. The figure highlights the limitations of current ToM eval￾uations, namely that other model capabilities (such as hallucination and scenario understanding) may interfere with the results. In summary, our main contributions are as follows: (1) we introduce GridToM, a novel multimodal ToM dataset that in￾corporates diverse belief-testing tasks alongside perceptual information from multiple perspectives; (2) we conduct … view at source ↗
Figure 3
Figure 3. A FB sample in GridToM includes an omniscient-perspective video covering the entire event timeline, along with omniscient￾perspective textual descriptions for each time interval. For all agents involved in the event, we provide full physical perspective information across the timeline. When an agent closes a door, we mask its perception of any information beyond the door to simulate realistic sensory limitations. Ea… view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: Overview of Our Workflow. We first constructed the GridToM dataset and conducted benchmark testing of MMLMs on it. Subsequently, we input video-text pairs to probe the internal at￾tention representations of the models. Using logistic regression, we performed binary cla…
Figure 5
Figure 5. Figure 5: (A) The linear probing accuracy of all heads across all layers in LLaVA-Next-Video on the test set. The x-axis represents the heads, and the y-axis represents the layers. Dark green indicates higher accuracy, with 50% serving as the baseline accuracy for random guessin…
Figure 6
Figure 6. Figure 6: In the attention feature extraction process for first-order belief tasks, the information obtained from the omniscient and protagonist perspectives is consistent in the TB task. We identify belief-reasoning-sensitive features in attention by comparing correct and incor…
Figure 7
Figure 7. Figure 7: In the attention feature extraction process for second-order belief tasks, both the TB and FB tasks include the TB and FB tasks from first-order belief tasks. Unlike first-order belief tasks, the FB task in second-order belief reasoning contains the participant’s incor…
Figure 8
Figure 8. Figure 8: (A) The video frames from the omniscient perspective (36 frames in total) in TB test are shown in the figure. (B) The video frames from the participant’s perspective (36 frames in total) in TB test are shown in the figure. (C) The video frames from the protagonist’s pe…
Figure 9
Figure 9. Figure 9: (A) The video frames from the omniscient perspective (36 frames in total) in FB test are shown in the figure. (B) The video frames from the participant’s perspective (36 frames in total) in FB test are shown in the figure. (C) The video frames from the protagonist’s pe…
Figure 10
Figure 10. Figure 10: The textual annotations for the initial belief task are shown in the figure. First-order belief The concept of first-order belief refers to the direct inferences or reasoning that MLLMs make about the mental states of others, grounded in their observable actions or st…
Figure 11
Figure 11. Figure 11: The textual annotations for the first order belief task in the TB and FB tests are shown in the figure. Second-order belief The concept of second-order belief pertains to the reasoning and inferences that MLLMs make regarding an agent’s beliefs about another agent’s m…
Figure 12
Figure 12. Figure 12: The textual annotations for the second order belief task in the TB tests are shown in the figure. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: The textual annotations for the second order belief task in the FB tests are shown in the figure. （A） (B) (C) (D) (E) (F) [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: The figure presents video sequence frames extracted from three different rooms in the dataset as examples, where (A)(B), (C)(D), and (E)(F) correspond to different rooms. (A) and (B) illustrate examples of the same room configuration but with different agent states an…
Figure 15
Figure 15. Figure 15: The impact of the hyperparameters K and α on the LLaVA-NeXT-Video-7B-hf model on the First-order TB task. 60 40 20 0 20 40 60 Alpha 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 Accuracy FalseBelief Baseline (+oracle,K=16) (+oracle,K=32) (+oracle,K=64) (+protagonist,K=…
Figure 16
Figure 16. Figure 16: The impact of the hyperparameters K and α on the LLaVA-NeXT-Video-7B-hf model on the First-order FB task. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_16.png]
Figure 17
Figure 17. Figure 17: The impact of the hyperparameters K and α on the Qwen2-VL-7B-Instruct model on the First-order TB task. 40 20 0 20 40 Alpha 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1.00 Accuracy FalseBelief Baseline (+oracle,K=14) (+oracle,K=28) (+oracle,K=56) (+protagonist,K=14) (+pr…
Figure 18
Figure 18. Figure 18: The impact of the hyperparameters K and α on the Qwen2-VL-7B-Instruct model on the First-order FB task. F. Evaluation protocol of baseline test Our objective is to provide MLLMs with complete third-person perceptual information in both visual and textual formats (repr…
Figure 19
Figure 19. Figure 19: Probe accuracies on first-order belief task and second-order belief task based on the attention head activations in all layers of LLaVA-Next-Video. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_19.png]
Figure 20
Figure 20. Figure 20: Probe accuracies on first-order belief task and second-order belief task based on the attention head activations in all layers of Qwen2-VL-7B-Instruct. H. Probing on Different Dataset (MMToM-QA) We further validated the effectiveness of our method on the MMToM-QA data…
Figure 21
Figure 21. Figure 21: Sample examples from the MMToM-QA dataset. The question types utilized in MMToM-QA are also illustrated. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_21.png]
Figure 22
Figure 22. Figure 22: (A) Omniscient. (B) Protagonist. The linear probing accuracy of all heads across all layers in LLaVA-Next-Video on the test set. (C) Insensitive. (D) Sensitive. The linear separability of belief representations is explained through a visual interpretation of the typic…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.