Title: AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling
Venue: NeurIPS 2025
Accepted: yes
Keywords: Theory of Mind

Agent Modeling

Bayesian Inverse Planning

Large Language Models

Cognitive Modeling
Abstract: Theory of Mind (ToM), the ability to understand people's minds based on their behavior, is key to developing socially intelligent agents. Current approaches to ToM reasoning either rely on prompting Large Language Models (LLMs), which are prone to systematic errors, or use handcrafted, rigid agent models for model-based inference, which are more robust but fail to generalize across domains. In this work, we introduce *AutoToM*, an automated agent modeling method for scalable, robust, and interpretable mental inference. Given a ToM problem, *AutoToM* first proposes an initial agent model and then performs automated Bayesian inverse planning based on this model, leveraging an LLM backend. Guided by inference uncertainty, it iteratively refines the model by introducing additional mental variables and/or incorporating more timesteps in the context. Across five diverse benchmarks, *AutoToM* outperforms existing ToM methods and even large reasoning models. Additionally, we show that *AutoToM* can produce human‐like confidence estimates and enable online mental inference for embodied decision-making.


Peer reviews:
comments: SUMMARY:
This paper contributes an automated method for combining LLMs and Bayesian Inverse Planning (BIP) for modeling an agent’s Theory of Mind (ToM). This method works by iterating between agent model discovery (proposing a model, extracting the values of observable variables, and potentially adding variables or timesteps) and BIP (proposing hypotheses for each latent mental variable and evaluating the likelihood of the query given the observations). The authors show this method is capable of outperforming other LLM-based ToM methods across a number of ToM benchmarks.

STRENGTHS AND WEAKNESSES:
Strengths:
- Each aspect of AutoToM is well-motivated, explained clearly, and well-evaluated with the ablation studies.
- The quality and significance of this contribution is well supported by the breadth of benchmarks and baselines that are considered.

Weaknesses:
- The benchmark performance results in Table 1 and Figure 4 do not contain any error bars. The improved performance of AutoToM could be better established using multiple runs for each test case.

QUESTIONS:
- Given the definition of the model utility in Eqn. (5), how does AutoToM avoid getting into local optima of models that are both overly simple and certain about the wrong answers to the queries?
- How does the performance of AutoToM vary when using different base LLMs?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces AutoToM, a framework for automating agent model construction and Bayesian inverse planning in Theory of Mind (ToM) inference tasks. Leveraging a large language model (LLM) as a computational backend, AutoToM dynamically selects relevant mental variables and timesteps from context to construct a suitable agent model. It performs iterative model refinement guided by uncertainty-based utility metrics. Experimental results across five diverse ToM benchmarks, cognitive studies, and embodied reasoning tasks show that AutoToM consistently outperforms state-of-the-art ToM methods, including prompting-based and model-based approaches.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. AutoToM addresses a key limitation of existing model-based ToM methods by eliminating manual model specification. Its automated model discovery enables generalization to open-domain and higher-order reasoning tasks.\n2. The framework dynamically selects the appropriate set of mental variables (e.g., goal, belief, observation) and relevant timesteps, making it adaptable to different ToM problems. Figure 2a effectively illustrates the variety of agent models AutoToM can construct.\n3. The paper presents thorough evaluations from multiple perspectives—including accuracy, context length, number of agents, and recursion depth. Qualitative case studies in Sections 4.2 (cognitive plausibility) and 4.3 (embodied assistance) further highlight the practical utility and interpretability of AutoToM.\n\nWeaknesses:\n1. Since AutoToM performs agent model discovery per query, it may incur higher inference latency compared to methods with static models. A comparison of inference time across methods would help contextualize this trade-off.\n2. The paper briefly mentions variable and timestep adjustments are done iteratively during model refinement, but a more detailed explanation or visualization of how the system alternates between these adjustments would improve transparency and reproducibility.\n\nQUESTIONS:\nCost Function Clarification:\nDoes the model complexity cost C(M) include both variable and timestep dimensions? Clarifying this could help readers understand how model parsimony is enforced.\n\nHi-ToM Performance:\nIn Table 1 and Figure 4d, AutoToM appears to underperform on Hi-ToM and high-order mind recursion. What challenges does the framework face in higher-order recursive tasks, and how does it decide when to expand to a higher-order agent model during agent-model automation?\n\nLLM Backend Sensitivity:\nGiven that AutoToM relies on the capabilities of the underlying LLM, it would be helpful to evaluate how the quality of proposed agent models and final inference performance vary across different LLM backends.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces AutoToM, a framework for scalable Theory of Mind (ToM) reasoning that automatically constructs agent models and performs Bayesian inverse planning using LLMs. Unlike prior work that relies on handcrafted model structures, AutoToM discovers relevant variables (e.g., beliefs, goals) and their values dynamically through LLM prompting. The system is evaluated across ToM benchmarks and cognitive studies, showing good performance in both accuracy and human-aligned confidence estimation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper proposes a novel integration of LLM-driven model structure discovery with Bayesian inverse planning.\n- AutoToM achieves state-of-the-art performance across multiple ToM benchmarks with particularly strong gains on classic false-belief tasks.\n- The framework generalizes across diverse tasks and domains, and demonstrates human-like confidence calibration.\n\nWeaknesses:\n- While AutoToM claims to automate model discovery, the underlying causal structure is fixed, limiting the scope of structural generalization.\n- The framework does not learn the variables or their dependencies; instead, it selects and scores hypotheses using LLM outputs without probabilistic grounding.\n- Some of the evaluation gains may stem from prompt-level tuning or LLM scoring behavior, rather than principled modeling.\n\nQUESTIONS:\nTo what extent does AutoToM's performance depend on the specific LLM used? Could smaller models offer similar benefits with prompt engineering?\n\nCan the system be extended to jointly learn both variable structure and causal dependencies, rather than fixing the structure a priori?\n\nLIMITATIONS:\nAutoToM’s inference relies heavily on LLM-generated hypotheses and likelihood approximations, which may introduce bias or inconsistencies. \n\nThe high computational cost (average 27 API calls per question) poses practical limitations for real-time or large-scale deployment.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a method for modeling agents and enable interpretable mental inference called AutoToM. AutoToM proposes and iteratively refines an agent model through Bayesian inference planning, leveraging an LLM for every component. AutoToM outperforms out-of-the-box LLMs and LRMs plus other existing ToM methods on five well-known ToM (often false-belief) benchmarks. Interestingly, AutoToM produces confidence estimates that are much more aligned with humans and shows great applicability in tasks such as embodied decision-making.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper tackles an exciting and important problem: modeling theory of mind without overly imposing structure and thus reducing the applicability of the method, as is often the case with many current methods. The paper demonstrates that AutoToM achieves this by simultaneously exhibiting strong performance on ToM benchmarks, while also evaluating beyond ToM benchmarks (embodied decision-making; human uncertainty comparison).\n- Generally strong set of experiments! Some remaining concerns below.\n\nWeaknesses:\n- The paper could be written more clearly, with for example an example figure that is more easily digestible and representative of the procedure.\n- Some literature should be discussed more deeply, particularly thought-tracing [22] since it shares the same claim of wide-applicability in ToM modeling using a Bayesian approach, and yet it is clumped with more structured methods like SymbolicToM [34]. It would be beneficial to clearly discuss the differences with AutoToM and benefits of the present approach! Moreover, it would be important to include other types of approaches in related work, such as training LLMs specifically for ToM, as shown by e.g. ExploreToM (Sclar et al., 2024) or Textual Time Travel (Arodi and Cheung, 2021). \n- Would be good to add a token cost vs performance discussion: this is already included in an ablation, but it would be good to know the cost of AutoToM versus other methods.\n- Some experimental concerns: comparing AutoToM with thought-tracing, I see some very different baseline accuracies being reported, such as MMToM-QA GPT-4o (44.00 in AutoToM; 56.5 in thought-tracing) and o3-mini. Why are there some big differences? Is it purely a model version issue? Also, would it be possible to add FANToM, since it disguises more the false-belief task than ToMi/Hi-ToM, and according to thought-tracing it seems to be the most challenging?\n\nQUESTIONS:\n(besides the ones in the \"weaknesses\" items above)\n\n- In Figure 4, do you have any insights as of to what cases does AutoToM excels at, and what are issues still to tackle to take e.g. \"Level 2+ Belief\" from 70% -> 100%?\n- Would AutoToM also improve performance for benchmarks that sometimes require affective ToM such as OpenToM?\n- Why do you mention that AutoToM is more *robust*? Specifically, since AutoToM is LLM-based, I assume it'll still be prone to issues such as those reported in ExploreToM (e.g. slightly different story structures triggering ToM issues due to LLMs' flakiness). It would be important to specify exactly robust to what behavior (since increased performance != robustness), or maybe considering not focusing on that claim given the high value in other aspects.\n\nI believe this is overall a strong paper, but I would really appreciate resolving these remaining concerns.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-07"}