AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling
Authors: Zhining Zhang
Chuanyang Jin
Mung Yao Jia
Shunchi Zhang
Tianmin Shu
Venue: NeurIPS 2025 spotlight ()
Keywords: Theory of Mind
Agent Modeling
Bayesian Inverse Planning
Large Language Models
Cognitive Modeling
Primary area: neuroscience_and_cognitive_science
TL;DR: We propose AutoToM, an automated agent modeling method for scalable, robust, and interpretable mental inference.

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.

AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling
Zhining Zhang" Peking University zzn zzn_nzzee mon_nzz@stu. pku. edu. on
Chuanyang Jin Johns Hopkins University cj jin330jhu.edu
Mung Yao Jia"
Johns Hopkins University mj 1a100 jhu.euu edu
Shunchi Zhang" Johns Hopkins University szhan2560jhu. edu
Tianmin Shu
Johns Hopkins University
ti tianmin. shu0juu. edu
Abstract
Theory of Mind (ToM), the ability to understand people's minds based on their be- havior, is key to developing socially intelligent agents. Current approaches to ToM reasoning either rely on prompting Large Language Models (LLMs), which are prone systematic errors, or use handcrafted, rigid agent models for model-basee inference, which are more robust but fail to generalize across domains. In this work, introduce AutoTToM, an automated agent modeling method for scalable, robust, and interpretable mental inference. Given a ToM problem, Auto first proposes 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, Auto ToM 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 pecision-making
Links: Project Page
Code
Introduction
To successfully engage in rich and complex social interactions such as cooperation, communication, and social learning, humans must adequately understand one another's mental states (e.g., goals, beliefs, desires). This ability is termed Theory of Mind (ToM) [49]. Prior works have demonstrated that like human interactions, Theory of Mind also crucial for the success of human-AI interactions
[7 14 (28] To safely and productively interact with humans in an open- ended manner, Al systems need interpret humans' mental states from observed human behavior 45
B1 33 51]
There are two primary approaches to developing machine Theory of Mind in recent works. First, with the rapid progress of large language models (LLMss), there has been increasing interest in directly applying L.MSs to reason about people's 's mental states with prompting strategies such as perspective-taking [48] 37 22), change-tracking [18] and temporal-spatial reasoning [17]. However, even with these advanced prompting techniques, state-of-the- LLMs still make systematic errors in complex scenarios (20). Second, cognitive studies have demonstrated that model-base inference, in particular, Bayesian inverse planning (BIP), can reverse engineer human-like theory of Mind reasoning
52. BIP relies on Bayesian Theory of Mind (BToM) models 3 to approximate rational agent behaviors. Inspired by this, recent works have proposed to combine BIP and LLMs to
"Equal contribution. Project Lead.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Question: ... Sally hides apple inside a box... Where does Anne think the apple is?
Agent Models P(V4e,x507
Bayesian Inverse Planning
P(g)x*=))
Hypothesis SrpollhsiiSSampinng
OF
b
alll
Information Extraction
ar4
P(
Of
b
Lokde Besilican
Initall
:
wnlii low
Hyputhai 1
Hyputhisk
gh-l
04-
b
a+1
Hypoheo 2
ilypatiek
Bayesian Inference
Initial Model Propusal
of
b'
Adjusted
Pollpee x > P(v). (26.,
new model
final result
model utility
P(ff Baskel xi6t) 0.6 P(of Cohineet 13%5
Answer: In the busket.
Model Adjustment
An overview of AutoToM. X are observable variables, V are latent mental variables, and q is the query (in this case, a mental variable vf E Vt). tu denotes timesteps from I, to & in the context that are considered for inference. Variables s", of, br, aa, g" represent state, observation, belief, action, and goal, respectively, with solid arrows indicating dependencies defined in the models. Given a question, we extract the observable variables (information extraction) and propose an initial agent model. This is followed by automated Bayesian inverse planning and iterative model adjustment. When the model utility is high enough, we will produce the final answer based on the inference result.
Figure I:
While these methods significantly outperform LLMs in specific domains, they typically require manual specification of agent models, including necessary mental variables (e.g.. goals, beliefs) for answering a given ToM question. Therefore, they lack the required generalizability for open-ended Theory of Mind.
achieve scalable yet robust model-based ToM inference 20 39
In this work, we aim to develop a fully automated model- Theory of Mind method. That is unified method that can be applied to robustly infer any given mental variable in any domain. Achieving this aim requires addressing two critical questions: (1) How can we ensure that our approach is flexible enough to adapt across contexts, robust enough model diverse human behaviors, and scalable enough to tackle increasingly complex scenarios? (2) How can we avoid manual model specifications and instead automate agent modeling for model-based mental inference?
To address these challenges, we introduce AutoToM, a general framework for model-based Theory of Mind. It automates every aspect of Bayesian inverse planning, including the proposal and adjustment of model structures, the identification of relevant timesteps, the generation of hypotheses, and the execution of Bayesian inference. It is designed operate in any context, infer any mental state, reason about any number of agents, and support any order of recursive reasoning, which represents our vision of an open-ended and robust machine Theory of Mind.
Figure provides an overview of AutoToM,, which consists of two main components: First, Auto- mated Bayesian Inverse Planning conducts Bayesian inference based on any given agent model (in the form of a Bayesian network) using an LLM as a computational backend. Unlike prior works that leverages LLMs for Bayesian inverse planning. it has assumptions about model structure or variable representations. Second, Automated Agent Model Discovery iteratively constructs and adjusts agent model most suitable a given ToM inference problem, eliminating the need for manual model specifications typically required by prior works on model-based ToM inference.
Our main contributions include: (1) a unified formulation of model-based ToM inference; (2) the first approach of automated agent model discovery, AutoTTM,, for scalable model-based ToM; and (3) a systematic evaluation of Auto'ToM on multiple ToM benchmarks, cognitive studies, and embodied assistance tasks. The results show that AutoToM outperforms sfate-of-the -he LLMS and large reasoning models, establishing scalable, robust, and interpretable framework for machine ToM.
2 Related Works
Enhancing L.MSs Theory of Mind. While LLMS remain limited in achieving robust Theory of Mind inference 42
38 [0] recent studies have introduced various prompting techniques to enhance

---

encourages LLMs to adopt perspective- PercepToM [22] improves perception-te to-bolee inference by extracting relevant contextual information, and Huang et al. [[8] employ an LLM as a world model to track environmental changes and refine prompts. Explicit symbolic frameworks also contribute: TimeTToM [17 constructs a temporal reasoning framework to support inference, SymbolicTToM 37 uses graphical representations to track characters' beliefs, and
this ability: SimToM 48
thought-tracing [24] traces multiple hypotheses over time. However, these approaches still exhibit systematic errors in handling long contexts, complex behaviors, and recursive reasoning scenarios.
Among these works, thought- is closely related to ours, as also maintains hypotheses of mental variables. Compared to nooughtttracing [24]. AutoToM performs explicit agent modeling: it constructs Bayesian networks over mental variables and their causal dependencies, rather than tracking only the queried mental variables. This yields higher robustness to wording or superficial story changes (e.g., no need for wording changes in AutoToM), and improves interpretability. as errors can be analyzed through the model structure. Moreover, AutoToo adaptively minimizes inference complexity by expanding models only when beneficial, preventing under-/over-modelle and improving efficiency on tasks with longer contexts, more agents, and deeper recursion. By contrast, thought-tracing reweights hypotheses without adjusting model structure or temporal depth.
Model-based Theory of Mind inference. Model-based Theory of Mind inference, particularly explicitly constructs representations of agents' mental states and models how these mental states guide behavior through probabilistic agent models. These
Bayesian inverse planning (BIP)
3 52
methods can reverse engineer human ToM inference in simple domains [c.g..B (1 40. Recent works
combine BIP with L.MSs to improve ToM inference in more realistic settings (00 39 However, they
require manual specification of the agent models as well as rigid, domain-speeifi implementations of Bayesian inference, limiting their adaptability to open-ended scenarios. To overcome this, we propose Aoto a method for automated agent modeling and mental inference across diverse domains.
Automated Modeling with LLMs. There has been an increasing interest in integrating LLMs with inductive reasoning and probabilistic inference for automated modeling. Piriyakulkij al. (32 combine LLMs with Sequential Monte Carlo to perform probabilistic inference about underlying rules. Qiu et al. (34) further enhance LLM-based inductive reasoning by iteratively proposing. selecting, and refining textual hypotheses of rules. Li et al. (27] employ LLMs to construct, critique, and refine statistical models represented as probabilistic programs for data modeling. Wang et al.
46) prompt LLMs to generate natural language hypotheses that are then implemented as verifiable programs for inductive reasoning. Hypothetical minds (6] leverage LLMs to propose and evaluate agent strategies for multi-agent planning, but do not specifically infer individual mental variables. Our method also aims to achieve automated modeling with LLMs. Unlike prior works, we propose a novel automated model discovery approach for Bayesian inverse planning, where the objective is to confidently infer any mental variable given any context by constructing a suitable agent model.
3 AutoTTM
3.1
Preliminaries: A Unified Formulation of Model-based ToM
Bayesian Inverse Planning (BIP) a computational framework for model- ToM inference 4. It assumes that the agent acts rationally according to generative agent model 3. which specifies how internal variables lead to observable actions in a Bayesian network (e.g., the example models on the bottom panels in Tp. Using inverse inference, BIP inverts this generative process to assess what latent mental variables can lead to observed agent behavior. This probabilistic inference reasons about how agents make decisions, serving robust solution to ToM challenges.
There have been different instantiations of BIP in prior works [e.g.. 4 43 30 19]. Here we formally define BIP in unified manner. We denote the observable variables at time i describing the envi- ronment and an agent's 's behaviors as X { IeNxx where Nx is the set of observable variables and of is a particular variable (state, action, or utterance) at t. We can extract the values of these observable variables from the context provided a ToM problem. We denote an agent's latent mental variables at time /. as V = {vf) Jie where Ny is the set of mental variables and of is particular mental variable (e.g., goal, desire, belief) at l. BIP formulates an agent model as a Bayesian network that defines P(V*, (1V",""), Xx),, which indicates how the mental variables drive an agent's 's behavior. Given
3

---

(a) AutoIoM construels appropriate agent models tailored to different scenarios
ToMi
BigToM
MMTAM-QQ
MuMA-TTM
Hi-lMM
Stary ueleeed lannert
The 640 fhe
Me
Do unegu
faali specket youl sugge
Noae candaned
ynd cayate
Some:
obiied comaira
Actine vella clips
peirl miree snigro open beries thet morr over Ski
Ake leeine The with io
Quentaa: Which oee Sclincing
Qeerten Where doen bike cork
Quention EJ Jeniko kooews pllut
baly sirretty Amne The
Otwettver Unen helkre the orr that tt emper
dapss
botn syving tegina bug
wie hud Car dalloerg MOST Suuy?
Chuatoon hev con Aue thiek
Seas lowe tyying goli
(o) leskuro leok Kowis
Model
Model
Model
Model
Model
(b) AutoToM produces human-like confidence estimates observed in cognitive studies
(c) AutoToM enables online mental inference to support embodied decision-making
A
Main agent gut up a dinner bable
infer Mann goal and help reach the goal feeder
33
Humon 2.
Hok' coets deerrgrrtt gasl
Auariald
Obearved Marc's actione
Mborepow Berls
Aike denm thu willond
Alke purt plae
Tbpe ao tn (aei
Pepottistunn glontedle
Hype
Goal
by Awe
Shyp iitt ant plan (pod tip
thyotit and sere
Put fomer
Whihhspe food foe agnit invel
te-ggen betiee in the come?? (Baker stal. 2017))
Hidperrs
Adkun
News.
Namc
anlped selpdeandttad
Figure 2: Overview of AutoToM''s capacities and applications evaluated in this work. (a) Example questions (top panels) and the necessary agent model for model-based inference (bottom panels) in diverse Theory of Mind benchmarks. Questions in these benchmarks encompass different mental variables, contexts, numbers of agents, the presence or absence of utterances, wording styles, and modalities. (b) AutoToM can produce human-like confidence estimation in classic cognitive studies. (c) AutoToM can also be used for online goal inference to enhance embodied assistance, where it sequentially updates the inference of main agent's 's goal inform a helper agent's assistance.
this model, BIP infers the latent mental variables for the current step
P(V = P(V., X Σy P(V, x P(V*,
In many real-world scenarios, past observations (such as actions taken at the previous steps) are often valuable for inferring the mental variables at the current step. Suppose the context from step t, to step t is relevant for the current mental variable inference, then the inference becomes:
P(V
P(V X
In a ToM problem, there is a query concerning a specific target variable q to be inferred. We can answer the query via (.tttt) Typically, the query asks about latent mental variable of Vt. the posterior probability is obtained by marginalizing over other latent variables ptait which is the subset of ytast excluding of:
P(uf x Ever P(uf, Viact, xxtx:))
This can also be extended to predicting a future observable variable q = az 11,
P( +1 [ x Ev.., P(Vta a X(::).
To conduct BIP in different scenarios, we must formulate the mental variables and their causal relationships with agent behavior using suitable agent models. Each model M is uniquely defined by

---

Variable Adjuddment
Hypothesis Sampling
in aceete suge
levile Beride
Bayesian Inference
Before
After
leedlr Durthe
et Kemert hures dass abot
Ay Kitt was
Padgesaaalyy MHlte > molittian
Timestep Adjusdment
Beffre
After
Annvertly
plaas wwdeeteeeeeeee.oooo.o Helge
(a) Automated Bayesian inverse planning.
(b) Model adjustments.
Figure 3: (a) Given an agent model, AutoToM samples hypotheses for each latent variable (o' and b in this example), remove spurious hypotheses, and conduct Bayesian inference based estimated local conditionals. (b) Given any ToM inference problem, AutoToM refines the agent model by alternating between variable adjustment (introducing belief in this example) and timestep adjustment.
the observable variables and the latent mental variables, i.e., M = (Vi,it X Let s E S be the state time f, and a' A be the action taken by the agent at time t. The current state and action determines the next state sf g++1 When the agent has an explicit goal g E G, this setup constitutes Markov Decision Process (MDP). If the agent only has a partial observation of the state, the model becomes a Partially Observable Markov Decision Process (POMDP) 233. In POMDP, the agent receives a partial observation o' of the true state s', maintains a belief b over the possible states, and selects its action a' based on this belief and goal. When there is high-order recursive reasoning between two agents (i and j), we can adopt an Interactive POMDP (I-POMDP) [22 where the belief of state at level > 0 for agent i will become the belief of interactive state is' = (s, bz1-1. where - is the belief of agent j at the lower level I and gj is agent j's goal.
3.2 Overview of AutoToM
As shown in Figure I AutoToM aims to construct suitable agent model for Bayesian inverse planning to confidently infer any target variable. There are several key challenges in achieving this: First, different ToM inference problems require different agent models (as illustrated Second, our method must determine which timesteps in the context are relevant. Third, there is no predefined hypothesis space for each variable, and each space could be infinite. Last, to infer mental variables in any context, we must flexibly represent them without manual specifications.
Figure[p)
AutoToM addresses these challenges in the two key components: (1) automated Bayesian inverse planning (Section B.3). which conducts BIP given a specified agent model, and (2) automated agent model discovery (Section 334), which proposes and adjusts the agent model based on the question and the inference results. These two components form improvenpve loop to iteratively update the agent model and the corresponding inference result. More details are provided in Appendix A
3.3 Automated Bayesian Inverse Planning
Given an agent model, M, including the necessary latent mental variables Vt,ir and the observable variables X we integrate LLMs as the computational backend to implement every aspect of the Bayesian inverse planning.
particular, the hypothesis sampling module suggests small set of possible values of latent variables. The Bayesian inference module then computes the posterior distribution the target variable in the query based on Eqn. (3) or Eqn.(44)
Hypothesis Sampling, Conventional BIP assumes manually defined hypothesis space and rep- resentation for each latent mental variable. Our hypothesis sampling module instead leverages an LLM to propose only a small set of quality hypotheses for each latent variable in Vtait, This is akin to amortized inference [35] 19. To ensure that the sampled hypotheses are relevant the ToM inference, we guide the sampling process with both the question and the observable variables X To remove spurious hypotheses generated by the LLM, we further apply hypothesis reduction to eliminate unlikely hypotheses and reduce the hypothesis space. Unlikely hypotheses are identified by evaluating the local conditionals. For instance, we discard observation hypotheses with low likelihood conditioned on the state as shown in Figure 3a
5

---

Bayesian Inference. As shown in Figure 3a we estimate each local conditional in P(V x using an LLM. After marginalizing the joint distribution over non- non-target latent variables via explicit calculation, we then produce the posterior probabilities of the target variable, i.e., Eqn. 33) This also applies to predicting a future observable variable, i.e., Eqn.
Our automated BIP greatly generalizes prior methods that combine BIP and LLMs, such as BIP-ALM (0 and LIMP [39]. Specifically, prior methods assume a fixed model structure defined for specific ToM problem and require handcrafted, domain-specific representations for physical and mental states, They also cannot propose hypotheses for non-target latent variables. For instance, infer an agent's s goal, BIP-ALM conducts a manual belief update while LIMP has no explicit belief update at all. In contrast, AutoToM can conduct any ToM inference based on any agent model structure and consider multiple non -targtt latent variables simultaneously. Additionally, unlike prior methods, our Bayesian inference can work with arbitrary levels of recursion for high-order ToM inference.
3.4
Automated Agent Model Discovery
Prior works on model-based ToM inference rely on manually designed agent models, limiting their applicability to domain-specific scemarios. In contrast, the Automated Model Discovery component automatically proposes model and dynamically adjusts it to ensure both the effectiveness of the coodel-confidently model confidently inferring agents' mental states and the efficiency of the inference by minimizing model complexity. To achieve this, we formulate the utility of model M (V*,it Xtuit) used for answering a given query as
((M = R(M.9) C(M),
(5)
where R(M, q) assesses the model's 's confidence in answering the query, and C(M) is its computa-
tional cost. In this work, the reward is defined as R(M, g) H ( x()))))))) )), where P(g] is the probability distribution of the target variable based on Eqn. (3) or Eqn. 4 and H()) is entropy. This is designed to decrease the uncertainty in the inference. To minimize the compute needed for the inference, we define the cost of the model as C(M) = a M where |M denotes the model's complexity, measured by the number of latent mental variables, and a > is weighting factor. The cost increases with complexity, encouraging parsimonious models with lower compute.
There are three modules for Automated Model Discovery:
Information Extraction. This module extracts the values of observable variables X from the context, including states (s"), actions (a'), and utterances (u'), organized along a timeline (the number of timesteps is determined by the number of actions and utterances). When there are multiple agents, we identify whose mental state the question is asking about (i.e., the target agent), and then construct the timesteps based on the target agent's 's actions and/or utterances. The extraction is performed once using an LLM and used for model proposal and Bayesian inverse planning.
Initial Model Proposal. We employ an LLM to propose an initial agent model based 1 and the query. This initial model has minimal complexity, containing only the essential mental variables needed to answer the question. This initial proposal also assesses the level of recursive reasoning necessary for higher-order ToM inference. Note that we always begin with only considering the last timestep in context, i.e., f. t. Following this model, planning. as described in Section 333] If the model utility exceeds a threshold Umin- we accept the inference result the final answer. Otherwise, we use the model utility to guide model adjustments.
conduct automated Bayesian inverse
Model Adjustment. We iteratively adjust the proposed model to maximize the utility by considering two types of model adjustments: variable adjustment (Figure 3b) and timestep adjustment (Figure 3b):
Variable Adjustment. We refine the model structure at specific timestep by iteratively introducing new, relevant latent variables into the model to address uncertainty in the inference. These variables include goal, belief, observation, and interactive state as summarized in Table in Appendix A This follows the typical causal structures introduced in prior decision decision-making models [e.g.. 23 Such restricted variable adjustment helps reduce the model space and ensures the proposed models can explain human behavior. For each adjustment, we compute the updated model utility and accept the modification that offers the biggest increase utility. This iterative process continues until no further significant improvements are possible. Note that our method can still propose diverse models beyond standard MDP, POMDP, and I-POMDP, even with this restricted model adjustment. Appendix A.5 provides more details the model space.
43 12

---

also more Table I: Results of all methods ToM benchmarks, grouped by model types: LLMs, ToM prompting. large reasoning models, and model-based inference. " indicates that the domain-specifie method is not applicable to the benchmark. The best results shown in bold.
Method
ToMi
BigToM
MMTOM-QA
MuMA-Toli
Hi-ToM
All
Llama 3.1 70B GPT-40
72.00
77.83
43.83
55.78
35.00
56.89
77.00
82.42
44.00
63.55
50.00
63.39
Gemini 2.0 Flash
66.70
82.00
48.00
55.33
52.50
60.91
Gemini 2.0 Pro
71.90
86.33
50.84
62.22
57.50
65.76
TombolicToM SimToM
98.60
44.50
79.90
77.50
51.00
47.63
71.00
65.41
DeepSeek-RI Gemini 2.0 Flash Thinking
89.40
86.25
49.67
63,44
56.50
69.05
78.00
82.83
54.00
82.56
73.50
74.18
03-mini-high
73.10
86.92
64.67
70.00
75.00
73.94
BIP-ALM
55,60
50.33
56.17
33.90
14.50
42.10
LIMP
44.60
61.67
55.33
76.60
6.50
48.94
AutoToM (w/ GPT-4o)
88.30
86.92
83.00
81.44
72.50
82.43
Timestep Adjustment. If model utility remains low and no significant improvement can be achieved via variable adjustment within the current timesteps L, L, incorporate additional step, 4, to enhance context for inference. Upon adding timestep, we first apply the initial model structure and then adjust variables accordingly.
We iterate the variable and timestep adjustments until either the model utility exceeds the desired threshold further meaningful improvement is possible.
Experiments
4.1 Experiment 1: Evaluation on ToM Benchmarks
Setting. We evaluated our method on multiple Theory of Mind benchmarks, including ToMi (6). BigToM [T] MMToM-QA (00]. MuMA-ToM (39), and Hi-ToM [5]. The diversity and complexity of these benchmarks pose significant reasoning challenges. For instance, MMToM-QA and MuMA-ToM incorporate both vision and language inputs, while MuMA-ToM and Hi-ToM require higher-order inference. Additionally, MMToM-QA features exceptionally long contexts, and BigToM presents open-ended scenarios.
We compared AutoToM against state-of-thhe-at baselines:
LLMs: Llama 3.1 70B 99, GPT-4o [ Gemini 2.0 Flash and Gemini 2.0 Pro 41
ToM Prompting for LLMs: SymbolicToM
37 and SimToM [88];
Large Reasoning Models: DeepSeek-RR R1
13
Gemini 2.0 Flash Thinking, and o3-mini- high;
Model-based Inference: BIP-ALM 20 and LIMP B9
We use GPT-4o as the LLM backend for AutoToM and ToM prompting and model-based inference baselines to ensure fair comparison. For multimodal benchmarks, MMToM-QA and MuMA-ToM: we adopt the information fusion methods proposed by Jin et al. (20) and Shi et al. [39] to fuse information from visual and text inputs, respectively. The fused information is in text form. We ensure that all methods use the same fused information as their input.
Results. The main results are summarized in Table I AutoToM demonstrates the strongest overall performance among all methods, including large reasoning models. Specifically, it outperforms its LLM backend, GPT-4o, by large margin. This is because AutoToM is more robust for inferring mental states given long contexts with complex environments and agent behavior. adept at recursive reasoning, which is key to higher-order inference. Compared to prior model-based methods, it exhibits superior generalization across different domains. This is enabled by our agent model discovery and the automated BIP.
7

---

(a)
103
Decpdoak-F1
Cernr1 Flssh Thundin
ALaITOMA
80
Accuracy
Level0 0100 TOR)
LIWal Acsio
Level
Level Delee
Lawel
Goal
Type
(b)
100
(c)
100
(d)
100
50
80
Accuracy (1)
Accurasy (%)
Accurscy
50
60
60
40
40
Dapdlokk
DappSkck #11
20
AuSTTUM
AusTTal
AusiTial
0.600
630-3000
3000-6003
5000
Context Lenghh
Number of Agents
Level of Recursion
Figure 4: Comparison of ToM and large reasoning models across various conditions (summa- rized among all benchmarks): (a) question types, (b) context length, (c) the number of agents, and (d) the level of recursion. Note that "Level Action" refers to Forward Action inference Bog,oM, and "Level 2 Goal" refers to the Belief of Goal inference in MuMA-ToM.
Story: Noor is working as a barista a busy coffee shop. Noor wants to make a delicious cappuccino for customer who asked for oat milk. Noor grabs milk pitcher and fills with oat milk. cowarker, who didn't hear the customer's request, swaps the milk the pitcher with almond milk while Noor attending to another task. Noor makes the cappuocino using the milk, in the pitcher.
Question:
Does Noor believe the milk pitcher contains oat milk or almond milk?
(a) Noor believes the milk pilcher contains abmond milk.
(b) Noor believes the milk pitcher contains out (Correct Answer)
Information
pitcher
pitcher contains
Extraction P3)
action
Noor grabs milk pitcher and fills with milk.
Noor makes the cappuccino using the milk the pitcher.
Initial Model Proposal
A Adjusted Model
ba
ab
g
oa
bo
a²
Model Adjustment
g
o¹
bi
of
bb
a²
Bayesian
S Bayesian Inference
p(0')p",a') oc Σ PRD Pebtibt, ( (s")P(>))
Σ
p(al)bt.e))08 ())((" o')e is' (s")
P(b] almond x² - 05 P(b) xx
Model Uuilit Lota X (Uncertain)
P(M - almond = 0.1 P(b) out x=1)=
Model Uuiiiy high V (Confident)
Figure 5: A qualitative example of AutoToM's model adjustment and inference process in a false- belief scenario from BigToM [I]. We show the results from each key model step. It demonstrates how AutoToM adjusts the agent model to increase inference confidence. Detailed procedures of Bayesian inference for both the proposed and adjusted models provided in Appendix C.5
We also compared the performance of AutoToM with large reasoning models across different conditions, summarized over all benchmarks. These include question types, the context length, the number of agents, and the level of recursion. As shown Figure 4 AutoToM demonstrates robust scalability and exhibits a much lower degree of volatility under different conditions than large reasoning models. We provide additional results and evaluations Appendix C.2 and C.3
We further report the token cost and inference time comparison on MMTOM-QA in Appendix AutoToM achieves higher reasoning performance with comparable or lower computational cost, highlighting its efficiency and scalability.
Figure 5 depicts qualitative example of how model discovery and adjustment improve inference for a false-belief question in BigToM. Users can use such interpretable explanations to diagnose and identify sources of model errors, and consequently correct model mistakes. Appendix shows example human feedback improving the model using a user interface developed with AutoToM.
Ablation Study. We evaluated the following variants of AutoToM for ablation study: no hypothesis reduction (w/o hypo. reduction); always using POMDP (w/ POMDP); always using the initial model proposal without variable adjustment (w/o variable adj.); only considering the last timestep (w/ last
8

---

Table 2:
Performance comparison on MMTOM-QA. LLM indicates the model itself; AutoToM represents our method with the corresponding model as the backend.
LLM
AutoToM
GPT-4o
44.0
83.0
Qwen3-2156-a22b-2507
45.0
67.5
DeepSeck-chat-v3-0524
34.8
71.1
Gemini-2.5-Flash (thinking disabled)
44.7
71.7
timestep); and considering all timesteps without timestep adjustment (w/ all timesteps). The results in Figure [ show that the full AutoToM method constructs a suitable agent model, enabling rich ToM inferences while reducing compute. In particular, key model components, including hypothesis reduction, variable adjustment, and timestep adjustment, optimize efficiency without sacrificing performance. Full ablation results are provided in Appendix C.4
Sensitivity to LLM Backends. To test AutoToM'ss per- formance sensitivity to LLM backends, additional experiments using alternative models. Note that used the same prompt for each backend LLM. Specifically, we replace the GPT-4o backend with Qwen3- 235B (open-sourced), DeepSeek-V3 V3 (open-sourced), and Gemini-2.5 2.5-flash (thinking disabled) on the most challeng- ing MMToM-QA benchmark. Notably, AutoToM with any LLM as the backend outperforms the corresponding LLM performance by large margin (Table 2 Crucially, we achieve this without extra prompt engineering.
conducted
AutelTaM
82
weo hypo reduction
Accurcyy (%)
78
w/o vartable adh
wi all timellepe
78
w/ POMEE
72
x/ hmestep
10
12
Compute (thousand tokens)
Statistical Reliability. To assess result stability, we addi- tionally ran multiple trials on the most challenging bench- mark, MMToM-QA. Across three different random seeds, AutoToM achieved a mean accuracy of 82.56% with a standard error of 0.45% which is consistent with the 83.00% reported in Table Similarly, o3-mini- high achieved a mean accuracy of 65.94% with a standard error of 0.59%. These results indicate that the evaluation is stable across runs, and our conclusions remain robust.
Figure 6: Averaged performance and compute of AutoToM (star) and its vari- ants (circles) on all benchmarks.
4.2 Experiment 2: Evaluation on Classic Cognitive Studies
Setting. AutoToM produces posterior distributions over the hypothesis space, offering uncertainty estimates. This allows us to compare the model uncertainties with human judgments. We adapted two well-known cognitive studies on human ToM: online goal inference in [4] and desire and belief' inferences in the food truck scenarios (3). As shown in Figure 2p, in each study, participants were shown agent behavior in a 2D gridworld and asked to judge the agent's 's goal 4 and desires and belief' in (3] A capable model needs to sequentially update multiple hypotheses with varying degrees of confidence that closely resemble human judgment.
In this experiment, we generated captions for the frames in both tasks and evaluated AutoToM on all available types of scenarios, using the posterior probabilities from AutoToM as its confidence. For baseline, we asked GPT-4o and o3-mini-high to produce confidence scores for each hypothesis in all trials, given the same captions. Implementation details are provided Appendix [.2
Results. We computed the correlation between model responses and human judgments reported in the original studies. As shown in Table AutoToM aligns well with human confidence judgments on all three tasks. In particular, Auto demonstrates substantially higher correlation with humans than GPT-4o and o3-mini-hhn high in more complex tasks with a partially observable environment. The results indicate that Auto ToM is able to produce nuanced confidence estimates that closely mirror human inference patterns in different environments. We provide additional results in Appendix D.I
4.3 Experiment 3: Embodied Assistance

---

Table Pearson correlation coefficients and p-values between model and human judgments. Strong
and significant correlations are bolded. ": I .05, *": p S .001. "obs." indicates observability.
Task
AutoToM
GPT-4o
03- high
Online goal inference (full obs.) in
0.93
0.81
0.97
Desire inference (partial obs.) in
0.88
0.30
0.52*
Belief inference (partial obs.) in
0.73
0.04
0.03
Setting. As recent cognitive studies have suggested. hu- mans routinely utilize ToM to improve our decision mak-
30
ing in multi-agent settings 47 [6]. To evaluate whether
Speedup (%)
Auto ToM can help improve multi-agent decision making,
20
we further evaluated it in an embodied assistance bench-
mark, Online Batch-Ann-Helle (O-WAH) 3 where helper agent must simultaneously observe a main agent's actions, infer its goal, and assist it to reach the inferred goal faster in realistic household environments. In these tasks, a ToM model must update its inference of the main agent's 's goal based on the latest observations in an online manner. Given the goal inference each step. adopted the uncertainty-awarr helping planner proposed in [33] to generate helping actions accordingly. There are 4 task categories (setting the table, putting groceries the fridge, preparing simple meal, washing dishes). We evaluated each method across 20 episodes, with 5 episodes in each task category. To reduce variance, the results are reported as the average over runs per episode.
10
Random GPT-4o AutoToM
Figure 7: Averaged speedup of Au- toToM and baselines on the O-WAH benchmark. Error bars indicate standard errors.
As shown in Figure 2c, we applied AutoToM to online goal inference. Specifically, AutoToM con- structs an agent model each step and maintains the goal hypotheses and corresponding probabilities using Sequential Monte Carlo (SMC) [8]. We also paired the same planner with two baseline goal inference methods: Random Goal (i.e., randomly sampling goal) and GPT-4o for online goal inference. We did not evaluate any large reasoning models due to their slow inference speed (more than minute per timestep), which makes it impractical for online embodied assistance tasks.
Results. As shown in Figure 7 the Random Goal baseline achieves 6.3% speedup, but with high variance and negative speedup in 50% of the episodes. GPT-4o achieves a similar but more stable speedup 6.8%. contrast, AutoToM achieves the highest speedup 27.7%, significantly outperforming all baselines. This is because AutoToM can produce more accurate uncertainty estimation of goal hypotheses based on observed actions, which is key to generating robust and useful helping plans, Additional details are provided in Appendix E
Conclusion
We have proposed AutoToM, novel framework for scalable model-based Theory of Mind. Given any ToM inference problem, AutoToM can automatically construct a suitable agent model and conduct automated Bayesian inverse planning with an LLM backend. Our experimental results have demonstrated that AutoToM can answer different Theory of Mind questions in diverse scenarios, significantly outperforming basclines. We have also shown that AutoToM can produce human-like confidence estimation about mental inferences in classic cognitive studies, and conduct online goal inference for enhancing embodied assistance in complex houschold scenarios. AutoToM suggests a promising direction toward cognitively grounded ToM modeling that is scalable and robust.
Limitations and Future Work. AutoToM currently requires separate process to first fuse infor- mation from different modalities into text before inference. In the future, we intend to investigate a natively supported multimodal capacity. Additionally, model adjustments may sometimes fail to recognize the relevance of certain mental variables, resulting in insufficient model. In the future, we intend to further improve the robustness of AutoToM while reducing inference cost by exploring the possibility of implicit model proposal and Bayesian inference.
10

---

Acknowledgments and Disclosure of Funding
This work was supported by grant from Amazon. The authors would like to thank Hyokun Yun and Tanya Roosta for their helpful comments.
References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and others. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
[2] Akshatha Arodi and Jackie Chi Kit Cheung. 2021. Textual time travel: A temporally informed approach to theory of mind. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4162-4172.
[3] Chris I. Baker, Julian Jara-Fttinger, Rebecca Saxe, and Joshua B Tenenbaum. 2017. Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour, 144):0064.
[4] Chris L Baker, Rebecca Saxe, and Joshua Tenenbaum. 2009. Action understanding as inverse planning. Cognition, 113(3):329-22
[5] Rohan Chandra, Aniket Bera, and Dinesh Manocha. 2020. Stylepredict: Machine theory of mind for human driver behavior from trajectories. arXiv preprint arXiv:2011.04816
[6] Logan Cross, Violet Xiang, Agam Bhatia, Daniel LK Yamins, and Nick Haber. 2024. Hypothet- ical minds: Scaffolding theory of mind for multi-agent tasks with large language models. arXiv preprint arXiv:2407.070066
[7] Kerstin Dautenhahn. 2007. Socially intelligent robots: dimensions of human-roke robot interaction. Philosophical transactions of the royal society Biological sciences, 362 (17(1)1:6699-7000
[8] Pierre Del Moral, Arnaud Doucet, and Ajay Jasra. 2006. Sequential monte carlo samplers. Journal of the Royal Statistical Society Series B: Statistical Methodology, 68(3):411-436.
[9] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, and others. 2024. The Ilama 3 herd of models. arXiv preprint arXiv:2407.21788
[10] Xianzhe Fan, Xuhui Zhou, Chuanyang Jin, Kolby Nottingham, Hao Zhu, and Maarten Sap. 2025. Somi-tom: Evaluating multi-perspective theory of mind in embodied social interactions. arXiv preprint arXiv:2506.22 23046.
[11] Kanishk Gandhi. Jan- Jan-Philipp Fränken, Tobias Gerstenberg, and Noah Goodman. 2024. Un- derstanding social reasoning in language models with language models. Advances in Neural Information Processing Systems, 36.
[12] Piotr Gmytrasiewicz and Prashant Doshi. 2005. A framework for sequential planning in multi-agent settings. Journal of Artificial Intelligence Research, 24:49-79.
[13] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and others. 2025. Deepseek-rl: Incentivizing reasoning capability in lmm via reinforcement learning. arXiv preprint arXiv:2501.1294
[14] Dylan HadfelddMennll,, Stuart Russell, Pieter Abbeel, and Anca Dragan. 2016. Cooperative inverse reinforcement learning. In Advances in neural information processing systems.
[15] Yinghui He, Yufan Wu, Yilin Jia, Rada Mihalcea. Yulong Chen, and Naihao Deng. 2023. Hi-tom: A benchmark for evaluating higher-order theory of mind reasoning in large language models, arXiv preprint 2r10v:2310.1655.
[16] Mark K Ho, Rebecca Saxe, and Fiery Cushman. 2022. Planning with theory of mind. Trends in Cognitive Sciences, 26(11):959-771

---

[17] Guiyang Hou, Wenqi Zhang, Yongliang Shen, Linjuan Wu, and Weiming Lu, 2024. Timetom: Temporal space is the key to unlocking the door of large language models' theory-of-minde arXiv preprini arXiv:240.001455.
[18] X Angelo Huang, Emanuele La Malfa, Samuele Marro, Andrea Asperti, Anthony Cohn, and Michael Wooldridge. 2024. A notion of complexity for theory of mind via discrete world models, arXiv preprint arXiv:2406. 11911.
[19] Kunal Jha, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B Tenenbaum, and Tianmin Shu. 2024. Neural amortized inference for nested multi-agent reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence.
[20] Chuanyang Jin, Yutong Wu, Jing Cao, Jiannan Xiang, Yen-Ling Kuo, Zhiting Hu, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum, and Tianmin Shu. 2024. Mmtom-qa: Mul- timodal theory of mind question answering. In 62nd Annual Meeting of the Association for Computational Linguistics (ACL).
[21] Chuanyang Jin, Jing Xu, Bo Liu, Leitian Tao, Olga Golovneva, Tianmin Shu, Wenting Zhao, Xian Li, and Jason Weston. 2025. The era of real-world human interaction: RI from user conversations. arXiv preprint arXiv:2509.251377
[22] Chani Jung. Dongkwan Kim, Jiho Jin, Jiseon Kim, Yeon Sconwoo, Yejin Choi, Alice Oh, and Hyunwoo Kim. 2024. Perceptions to beliefs: Exploring precursory inferences for theory of mind in large language models. arXiv preprint wrxiv:2407.06004
[23] Leslie Pack Kaelbling, Michael I. Littman, and Anthony R Cassandra. 1998. Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1-2):99-134
[24] Hyunwoo Kim, Melanie Sclar, Tan Zhi-Xuan, Lance Ying, Sydney Levine, Yang Liu, Joshua B Tenenbaum, and Yejin Choi. 2025. Hypothesis-drive theory-of-ming reasoning for large language models. arXiv preprint arXiv:2502.1 11881.
[25] Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, Ronan Le Bras, Gunhee Kim, Yejin Choi, and Maarten Sap. 2023. Fantom: A benchmark for stress-testing machine theory of mind in interactions. arXiv preprint arXiv:2310.1542.
[26] Matthew Le, Y-Lan Boureau, and Maximilian Nickel. 2019. Revisiting the evaluation of theory of mind through question answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3872-5877.
[27] Michael Y Li, Emily B Fox, and Noah D Goodman. 2024. Automated statistical model discovery with language models. arXiv preprint arXiv:2402. 178779
[28] Chang Liu, Jessica B Hamrick, Jaime F Fisac, Anca D Dragan, Karl Hedrick, S Shankar Sastry, and Thomas L Griffiths. 2018. Goal inference improves objective and perceived performance in human-robot collaboration. arXiv preprint
[29] Aviv Netanyahu, Tianmin Shu, Boris Katz, Andrei Barbu, and Joshua Tenenbaum. 2021. Phase: moysically-groundee abstract social events for machine social perception. In Proceedings of the aaai conference on artificial intelligence, volume 35, pages 845-853.
[30] Desmond C Ong, Jamil Zaki, and Noah D Goodman. 2019. Computational models of emotion inference in theory of mind: A review and roadmap. Topics in cognitive science, 11(2):338-3 3557.
[31] Maithili Patel and Sonia Chernova. 2022. Proactive robot assistance via spatio-temporal object modeling. arXiv preprint arXiv:2211.15501.
[32] Wasu Top Piriyakulkij, Cassidy Langenfeld, Tuan Anh Le, and Kevin Ellis. 2024. Doing experiments and revising rules with natural language and probabilistic reasoning. arXiv preprint arXiv:2402.06025.
[33] Xavier Puig, Tianmin Shu, Joshua B Tenenbaum, and Antonio Torralba. 2023. Nopa: Neurally- guided online probabilistic assistance for building socially intelligent home assistants. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 7628-7633 IEEE.
12

---

[34] Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Selar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, and others. 2023. Phenomenal yet puzzling: Testing inductive reasoning capabilities of language models with hypothesis refinement. arXiv preprint arXiv:2310.08559.
[35] Daniel Ritchie, Paul Horsfall, and Noah D Goodman. 2016. Deep amortized inference for probabilisticc programs. arXiv preprint arXiv:1610.05735
[36] Maarten Sap, Ronan LeBras, Daniel Fried, and Yejin Choi. 2022. Neural theory-of-mind? on the limits of social intelligence in large Ims. arXiv preprint arXiv:2210.1333 133112.
[37] Melanie Sclar, Sachin Kumar, Peter West, Alane Suhr, Yejin Choi, and Yulia Tsvetkov. 2023. Minding language models' models"(lack of) theory of mind: plug-and-play multi-character belief tracker. arXiv preprint arXiv:2306.000 00924.
[38] Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, and Vered Shwartz. 2023, Clever hans or neural theory of mind? stress testing social reasoning in large language models. arXiv preprint 2rXiv:2305.1477
[39] Haojun Shi, Suyu Ye, Xinyu Fang. Chuanyang Jin, Leyla Isik, Yen-Ling Kuo, and Tianmin Shu. 2024. Muma-tom: Multi-modal multi-agent theory of mind. arXiv preprint arXiv:2408. 12574.
[40] Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke. Joshua Tenenbaum, and Tomer Ullman. 2021. Agent: A benchmark for core psychological reasoning. In International conference on machine learning, pages 9614-9625.
PMLR.
[41] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean- -aan-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, and 1 others. 2023. Gemini: family of highly capable multimodal models. arXiv preprint arXiv:2312.11800
[42] Tomer Ullman. 2023. Large language models fail on trivial alterations to theory-of-ne -f-ind tasks. arXiv preprint arXiv:2302.08319
[43] Tomer Ullman, Chris Baker, Owen Macindoe, Owain Evans, Noah Goodman, and Joshua Tenenbaum. 2009. Help or hinder: Bayesian models of social goal inference. Advances in neural information processing systems, 22.
[44] Yanming Wan, Jiayuan Mao, and Josh Tenenbaum. 2022. Handmethat: Human-robot com- munication in physical and social environments. Advances in Neural Information Processing Systems, 35: 15144226..
[45] Qiaosi Wang, Koustuv Saha, Eric Gregori, David Joyner, and Ashok Goel. 2021. Towards mutual theory of mind in human-ai interaction: How language reflects what students perceive about a virtual teaching assistant. In Proceedings of the 2021 CHI conference on human factors in computing systems, pages 1-14.
[46] Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, and Noah D) Good- man. 2023. Hypothesis search: Inductive reasoning with language models. arXiv preprint arXiv:2309.05660.
[47] Felix Warneken and Michael Tomasello. 2006. Altruistic helping in human infants and young chimpanzees. science. 31165565)13011-1333
[48] Alex Wilf, Sihyun Shawn Lee, Paul Pu Liang, and Louis-Philippe Morency. 2023. Think twice: Perspective-taking improves large language models' theory-of-mind capabilities. arXiv preprini arXiv:2311.10227.
[49] Heinz Wimmer and Josef Perner. 1983. Beliefs about beliefs: Representation and constraining function of wrong belief's in young children's 's understanding of deception. Cognition, 13(1):103- 128.
[50] Hainiu Xu, Runcong Zhao, Lixing Zhu, Jinhua Du, and Yulan He. 2024. Opentom: A com- prehensive benchmark for evaluating theory-of-mind reasoning capabilities of large language models. arXiv preprint arXi::400.000044
13

---

[51] Lance Ying. Kunal Jha, Shivam Aarya, Joshua Tenenbaum, Antonio Torralba, and Tianmin Shu. 2024. GOMA: Proactive embodied cooperative communication via goal-oriented mental alignment. In IEEF/RSJ International Conference on Intelligent Robots and Systems (IROS).
[52] Tan Zhi-Xuan, Jordyn Mann, Tom Silver, Josh Tenenbaum, and Vikash Mansinghka. 2020. Online bayesian goal inference for boundedly rational planning agents. Advances in neural information processing systems, 33:19238-1999 19250..
[53] Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, and Joshua B Tenenbaum. 2024. Pragmatic instruction following and goal assistance via cooperative language-guided inverse planning. arXiv preprini arX11:2402.17930.
14

---

Algorithm/ AutoToM
Require:
Question Q. terminate threshold Umin
1: b Automated Bayesian inverse planning
2: function BIP(M (V*.*t , xx*
3: Sample hypotheses for latent variables V
4: Conduct Bayesian inference via LLMs to compute P(g ((::))
Based Eqn.
or Eqn.
5: return Pq X (t)
6: end function
7: Automated Model Discovery
8: Extract query q from Q
9: Extract observable variables X from Q
10: tu +
11: while L, 2 do
12: Propose initial V
M -VVt: X P(q X + BIP(M,
14:
15:
Compute the model utility (M, g)
16:
while V does not contain all mental variables do
17:
Une arg oaggmax.gv ugVe UMM + v)
Based results from BIP M v,
18:
if (M + unew 9) > U(M, g) then
19:
M M Vie
P(g X at (IP q)
21:
else
22:
Exit loop
23:
end if
24:
end while
if (M, q) 2 Unin then
26:
Exit loop
27:
else
28:
t. -1
29:
end
30:
end while
31:
Return the answer A <- arg max, P(g Xtait)
A AutoToM Implementation Details
A.1 Algorithm
We summarize the overall AutoToM algorithm in Algorithm Automated Bayesian Inverse Planning (Section (3.3 corresponds to Lines 2-6. Automated Agent Model Discovery (Section 3.4) corresponds Lines 8 30: Information Extraction Lines 8-9, Initial Model Proposal in Lines 12- 13, and Model Adjustment in Lines 11 11-30.
A.2 Automated Bayesian Inverse Planning
Hypothesis Sampling. At each timestep, hypotheses for the latent variables are generated using a Large Language Model (LLM) as the backend, guided by the observed variables. Specifically, when the state is not explicitly provided, the LLM acts as world model, tracking state changes in the story based on the previous state and current actions. For an agent's observation, the LLM is prompted to adopt the perspective of a character, simulating what that character might see, know, hear in the given environment (c.g., inside a closed room). no new observation is available at observations nor update the belief. Additionally, the LLM
specific timestep, we neither generate proposes plausible hypotheses for the agent's 's belief and goal based on the available information.
Hypothesis reduction. We examine all local conditional probabilities involving single uncertain variable with multiple hypotheses and eliminate those hypotheses that result in significantly low likelihood values. For example, in P(o 8²), where s' represents determined state, any observation hypothesis that yields low likelihood for this term discarded. This approach reduces the computa- tional cost of estimating P(b o (,,11 Similarly, the same principle is applied to P(a b b²,g")
15

---

Table 4: Potential variable adjustments, including introducing goal, belief, observation, and interactive state (for high-order ToM). We show the corresponding local conditionals before and after introducing the new variables.
New Var.
Before
After
P(a ss) P(a' b)
P(a' s' P(g)
P(a" b/ P(4)
Goal
P(a") P(a")
P(a" s P(g)
P(a' blg))(())
P(a') (t)
P(a' P | s-,80-1)
Belief
P(a' 16
P(a' 1 P(.') b
Observation
P(b 1)
P(b 1o,,0" s²)
Interactive State
b((²)
b(is))
and P(u b', g") where unlikely belief hypotheses are removed to further reduce computational complexity.
A.3 Automated Agent Model Discovery
During model adjustment, AutoToM iteratively adjust the proposed model by considering two types of model adjustments: variable adjustment and timestep adjustment. Table summarizes possible variable adjustments at each timestep.
Given a ToM problem and context, when exploring different models during agent model discovery, AutoToM can reuse extracted information, proposed hypotheses about certain mental variables, and local conditionals from previously computed models to avoid redundant computation.
In Algorithm]
I
we configure the hyperparameters as follows: = 0.02, Unin -0.693.
A.4 Recursive Reasoning
Interactive Partially Observable Markov Decision Process (I-POMDP) extends POMDP to multi- agent settings by introducing the concept of interactive states, which include agent models into the state space capture the recursive reasoning process [12]. We denote is;,1 as the interactive state of agent i at level I. For two agents i and j. where agent is interacting with agent j. the interactive states each level are defined as:
Level 18;,1 =
Level 1: is;,1 = (s, b,.0. 9j) where 6y, is a distribution over j's interactive state at level O,
isj,0
The framework provides generative model for agents: given agent i's belief of interactive state b(is;.), its action policy will be (a,lisi,,, (9:),, and its utterance policy will be (u.]iss,,,9 .9:).
In our implementation, we sample one possible state based on b(s) at level to approximate the state at level [ 1 as imagined by the agent at level L. We can recursively apply this process until reaching level Based on the state sampled for level 0, we can then conduct the typical automated BIP based on the model structure at that level. This approach can be conveniently applied to arbitrary levels of recursive reasoning, allowing us to answer higher-order Theory of Mind questions using the same method.
A.5 Agent Model Space
To apply Bayesian Inverse Planning (BIP) across various scenarios, we define the mental variables and their causal relationships with agent behavior using family of probablistic agent models. These models accommodate different levels of complexity in how agents behave and reason about their environment.
16

---

At each timestep t. the observable variables are represented by:
x = {ailieNx where Nx = {8". n'}
Here, the state s' always appear in X while either a' (action) u' (utterance) is included at timestep t, depending on whether physical motion or verbal communication is presented. In some cases, a' only used to update the state and does not affect the inference of beliefss goals, while in other scenarios it can be crucial for inferring hidden mental states (e.g.. an agent's 's belief or goal).
The latent variables are denoted by
V = fo/NeNy where Ny = (o',b'.g')
Here, the observation o' is only included when the agent's belief b' is part of the model, as it updates B.. The goal g¹ included only if it influences action and relevant inference. In cases of higher-order recursive reasoning among multiple agents, the belief over the state b (s*) extends to belief over an interactive state b (is*).
Combining these choices at each timestep yields model space with 30 possible configurations:
Action/Utteeracce: which one is included (2 options).
Belief/Observation: no belief, belief of state, belief of interactive state, belief of state, or belief of interactive state observation (5 options).
ActioonUUterancee/Goon no goal (action(utterance) irrelevant), action(utterance) only, or action(utterance) goal (3 options).
Over time interval from i, to f, this scales to 30 possible models.
Examples. addition to the Markov Decision Process (MDP), Partially Observable Markov Decision
Process (POMDP), and Interactive POMDP (I-POMDP) models introduced in Section 3.1 we present
additional examples of models from the BToM model space:
Observation Update Model: Used in the ToMi benchmark (see Figure 24), this model focuses how observations update beliefs. Actions present but only serve update states and are irrelevant to the inference questions. This model is well-suited for passive scenarios where the focus is on understanding how hidden states produce observable evidence and how the agent updates its beliefs about the world.
POMDP Variant without Goal: A partially observable scenario in which goals are trivial or irrelevant. This variant emphasizes how partial observability affects belief formation and action selection, without explicit goal-driven behavior.
B AutoToM: Model Improvement from Human Feedback
AutoToM provides strong interpretability and can improve with human feedback. We built a de- bugging tool, simplified version displayed Figure g that shows an example incorporating human-in-the-lowe feedback with AutoToM, For given question, the interactive interface provides clear reasoning justifying its choice. The model lists the mental state variables and actions of agents, which were extracted sampled with the highest probability. Using this information and the highest calculated probabilities, the model explains its reasoning. After the user understands AutoToM's reasoning, they can identify potential faulty reasoning and provide feedback. Providing human feedback can help improve model reasoning.
In the example BigToM problem in Figure 8 the model initially extracts the wrong mental state variables for Kofi. The user can easily identify this error from the model explanation and give feedback. The user reflects on the model about the lack of details needed for Kofi's goal and Kofi's incorrect observation. AutoToM can use this updated feedback to clarify essential information, update its reasoning, and improve its accuracy.
17

---

Koli chef popular Ghanaian restaurant. Koli to prepare Iraditional groundnul soup the dinner Kofi adds small container of peanut butter to the soup pet the stove. An apprentice chet mistakenly thinking it was seasoning paste removes peanut butter soup replaces tomato paste. contimues cooking the soup.
Strong Interpretability
Does Kofi believe the contains peanut butter or tomato paste?
Koti believes that the soup contains tomato paste.
Explain why.
paste soup, highly probable that Kofi believes the soup has tomato paste. Kofi's acton of continuing cook thke soup aligns this Kaff's goal of preparing traditional growndnut.
Kaff's obsersation
Human-in-the-llop Feedback
We don't know it Kofi sees tomato paste. Koff's goal lacks the wants peanut the soup.
Recomputing with human feedback > Kofi believes the soup contains peanut butter.
Despite Kof's 's undetermined observation, Kafi contimuig cooking the soup aligns strongly with his goal that he wants to prepare a traditional groundusl scup with peanzi
butter belief the soup contains pemnut
Figure A debugging platform showcasing AutoToM's interpretable explanations for its model choice and learning from human feedback to correct its decision for a sample Big BigToM backward belief problem.
Table 5: Token cost and inference time comparison on MMToM-QA (lower is better). "K" denotes thousands tokens, and "s" denotes seconds.
Model
Avg. #Tokens per Question (K)
Avg. Inference Time (s)
AutoToM
8.0
8.5
03-mini-high
10.9
21.6
Gemini 2.0 Flash Thinking
8.8
6.1
C More Results and Implementation Details for Experiment 1
C.1 Token Cost and Inference Time Comparison
We evaluate the computational efficiency of AutoToM compared to large reasoning models in terms of token cost and inference time. Table 5 reports the average number of consumed tokens per question and the average inference time on the MMToM-QA benchmark, which is computationally demanding due its long contexts. Results show that AutoToM achieves substantially higher reasoning performance with comparable or lower computational cost.
C.2
Per-type Accuracy on All Benchmarks
In Tables
present the results of AutoTToM and baselines each question type of all benchmarks ks. Here we compare general methods that can be applied to all benchmarks.
10
C.3 Additional Benchmarks
We evaluated AutoToM on additional benchmarks, FANToM (25 for its challenging scenarios and OpenToM 55 affective Theory of Mind questions.
C.3.1
Evaluations on FANToM
To further demonstrate AutoToM 's ability to solve false-belief tasks in more complex scenarios, we tested AutoToM on FANToM. We randomly selected a subset of 200 false-belief first-order questions with short contexts due to budget constraints.
18

---

Table 6: Detailed accuracy for ToMi.
Question Type
First order
Second order
Reality
Memory
All
Llama 3.1 70B
73.75
56,25
100.00
100.00
72.00
GPT-40
80.25
62.25
100.00
100.00
77.00
Gemini 2.0 Flash
58.50
58.25
100.00
100.00
66.70
Gemini 2.0 Pro
75.00
54.75
100.00
100.00
71.90
SyMbolicToM SimToM
98.75
98.25
100.00
98.00
98.60
84.75
65.00
100.00
100.00
79.90
DeepSeek-R1
90.75
82.75
100.00
100.00
89.40
Gemini 2.0 Flash Thinking
83.25
61.75
100.00
100.00
78.00
03-mini-high
79.50
53.25
100.00
100.00
73.10
BIP-ALM
58.00
56,25
56.00
43.00
55,60
LIMP AutoToM (w/ GPT-4o)
43.50
44.50
44.00
50.00
44.60
95.00
77.50
93.00
100.00
88.30
Table 7: Detailed accuracy for BigToM.
Question Type
Forward TB
Forward FB
Backward TB
Backward FB
All
Llama 3.1 70B
93.75
81.00
57.00
60.50
77.83
GPT-4o
96.00
88.50
63.50
62.00
82.42
Gemini 2.0 Flash
94.25
87.50
77.50
51.00
82.00
Gemini 2.0 Pro
96.00
93.75
70.00
68.50
86.33
SimToM
92.50
90.00
25.00
75.00
77.50
DeepSeek-RI
89.75
90.50
74.50
82.50
86.25
Gemini 2.0 Flash Thinking
94.75
91.50
77.50
47.00
82.83
03-mini-high
93.25
90.75
78.50
75.00
86.92
BIP-ALM
71.75
32.50
69.50
24.00
50.33
LIMP
40.75
77.75
43.00
90.00
61.67
AutoToM (w/ GPT-4o)
91.25
93.75
73.00
78.50
86.92
Table 8: Detailed accuracy for MMTOM-QA.
Question Type
Belief
Goal
All
Llama 3.1 70B
51.33
36.33
43.83
GPT-4o
55.67
32.33
44.00
Gemini Flash
62.67
33.33
48.00
Gemini 2.0 Pro
57.00
44.67
50.84
SimToM
75.67
26.33
51.00
DeepSeek-R1
63.00
36.33
49.67
Gemini 2.0 Flash Thinking
73.33
34.67
54.00
03-mini-hige
88.67
40.67
64.67
BIP-ALM
64.33
48.00
56.17
LIMP
60.00
50.67
55.33
AutoToM (w/ GPT-4o)
96.67
69.33
83.00
Results. AutoToM, with GPT-4o backend, achieved 72.7%, outperforming the GPT-4o baseline, which achieved 57.5%. AutoToM, with Gemini 2.5 Flash backend, achieved 77. 77.9%. outperforming the Gemini 2.5 Flash baseline, which achieved 38%. With either model as the backend LLM, AutoToM improves upon the original baselines.
Analysis. AutoToM is able to solve false belief questions by extracting the essential variables. In FANTOM, AutoToM extracts the state of the conversation (the agents in the conversation, if the main agent currently in the conversation, and the topics discussed), utterances, and observation of the main agent (depending whether they in the conversation not) to infer belief. In contrast,
19

---

Table 9: Detailed accuracy for MuMA- -TMM
Question Type
Belief
Goal
Belief of Goal
All
Llama 3.1 70B
68.67
51.33
47.33
55.78
GPTT-4o
85.33
57.00
48.33
63.55
Gemini 2.0 Flash
68.33
50.67
47.00
55.33
Gemini 2.0 Pro
63.00
66.67
57.00
62.22
SimToM
54.60
43.50
44.80
47.63
DeepSeek-RI
74.67
53.33
62.33
63.44
Gemini 2.0 Flash Thinking
95.33
79.00
73.33
82.56
03-mini-high
74.00
67.67
68.33
70.00
BIP-ALM
41.20
34.10
30.60
33,90
LIMP
93.40
67.70
68.70
76.60
AutoToM (w/ GPT-4o)
88.33
77.00
79.00
81.44
Table 10: Detailed accuracy for HiToM.
Question Type
Order
Order
Order
Order
Order
All
Llama 3.1 70B
65.00
47.50
22.50
20.00
20.00
35.00
GPT-4o
92.50
65.00
40.00
27.50
25.00
50.00
Gemini 2.0 Flash
95.00
70.00
50.00
27.50
20.00
52.50
Gemini 2.0 Pro
100.00
62.50
50.00
37.50
37.50
57.50
SymbolicToM SimTTMM
62.50
57.50
25,00
32.50
45.00
44,50
100.00
77.50
60.00
60.00
57.50
71.00
DeepSeek-R1
95.00
80.00
55.00
35.00
17.50
56.50
Gemini 2.0 Flash Thinking
100.00
85.00
72.50
50.00
60.00
73.50
33-mini-hige -highh
100.00
72.50
65.00
60.00
77.50
75.00
BIP-ALM
10.00
17.50
10.00
20.00
15.00
14.50
LIMP
5.00
10.00
2.50
7.50
6.50
AutoToM (w/ GPT-4o)
95.00
75.00
70.00
67.50
55.00
72.50
the two baselines struggle to accurately extract and track the agent's observation throughout the conversation.
C.3.2 Evaluations Affective Reasoning in OpenToM.
We evaluated AutoToM's affective ToM by extending the causal structure to include attitude and preference (all other components unchanged) and testing on all 596 OpenToM attitude questions.
Following OpenToM [50] we used Macro-F1 as the evaluation metric. The random baseline 0.33. GPT-4o achieved 0.48, while AutoToM with GPT-4o backend outperformed with a score of 0.56. AutoToM also approached the performance of the large reasoning model 03-mini-high (0.60), indicating its strong affective reasoning capability.
Results.
Analysis. Answering the attitude questions does not require inverse planning, since the model can just directly perform forward estimation of attitude based on observed events and preference. This explains why AutoToM performed similarly compared to 03-mini-high, This is consistent with results for other question types that do not require inverse planning, such level (no ToM) and level I action questions shown in Figure 4a. However, even in the case where inverse planning is not required, AotoToM still scores higher than backend LLM (GPT-40). We attribute this to AutoToM 's ability to extract and focus on variables that are causally relevant to the task, while filtering out spurious cues by design (see [50]. Section 2.5) that may mislead GPT-4o.
C.4 Full Results of the Ablation Study
Table IT shows the performance of ablated methods compared to the full AutoToM method on all benchmarks.
20

---

Table 11: Results of ablated methods compared to the full AutoToM method.
Method
ToMi
BigToM
MMToM-QA
MuMA-ToM
Hi-ToM
All
w/o hypo, reduction
87.60
86.17
80.83
81.67
69.50
81.15
w/ POMDP
76.00
86.50
82.67
50.78
67.00
72.59
w/o variable adj.
85.80
78.25
79.00
77.89
66.50
77.49
w/ last timestep
68.40
77.83
76.50
78.33
44.50
69.11
w/ all timesteps
86.00
79.09
76.17
79.33
69.00
77.92
AutoToM
88.30
86.92
83.00
81.44
72.50
82.43
Table 12: Comparison of ablated models and the full model on the averaged number of tokens per question (in thousands). Lower better.
Method
ToMi
BogToM
MMToM-QA
MuMA-ToM
Hi- Hi-ToM
All
w/o hypo. reduction
13.8
24.4
15.3
w/ POMDP w/o variahle adj.
14.9
5.5
6.2
20.0
18.8
13.1
8.5
6.1
8.0
14.0
10.0
9.3
w/ last timestep
7.8
6.1
3.9
11.6
4.0
6.7
w/ all timesteps
14.2
7.7
44.5
16.4
12.4
19.0
AutoToM
9.8
6.5
8.0
13.6
12.0
10.0
Table 13: Comparison of ablated models and the full model on the averaged number of API calls at
inference per question. Lower is better.
Method
ToMi
Big RigToM
MMToM-QA
MuMA-ToM
Hi-ToM
All
w/o hypo, reduction
38.91
13.99
21.72
70.73
72.58
43.59
w/ POMDP
36.25
8.32
12.89
42.10
51.73
30.26
w/o variable adj.
22.91
12.99
17.51
35.76
29.81
23.80
w/ last timestep
21.60
12.76
7.72
28.39
9.39
15.97
w/ all timesteps
39.83
15.95
101.28
43.25
36.27
47.32
AutoToM
32.23
13.81
17.60
35.08
36.45
27.03
In Table and 13
we compare the ablated methods and the full model on the averaged number of tokens per question (in thousands) and the averaged number of API calls at inference per question.
C.5 Detailed Inferences
Figure 9
shows the detailed procedures of Bayesian inferences for the qualitative example in Figure 5
C.6 Qualitative Results
Among general methods, AutoToM achieves sfte-off-heeart results across all benchmarks. We provide two qualitative examples to illustrate the effect of variable adjustment (example 1) and timestep adjustment (example 2). These examples also demonstrate the interpretability of AutoTTM,, as the constructed model offers us insights into how the method is modeling the agent behavior for the inference.
Example 1: BigToM (Backward Belief Inference)
Story: Kavya is florist in vibrant Indian market. Kavya wants to create beautiful bouquet of fresh roses for a customer's anniversary celebration. Kavya sees a batch of roses in her shop that appear to be fresh and vibrant. Unbeknownst to her mischievous monkey sneaks into the shop and nibbles on the rose petals leaving them damaged and unsuitable for the bouquet. Kavya starts arranging the bouquet using the roses she initially saw.
21

---

Detailed Procedure of 3
Bayesian Inference with proposed model from 2 Initial Model Proposal
Sampled Hypotheses and Estimated Local Conditionals (t =
P(o [s9 = as P(oi (s9 - 0.
Nici picher.
ey Noor see milk pitleer
(8 P(u" (8] - 0.5
s!
b
af
at Noor makes the cappuccina using the milk in the pitcher.
Py piicher
P 185
of)
= 0.9
at The milk pitcher conlains alitond milk
P(
My believes milk.
P(
18 o2) 0.1
Posterior Update
P(b'stta") Σ "0010".o'966""""""699
PCD = u.1, P. = 09 (fromt u)
p(ajis".a)) (""""""""))<"""""""""""""""""
P(bfist, =
Detailed Procedure of (5) Bayesian Inference with adjusted model from @ Model Adjustment
Sampled Hypotheses and Estimated Local Conditionals (t 1)
P(a 18) 0.1 P(a" (b
e: Nour sac aleoad talk the pridur.
Plof I=*)= a.s P (8t) 0.5
#y Nocraoa not milk the
Noor makes the capgraccino using pitcher
bt
al
g
Mi: Noce beleeves the milk pitcher rentiin alnumd milk.
P(B bi
P(B Ik 1.05)= = 0.1
8: cappuccino with out milk.
The milk prtcher contains alnowd milk.
P( kt
o() u9
My contains
pitcher
P( b ...)
Posterior Update
P(
Σ wwtp:p/pwww.....ooooooooooooooon
Mang
P(O)) n.1,P()) ( 09 (Fromt
P(a" (0l"")
P(Dli))a')
P(a ((0))) P(L1s)
0.45
Figure 9: Detailed procedures of how Bayesian inferences are conducted for the proposed and adjusted models in Figure(5) In the initially proposed model, the belief inference results in uncertainty due to ambiguous observations. In the adjusted model, where the agent's goal is explicitly modeled, the use of conl-conditooned action likelihood ((a bb, g) instead of P(a" (6) enables more accurate estimation of action likelihoods and leads to improved belief inference with high certainty.
Question: Does Kavya believe the roses are fresh and perfect for the bouquet or damaged by the monkey?
(a) Kavya believes the roses are fresh and perfect for the bouquet. (Correct Answer)
(b) Kavya believes the roses are damaged by the monkey.
Variables in the Initial Model Proposal: State, Observation, Belief
Since the scenario involves only one timestep, a single model suffices. In the initial model, the state of the world indicates that the flowers are damaged after the monkey nibbles on them. However, remains unclear whether Kavya observes the true condition of the flowers. The model lacks crucial information about Kavya's actions, which observable and influenced by her beliefs
22

---

about the flowers' state. These actions can help infer her true belief. Initially, the probability that Kavya believes the flowers are fresh is moderate, P(Kaaya believes the roses are fresh and perfect for the bouquet X 0.50. Without variable adjustment, the model cannot answer the question.
Variables in the Adjusted Model: State, Observation, Belief, Action, Goal
-0.693 and the model cost is C(M) = a MM| 0.04, resulting in utility U(M, -0.733, which does not exceed the utility threshold U min = -0.66 09.693 To address the insufficiency the initial model's utility relative to our termination threshold, we propose enhanced model incorporating state, observation, belief, action, and goal. In this revised model, Kavya's 's actions- specifically arranging the bouquet using the roses- roses-align with her goal of creating beautiful bouquet. These observations allow to infer with high probability that Kavya believes the roses are fresh and suitable for the bouquet, increasing the belief probability With this revised model, the reward is R(M, q) = H(P (( = -0. -0.135 and the model cost C(M) M|M 0.06, resulting in utility UM, q) -0.195, which exceeds our utility threshold min -0.693. Based on the adjusted model, AutoToM can confidently determine the correct answer: (a) Kavya believes the roses are fresh and perfect for the bouquet.
For the initial model, the reward is ((M,g) = -=
P(Kavya believes the roses are fresh and perfect for the bouquer X 0.97.
Example 2: MMToM-QA (Belief Inference)
Video input:
What's inside the apartment: The apartment consists of a bedroom, kitchen, living room, and bathroom. In the bedroom, there coffee table and desk. The kitchen is equipped with four cabinets, fridge, kitchen table, microwave, and a stove. The 3rd kitchen cabinet from the left houses water glass and a dish bowl. Inside the fridge, there are two apples, salmon, a plate, and a dish bowl. The 2nd kitchen cabinet from the left contains water glass, a chips, condiment bottle, and dish bowl. The 1st kitchen cabinet from the left holds wineglass, a wine, and condiment bottle. The microwave contains salmon, and there cupcake in the stove. The 4th kitchen cabinet from the left has a plate. The living room features cabinet, sofa, coffee table, and desk. Inside the cabinet, there are two apples and four books. A plate and remote control are placed on the coffee table. The bathroom is furnished with a bathroom cabinet, which is currently empty.
Actions taken by Mark: Mark is situated in the bathroom. He proceeds towards the kitchen, making his way to the stove. He opens and then closes the stove. Subsequently, he strides towards the 4th kitchen cabinet, opens it, and then shuts it. He then moves to the 2nd kitchen cabinet, opens and closes it, before doing the same with the 3rd kitchen cabinet. Finally, he heads towards the 1st kitchen cabinet, opens and closes it, and is about open the microwave.
If Mark has been trying to get salmon, which one of the following statements is more likely to be true?
Question:
(a) Mark thinks that the salmon is not inside the microwave.
(b) Mark thinks that the salmon is inside the microwave. (Correct Answer)
In this problem, first fuse the information from text and video following Jin et al. (20]. The fused information is structured into 23 timesteps, each corresponding to an action of Mark at the time. We then propose the initial model: State, Observation, Belief, Action, Goal.
Without timestep adjustment. Bayesian inference must be performed sequentially from the first timestep, even though most actions do not contribute to answering the final question. The model
23

---

Table 14: Summary of the ToM benchmarks used in the experiments.
Benchmark
Agent num- ber Multi
Tested concepts
Size
Modality Communication Generation
Evaluation
ToMi 26
First & Second Or- der belief, Reality, Memory
1000
Text
No
Templates
Multiple
agents
choice
Q&A
BigToM
Single
Belief, Action
1200
Text
Procedural
Q&A
agent
generation
MMTOM-QA
Single
Belief & Goal
600 Text & No Video
Procedural
Multiple
20
agent
generation
choice
Q&A
MuMA-TTM
Multi
Belief, social goal and belief of other's goal
900
Text & Video
Yes
Procedural
Multiple
39
agents
generation
choice
Q&A
Hi-ToM 1
Multi
High-order belicts
200
Text
Yes
Procedural
Multiple
agents
Genera-
choice
tion
Q&A
will compute across all timesteps, while the most informative action actually the last one: if Mark wants to get salmon but does not believe there is one inside the microwave, he will not open it.
With timestep adjustment. We begin inference from the last timestep, where the action likelihood P(alb, g) is low when = Mark thinks that the salmon not inside the microwave, and high when b Mark thinks that the salmon is inside the microwave. After performing inference the last timestep, the belief probabilities corresponding to the choices are 0.998 and 0.002. The reward is -0.014, while the model cost is C(M) al M 0.06. This results in utility of U [M, 4) = -0.044, which exceeds the threshold Umin = 0..693, allowing
given by R(M, q) (P ((()(g))(x))
our model determine the final answer without considering earlier timesteps.
C.7 Baseline Implementation Details
For the baselines, we use gpt-40-2024-08 ruct from Hugging Face for Llama 3.1 70B, gemini-2 1amnii2.00fffi 0-flash for Gemini 2.0 Flash, 001-2200ppr--- gemini-2.0-pro--epp--22.. o-pro-exp-02-05 for Gemini 2.0 Pro, for 03-mini-high, and deepseek-r1 for
for GPT-4o, mat.mmm-l1ammmm1aa--3..... 133.1-70B-IM Inst-
exp-01121
for Gemini 2.0 Flash Thinking. Deepseek R1. Among the ToM prompting for LLM benchmarks previously tested on the BigToM dataset, e.g., Sim'ToM, they only tested the subset of the entire dataset with questions for forward action and forward belief and did not test on backward belief questions. With the available SimToM code, we tested it on the full BigToM dataset with GPT-4o.
23-mini-2025-0111
SymbolicToM maps out the agents' beliefs throughout stories of different levels reasoning via symbolic graphs. However, the construction of these graphs is specifically designed for the ToMi dataset, where there are fixed actions and sentence formats in the story. Thus is difficult to generalize more open-ended scenarios (e.g., BigToM) stories with multiple agents acting simultaneously s,,, Hi- Ho-M. Therefore, we can only evaluate Symbolic ToM on ToMi (tested with GPT-4o on the full dataset), for which
(e.g.,
designed.
BIP-ALM and LIMP are both models that combine BIP and LLMs to solve ToM problems. BIP-ALM manually defines symbolic representations of observable and latent variables and assumes POMDP. LIMP is designed to only solve two-level reasoning problems. It uses natural language to represent variables. Both methods assume that the goals are about finding object and the beliefs are about the locations of that object in a household environment.
C.8 Benchmark Details
In our evaluation, we test AutoToM BigToM [1] MMToM-QA 20 MuMA-ToM 39 ToMi
26 and Hi-ToM [15]. For ToMi, we use the ToMi dataset that disambiguated container locations in the story and correctly labeled order of reasoning 2 36). For Hi- Hi-ToM, we choose the length
24

---

AutoToM
GPT-4o
03-mini-high
0.7
07
0.7
Online goal inference (full obs.)
0.3
03
0.93
0.81
0.97
0.7
0.3
0.7
03
0.7
Desire inference (partial obs.)
T
0.88
7
0.30
0.52
0.1
0.4
0,7
4
0.5
0.5
Belief inference (partial obs.)
0.3
0.3
0.3
1' = 0.73
T= 0.04
7 = 0.03
Figure 10: Comparing model and mean human mental state inferences.
1 subset consisting 200 questions across orders (0-4) due to the high cost of testing the full dataset.
Table 14 summarizes the benchmarks used evaluate AutoToM against baselines, detailing key features such as test concepts, input modalities, and the number of agents. The results demonstrate that AoM operates across diverse contexts, infers any mental state, reasons about any number of agents, and supports any level of recursive reasoning.
D More Results and Implementation Details for Experiment 2
D.1 More Results
We provide the scatterplot of human and model judgment fits for all three tasks in Figure 10
D.2 Implementation Details
Scenario Selection and Adaptation. the online goal inference task, we selected all usable scenarios (where the human data for each scenario is displayed in the plot) from 4 For the other two tasks, we adapted from [3]. where the original stimuli are grouped into distinct types. We selected one representative scenario from each type, resulting in unique experimental scenarios. This selection was motivated by the fact that scenarios within the same type are highly similar (only minor variations in agent starting positions), which posed challenges for creating clear and distinct natural language narratives. It was also supported by the original study's finding that desire judgments varied minimally within scenarios of the same type.
Stimuli Translation. All selected scenarios were translated into natural language descriptions. The generation of the captions was guided by the following principles: (1) We aimed for statements that were complete, clear, and detailed, fully capturing the scenarios and agent trajectories. (2) We focused on describing only what could be objectively observed (physical states, visibilities, and the agent's
25

---

Example Experiment Stimuli
Natural Language Caption
1. Andy moves closer all objects (A, B, and C) where C closer.
7--10
10
Andy continues walking.
Online goal inference
3. Andy does not move in the direction of but direction
3- X
13
Andy walks directly towards B, but not or Andy keeps walking.
Frome 1
Andy having lunch from one of three food trucks: Korean, I ebanese Mexican, Initially, Korean truck spot and within Andy's line of sight, and the Lebanese truck is in spot B, without Andy's the Korean truck to see what is in spot B. He then the Lebanese truck Andy walks the Korean truck spot A.
Food truck experiment (including desire and belief inference)
Frame
sight. Andy
past
B. Then,
Figure 11: Example inference task scenarios and translated captions in natural language.
actions), without making assumptions about the agent's 's mental states. Please refer to Figure for caption examples.
Rationality Assumption. We incorporated the assumption of approximately rational agents, ensuring consistency with the original studies. This assumption was integrated into the prompts used for estimating action likelihoods in AutoToM. To ensure fair comparison, the identical assumption statement was also included when testing the GPT-44 and 03-mini-high baselines.
Baseline Evaluation Details. When testing the baselines,
included the same captions with contexts in the prompt to ask baseline models (1) generate a series of goal probabilities with regard time steps in task I (online goal inference), and (2) provide confidence ratings on 7-point scale for each belief/goal hypothesis in task 2 (food truck experiment). This process mirrors the judgment task given human participants in the original experiments.
E
More Results and Implementation Details for Experiment 3
E.1
More Results
In the early stages of the episode, GPT-4o may become overly confident in certain hypotheses that lack sufficient cues inspire confidence. some cases, such guesses turn out be correct, resulting in increased speedup. However, more often than not, these hypotheses are incorrect. This can lead to assistance plans that are misaligned with the main agent's ss goals and cause unnecessary state changes (e.g., moving irrelevant objects), ultimately wasting the helper agent's 's time and reducing the speedup. In contrast, AutoToM recognizes these situations as uncertain and avoids taking actions.
E.2 Online Mental Inference
The ability infer and track others' mental states from dynamic cues is key to effective social interactions. We show that AutoToM is inherently well-suited for online mental inference by framing into sequential Monte Carlo (SMC: (8] approach that maintains multiple weighted hypotheses about an agent's 's hidden mental states. We first generate set of hypotheses (Sampling) and assign weights each hypothesis using AutoToM (Weighting). As new observations arrive, these weights guide the selection of promising hypotheses and the generation of new hypotheses. The retained hypotheses carry their weights forward, while AutoToM evaluates the full past trajectories to assign weights to the new ones (Propagation). Based on the latest observation, AutoToM then reweights each particle. This particle-tracking method approximates the evolving posterior over mental variables in real time, continually refining both the hypotheses and their likelihood estimates.
26

---

E.3 Implementation Details
As shown in Figure 2. online mental inference with AutoToM supported the embodied decision- making in Experiment 3, where used the inferred goal of the main agent each step to guide the planning the helper agent. Specifically, the helper began to assist once the most probable goal hypothesis reached sufficient confidence level (0.8 for picking object and 0.6 for placing the object at a location).
During online mental inference, we maintained K 5 particles for hypotheses and set weight threshold of 0.1 for particle filtering. As new observations arrived, promising hypotheses with weights greater than or equal to 0.1 were retained, and new hypotheses were generated to restore total particles.
F Prompts used in Auto ToM
F1 Information Extraction
We use the following prompts to extract information for each variable in a given question.
Identifying the main agent
Find the name of the character that we need infer about in the question and choices. Only output the name. Do not answer the question.
Question: [Question]
Choices: [Choices]
Character name:
Identifying all the agents
Extract the names of all the characters from the story and question. Provide only the names or roles, without any additional information. Do not answer the question. Your response should be list containing the names, like ["namel", "name2")..
Story: [Story]
Response:
Identifying the mental variable be inferred
Choose the variable that best summarizes the information about the differences that the choices contain. Only output the variable.
Variables include: [Variables]
Choices: [Choices]
Variable:
Identifying extra information in the question
If there is any assumed information in the question given (a conditional clause starting with specific words like "if" is contained), rewrite it as declarative sentence. Do not include any questions in the extra information. Do not make details for the information. Use the original wording. Otherwise, output "NONE".
Question: [Question]
Extra Information:
27

---

Extracting actions of the main agent
Extract the actions of [Inferred_agee agent] in the story verbatim without changing any of the original words, pluralizing the words, adding in [Inferred [agent or any other name, replacing any of the words, replacing pronouns with names replacing any names with pronouns. Actions of [ agent] are defined as events that will change the world state, c.g.. [Inferred_agent moving new location is action but [Inferred_ being location is not an action. If [Inferred agent] says something, the whole sentence (with "replied", "said") is seen as an action.
Do not change the names of any of the agents, if there is not name and only a pronoun then just leave the pronoun. There can he more than one agent or more than just the inferred agent. If there are multiple actions in sentence then they should be extracted as one single action, without changing any of the original words, such as pluralizing the words, replacing any of the words, replacing pronouns with names, or replacing any names with pronouns, and do not add any words.
Do not insert actions, pronouns, other words that are not explicitly stated in the text. Do not separate the objects the same action.
Do not add any pronouns. Keep the commas, if any.
Only actions that have already occurred at the time can be considered clearly stated. Again, only extract actions performed by [Inferred agent].
The output format should be: ["aaa.", "bbb.", ...]. Output only this list.
Story: [Story]
Extraction:
Extracting actions
Determine if [Character]'s action(s) is clearly stated in the story.
The action(s) cannot be the character's inner thoughts. Only actions of [Character] that have already occurred, or are currently taking place can be considered clearly stated.
If it's more like [Character]'s desire or goal, it does not count as an action. [Character]'s utterance is considered sentence, if any). Do not change any of the original wording. Answer in the form of a list. The first element of the list contains the option A B. means clearly stated, and B means not clearly stated.
action (include the verb like "said" "replied" in the evidence
the answer A, include sentence(s) from the original story that serves as evidence, and place it in the second element of the list, without any kind of formatting. Note that there could be multiple action sentences.
Otherwise, the second element can be an empty string. Do not write anything else. Example 1: ["A", "evidence sentence.")
Example 2: ["B", """]
Story: [Story]
Answer:
Extracting beliefs
Determine if the belief of [Character] is clearly stated in the story.
Usually, belief is one's understanding of the state the world or the state of others. subjective attitude towards things does not count as belief. An action or utterance of the agent does not count as belief. Words like "know" or "believe" could be hints for belief. Answer in the form of a list. The first element of the list contains the option A B. A means clearly stated, and B means not clearly stated.
If the answer is A, include sentence(s) from the original story that serves as evidence, and place it in the second element of the list without any kind of formatting. Otherwise, the second element can be empty string. Do not write anything else.
28

---

Example 1: ["A", "evidence sentence.""
Example 2: ["B", ""]
Story:
[Story]
Answer:
Extracting goals
Determine if the goal of [Character] is clearly stated in the story.
Usually, goals refer to person's 's goals or intentions regarding particular event. Moreover, sentence that shows person has been trying do something, or summarizes their efforts of doing something should always be considered a goal. Helping others to achieve their goals also counts as a person's 's goal.
Answer in the form of a list. The first element of the list contains the option A B. A means clearly stated, and means not clearly stated.
If the answer is A, include sentence(s) from the original story that serves as evidence, and place it in the second element of the list, without any kind of formatting. Otherwise, the second element can be empty string. Do not write anything else. Example I: ["A", "evidence sentence."]
Example 2: ["B", ""]]
Story: [Story]
Answer:
Extracting observations
Determine if the observation of [Character] is clearly stated in the story.
Observation refers to the main character's perception an event; is only considered clearly stated when the protagonist's perception is explicitly mentioned, like if they visually see something, visually notice something, or hear something, or any other state that can be perceived by the agent with but not limited to their 5 senses.
character's utterance does not mean that their observation clearly stated, because they might lie.
Answer in the form of a list. The first element of the list contains the option A B. means clearly stated, and B means not clearly stated.
the answer A, include sentence(s) from the original story that serves as evidence, and place it in the second element of the list, without any kind of formatting. Otherwise, the second element can be an empty string. Do not write anything else. Example 1: ['A", "evidence sentence.") Example 2: ["B", """]
Story: [Story]
Answer:
Extracting states
Determine if the story contains the objective state(s) of object or an event. State refers to the physical condition of something the state of the world.
actions agents should be involved the state but can be the result of an action of an agent. For example, "A entered B" is not state, while "A is in B" is state.
objective state statement should not include personal perspectives but should be objective. If person's perception is involved, it is longer considered an objective state.
Answer in the form of a list. The first element of the list contains the option A or B. A means clearly stated, and B means not clearly stated.
the answer A, include sentence(s) from the original story that serves as evidence, and place it in the second element of the list, without any kind of formatting.
29

---

If there multiple sentences, include them all in the second element of the list. Otherwise, the second element can be an empty string. Do not write anything else. Example 1: ["A", "evidence sentence(s)."]
Example 2: ["B", ""]
Story: [Story] Answer:
F.2 Hypothesis Sampling
We use the following prompts to sample hypotheses for the latent variables in the BToM models.
Sampling belief's
Propose [num] hypotheses for the belief of [Character] in the story. Usually, belief is one's view or perspective on a state, and it represents an understanding of the physical state of the world. Do not state any reason for the hypotheses. Do not contain any form of explanation in the hypotheses. Output a list of hypotheses of length [num] in following form: ["aaa.", "bbb."....
Given information: [Information] Ensure that the hypotheses align with the given information perfectly. The hypotheses could be like "[Character] believes that A is in B". First, list out all entities in the given information. Then, formulate hypotheses using all entities. Make sure the hypotheses starts with [Character]. Output the hypotheses the following form: ["aaa.") "] Observation Hypotheses: [] Belief Hypotheses:
Sampling goals
Propose [num] hypotheses for the goal of [Character]. The goal refers to [Character]'s intentions.
Do not provide any explanation for the hypotheses. Do not propose any sentence that's not depicting the goal, like the action belief of [Character].
The wording for hypotheses cannot be speculative.
The proposed goal does not have to too specific, e.g., Andy wants to help others; Andy wants to hinder others; Andy is indifferent towards other's s goals, etc. Given information: [Information]
Ensure that the hypotheses align with the given information perfectly. It means that the proposed [Character]'s goal matches what's 's contained in the information. Output the hypotheses in the following form: ["aaa."]
Goal Hypotheses:
Sampling observations
Propose [num] hypotheses for [Character]'s observation of the world. The observation refers to [Character]'s current perception of events or the world state. It only considered clearly stated when [Character]'s 's perception is explicitly mentioned, like if [Character] sees something or perceives something through other senses. Do not be speculative.
Do not provide any explanation for the hypotheses. Do not propose any sentence that's not depicting the observation, like the action or belief of [Character].
The wording for hypotheses cannot be speculative.
the information contains "not", make sure the verb for perception (e.g., "see", 'perceives') goes before "not" in the hypotheses. e.g. use 'sees that A is not in B' instead of 'does not see that A B' Otherwise, do not include "not" in your hypotheses, and make sure the verb for perception goes first, e.g., 'sees that A is in B'.
30

---

Given information: [Information]
Ensure that the hypotheses align with the given information perfectly. means that when the person has the observation the person will act according to the given information. First, list all entities in the given information. Then, formulate hypotheses using all entities. Make sure the hypothesis starts with [Character]. Output the hypotheses in the following form: ["aaa.")
Observation Hypotheses:
[
F.3 Likelihood Estimation
We use the following prompts to estimate the likelihood of different variables.
Estimating the likelihood of the observation given the state
Determine if the statement is likely, and respond with only either A B.
State: [state]
Here is statement of (inf agent)'s current observation. Only evaluate current observation of (inf _gentt based on the state. Do not imagine anything else. Think about (inf _a___agen]] 's location. (inf agent) is quite likely to observe all objects and events in {inf agent) 's location, and is unlikely to observe states in another location. (inf_ agent] does not appear in the state, inf_agent] can't observe anything. Note that the statement has to be precise in wording to be likely. For example, the treasure chest and container are different wording and they'r different objects.
Determine if the following statement is likely: (statement]
A) Likely. B) Unlikely.
Estimating the likelihood of the action given the goal and belief and belief of goal
Determine if the statement likely, and respond with only either A B.
{inf_agent]'s goal: [goal)
{inf agent) 's belief: [belief)
agenttss 's belief of other's goal: (belief of goal) {inf_ agent]'s action: (action]
When [inf_agent] wants to help, (inf agent] is likely to bring an object to other's desired location, and unlikely grab object away from other's desired location.
When (inf agent] wants to hinder, iinf_ ang_n]] is likely to grab an object away from other's desired location, and unlikely bring an object to other's desired location.
When linf_agent] doesn't know other's goal, Linf_agent is likely to act according to [inf_agent]'s belief.
[inf_agent] wants to help and (inf_agent) believes the object is placed at other's desired location, it's unlikely (inf agent) will move the object. If (inf agent)' goal, (inf agent) 's belief of goal, and (inf agent) 's action do not align in any way, the action is unlikely.
If [inf agent)
Determine if (inf_agent] 's 's action likely.
A) Likely. B) Unlikely.
Estimating the likelihood of the action given the social goal and belief
Determine if the statement is likely, and respond with only either A or B. If it's not certain but it's possible, it's likely.
31

---

{inf agent]'s social goal: (goal) {inf_ agentt't belief: {belicf)
Here is statement of iinf agent]'s action. Think about {inf _anf_a'ge''s goal.
{inf _aaaetten will perform actions according to {inf agent)'s belief, and any action that does not align with the belief is very unlikely, except when (inf agent) goal is to hinder to prevent others. this case (goal hindering others) {inf agent) 's action is only likely when it's different from (inf inf_agent] is belief. If (inf (inf_agen) _agent] mental states contain conditions like "When giving information" and the action is not giving information, it' unlikely.
Determine if the following statement is likely: {action]
A) Likely.
B) Unlikely.
Estimating the likelihood of the action given the goal and belief
Determine if the statement is likely, and respond with only either A or B. If it's not certain but it's possible, it's likely.
{inf_agent)'s social goal: (goal)
Linf_agent]'s belief: (belief)
Here is a statement of {inf {inf_ aagnt)'ss action. The belief stands for (inf agent] current belief. {inf_ agent] is likely to act according to goal and belief concerning certain objects (the wording for objects must be same. You should ignore the correlation of different objects. c.g., plate and apple are two different objects.) Notice that [inl_ lin__a 's belief does not represent the goal.
When belief and goal are irrelevant, and action is driven by goal, it's likely. When belief and goal are relevant (about exactly the same object) and they contradict with action, it's unlikely.
Determine if the following statement is likely: (action)
A) Likely.
B) Unlikely.
Estimating the likelihood of the best action among choices given the goal and belief
Determine if the statement is likely, and respond with only either A or B. If it's not certain but it's possible, it's likely.
{inf agent)'s belief: (belief)
(inf_agent]'s goal: (goal)
If the next immediate actions possible are: [actions)
Determine which immediate action is most possible given the information about Linf_agent's goal and belief.
Determine if the following statement is likely; (action_as a is a better immediate action than (action_b) b).
A) Likely.
B) Unlikely.
Estimating the likelihood of the initial belief
Determine if the statement is likely, and respond with only either A or B. If it's not certain but it's possible, it's considered likely.
Here is statement of the story and (inf aa___tte] initial belief.
There an action that causes the state the main object change. Based on (inf_agent)
observations determine if iinf __gent] perceives the state of the object change.
it is not clearly stated that ( inn agent) perceives then we do not assume that {inf agentt)
perceived the change of state.
32

---

If [inf agent) perceives this change then it is highly likely that (inf agent) belicf aligns with the change of state of the object. If {inf_agenn agent) does not perceive this change if it is unknown if (inf agent] perceives this change then is highly likely that (inf agent] belief does not align with the change state of the object.
Story: {story)
Think about the state of the world and others actions. {inf {inf_ageni) agent) belief change throughout time through other's actions and what [inf_agent] can observe. It also important think about if (inf_agent) observe the same then their belief will change and if not then their belief will remain constant. Use this to determine (inf agent) 's beliefs.
observe other's actions. If
can
Determine if the following statement is likely: [statement] A) Likely.
B) Unlikely.
Estimating the likelihood of the belief given the observation and previous belief
Determine if the statement is likely, respond with only either or B.
{inf agent|'s 's previous belief: {previous_ belief]
aan__agett's observation: !observation
Here is a statement of iin__agent's current belief. If {inf iine_a current belief is aligned with [inf_agent, 's observation, it is very unlikely.
Determine if the following statement is likely: [statement]
A) Likely.
B) Unlikely.
Estimating the likelihood of the belief given the state and previous belief
Determine if the statement likely, respond with only either or previous belief: [belief] State: [state]
Here is a statement of (inf agent)'s 's current belief. If (inf agent)'s 's current belief is not aligned with the state, is very unlikely.
Determine if the following statement is likely: (statement)
A) Likely.
B) Unlikely.
Estimating the likelihood of the utterance
Determine if [inf_agent, I's utterance is likely, and respond with only either A or B. (inf {inf_ aggnt]eentss belief: (belief)
aan__ag Is goal: [goal]
{inf agent]'s utterance: [utterance)
When [inf_agent) 's goal is to help others, (inf_ agent)'ss utterance is likely when it strictly reflects [inf_agent)'s belief, and unlikely it does not reflect (inf_agent)'s belief. When (inf _uf__agen]'s goal is to hinder to prevent others from achieving their goals, (inf agent)*s utterance likely when it's different from finf_agent'ss belief, and unlikely it reflects inn canf_aaget''ss 's belief.
Determine if dinf_agent] utterance is likely.
A) Likely.
B) Unlikely.
33

---

F.4 Initial Model Proposal
We use the following prompts to propose an initial model for question and determine if the question has higher-order beliefs.
Proposing the initial model
What variables are necessary solve this question? Please provide the answer without explanation.
Please select from the following: ["State", "Observation", "Belief", "Action", "Goal"]
The true condition of the environment. This should always be included. Observation: The observed information about the state. Include this when the agent has partial observations of the state.
State:
Belief: The agent's 's current estimation the true state based on the state or its observation.
Action: A move made by the agent, informed by the state or belief. Include this only when directly relevant answering the question.
Goal: The objective the agent trying to achieve. Include this only if "Action" is included.
Question: (example_questionn
Variables: (example answer)
Question: {question)
Variables:
Determining if the question contains higher-order belief
Determine whether the question is about higher-order belief. higher-order belief refers to belief about another person's 's belief, goal, or action. It is not high-order belief if only asks about agent's belief. Please respond with "Yes" or "No". If the answer is "Yes", the question often ends with "Where does A think that B ...!" Otherwise, respond "No".
Question: [A story involving several people.] Where will Jack look for the celery?
Higher-order belief: No
Question: |A story involving several people.] Where does Jack think that Chloe searches for the hat?
Higher-order belief: Yes
Question: (question)
Higher-order belief:
34

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Main claims made in the abstract and introduction accurately reflect the paper's contributions and scope. Our main contributions include: (I) a unified formulation of model- based ToM inference; (2) the first approach of automated agent model discovery, Auto for scalable model-based ToM; and (3) a systematic evaluation of Auto on multimodal ToM benchmarks, cognitive studies and embodied assistance tasks. The results show that Auto AutoToM outperforms state-of-the-ar LLMs and large reasoning models, establishing a scalable, robust, and interpretable framework for machine ToM.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results be expected to generalize other settings.
It is fine include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
The limitations are discussed in Section 5
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model vell-specifiiattin, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimess.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
35

---

3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: The paper does not include theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results. All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proof' can either appear the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented
by formal proof's provided in appendix supplemental material. Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: The paper and the code fully disclose all the information needed reproduce the main experimental results.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is a dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access the model. In general. releasing code and data often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how replicate the results, access a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm.
(b) the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (c.g., to registered users), but it should be possible for other researchers have some path reproducing verifying the results.
36

---

5. Open access to data and code
Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [Yes]
Justification: The paper provides open access the data and code, with sufficient instructions to faithfully reproduce the main experimental results.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.cm cc/
wublic//guide//Ddeeebbiiiss www.asaboioiiioooooooooooooo for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" an acceptable answer. Papers cannot be rejected simply for including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https for more details.
//nips //ips.cc/puut
guides/ PwoddSSumiissioooool....ooo
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Question:
Answer: [Yes]
Justification:
The paper specifies all the training and test details.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification:
The paper reports information about the statistical significance of the experi- ments.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emor bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
37

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative еπτог rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: For each experiment, the paper provides sufficient information on the computer resources needed reproduce the experiments.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https::/neurips.. cwtpurrisssccppppeeeeo http://wwwwppp.cppp....//aaaaaaa
Answer: [Yes]
Justification: The research conducted in the paper conforms, in every respect, with the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make to preserve anonymity (e.g., s if there is special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [NA]
Justification: There is no societal impact of the work performed.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
38

---

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), faimess considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but encourage authors to take this into account and make best. faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification:
The creators or original owners of assets (e.g., code, data, models), used in the
paper are properly credited.
Guidelines:
The answer NA means that the paper does existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset used and, possible, include a URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
39

---

If assets are released, the license, copyright information, and terms of use in the thcode. has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
package should be provided. For popular datasets, paperswi
datasets.papersstt
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors encouraged to reach out the asset's s creators.
13. New assets
Question: Are assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: New assets introduced in the paper are well documented and the documentation is provided alongside the assets.
Guidelines:
The answer NA means that the paper does not release assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
40

---

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [Yes]
Justification:
The paper describes the usage of LLMs.
Guidelines:
The answer NA means that the method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy (https: /ttp:::///eupp....// hctps////oooeeeeccc//////////// for what should or should be described.
41


Reviews:
[{"review_id": "kI1hViPjPx", "reviewer": "Reviewer_J3da", "summary": "The 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.", "questions": "(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.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\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?", "was_revised": true, "final_justification": "Raising my score from 4 to 5 based on the extra detailed explanations, comparisons with prior work, and extra experiments. It's particularly great to hear that AutoToM is efficient w.r.t. reasoning models!", "rebuttal": "Thank you for your insightful review and for recognizing that our paper addresses an important challenge and presents strong experiments. We will address your concerns as follows.\n\n> **W1. The paper could be written more clearly, with a more digestible and representative example figure.**\n\nThank you for the constructive feedback. In the paper, *Figure 1* overviews the AutoToM procedure; *Figure 3* illustrates example hypotheses and calculations for Bayesian inverse planning; and *Figure 5* shows a real example of AutoToM’s model adjustment and inference in a BigToM false-belief scenario. *Appendix C.3* presents two real examples demonstrating variable adjustment (Example 1) and timestep adjustment (Example 2) in AutoToM.\n\nFollowing your suggestion, we will add an additional example figure that clearly presents every element (each hypothesis, each calculation) and every step of the process (initial model → inference results → variable adjustment → inference results → timestep adjustment → …), with easily digestible annotations.\n\n> **W2. Some literature should be discussed more deeply.**\n\nThank you for the suggestion. We will include more discussion on thought-tracing and other related works. We clarify how AutoToM differs below.\n\n**Key difference 1: explicit agent modeling**. *AutoToM* constructs agent models (Bayesian networks) and conducts Bayesian inference based on them, specifying relevant mental variables and their causal relations. They describe the decision-making processes that lead to the observed agent behaviors. In contrast, *thought-tracing* maintains hypotheses only about the mental variable asked in the question, without the structural representations of other related mental variables. This explicit modeling yields higher accuracy on hard benchmarks and robustness to wording or superficial story changes (e.g., thought-tracing needs wording changes for MMToM-QA, while AutoToM does not). Moreover, error sources are easier to identify in AutoToM by inspecting its model structure and variables.\n\n**Key difference 2: inference complexity minimization**. *AutoToM* discovers a minimally sufficient model per query: it proposes an initial model and adds variables and/or timesteps only when they improve utility. This avoids under-/over-modeling and scales with longer contexts, more agents, and deeper recursion. As shown in Exp. 3, this complexity control makes AutoToM much more efficient than reasoning models on downstream embodied assistance tasks. By contrast, *thought-tracing* only reweights hypotheses and does not adapt hypothesis complexity or timesteps.\n\n> **W3. Would be good to add a token cost vs performance discussion.**\n\nFollowing your suggestion, we added a cost analysis of AutoToM vs. other methods on MMToM-QA, the most challenging dataset due to long context. The average number of tokens per question for AutoToM, o3-mini-high, and Gemini 2.0 Flash Thinking are 8.0k, 10.9k, and 8.8k, respectively. This shows that AutoToM outperforms large reasoning models while using fewer tokens.\n\n> **Q1. Difference in reported baseline accuracies; adding FANToM.**\n\nThe discrepancy in baseline performance on MMToM-QA is caused by:\n\n1. Dataset modifications: The authors replaced object and room names with symbols like A, B, etc.\n\n2. Modalities: The authors used a text-only version, while we use translated multimodal fusion. The text-only version is significantly easier—with the same model, o3-mini achieves 71.5% accuracy on text-only versus 64.7% on the multimodal version.\n\n3. Number of questions: The authors evaluated on a subset of 200 questions, whereas we tested on the full benchmark of 600 questions.\n\nSimilarly, thought-tracing also used subsets for other benchmarks. Additionally, they relabeled the answers in BigToM.\n\n**FANToM:** To further demonstrate AutoToM’s ability to solve false-belief tasks, we tested AutoToM on FANToM. We randomly selected a subset of 200 false-belief first-order questions with short contexts due to time and budget constraints. \n\n**Results.** AutoToM, with a GPT-4o backend, achieved 72.7%, outperforming the GPT-4o baseline, which achieved 57.5%. AutoToM, with a Gemini 2.5 Flash backend, achieved 77.9%, outperforming the Gemini 2.5 Flash baseline, which achieved 38%. With either model as the backend LLM, AutoToM improves upon the original baselines. \n\n**Analysis.** AutoToM is able to solve false belief questions by extracting the essential variables. In FANToM, AutoToM extracts the state of the conversation (the agents in the conversation, if the main agent is currently in the conversation, and the topics discussed), utterances, and observation of the main agent (depending on whether they are in the conversation or not) to infer belief. In contrast, the two baselines struggle to accurately extract and track the agent’s observation throughout the conversation. \n\n> **Q2. In Figure 4, what cases does AutoToM excel in, and what challenges remain to improve \"Level 2+ Belief\" from 70% to 100%?**\n\n**Where AutoToM excels**. AutoToM is notably stable and scales effectively as contexts grow longer and more agents are introduced, outperforming large reasoning models in robustness. This is due to its explicit construction and adjustment of a causal agent model, followed by Bayesian inverse planning over latent mental variables, rather than relying solely on prompt heuristics.\n\nAutoToM also performs better at goal inference, which all LLMs and large reasoning models struggle with. This is because AutoToM conducts explicit inverse planning, which reasons about how agents act conditioned on certain goal hypotheses and other relevant mental variables (e.g., beliefs). In contrast, LLMs and large reasoning models often produce incorrect goal inferences due to spurious action patterns, particularly when there is partial observability, where action plans are often not obvious due to incomplete information or false beliefs.\n\n**How to push toward 100%**. Errors mainly arise from noisy multimodal extraction, imprecise probability estimates, and occasionally missing mental variables. For Level-2+ Belief, recursive belief sampling (from level *l* down to 0) **accumulates errors as recursion deepens**. Improving base (non-recursive) belief inference and BIP estimation will reduce errors. Other errors occur when AutoToM misses relevant mental variables, producing an insufficient model. We will mitigate this with stronger LLM backends and framework-level advances, including learning-based approximations to replace costly nested BIPs.\n\n> **Q3. Would AutoToM also improve performance for benchmarks that sometimes require affective ToM such as OpenToM?**\n\nThanks for the insightful question. We evaluated AutoToM’s affective ToM by extending the causal structure to include *attitude* and *preference* (all other components unchanged) and testing on all 596 OpenToM attitude questions.\n\n**Results.** Following the OpenToM paper (Xu et al., 2024), we used Macro-F1 as the evaluation metric. The random baseline is 0.33. GPT-4o achieved 0.48, while AutoToM with GPT-4o backend outperformed it with a score of 0.56. AutoToM also approached the performance of the large reasoning model o3-mini-high (0.60), indicating its strong affective reasoning capability.\n\n**Analysis.** Answering the attitude questions does not require inverse planning, since the model can just directly perform forward estimation of attitude based on observed events and preference. This explains why AutoToM performed similarly compared to o3-mini-high. This is consistent with results for other question types that do not require inverse planning, such as level 0 (no ToM) and level 1 action questions shown in Figure 4a. However, even in the case where inverse planning is not required, AutoToM still scores higher than its backend LLM (GPT-4o). We attribute this to AutoToM’s ability to extract and focus on variables that are causally relevant to the task, while filtering out spurious cues by design (see Xu et al., 2024, Section 2.5) that may mislead GPT-4o.\n\n> **Q4. Why describe AutoToM as more robust? It would be important to specify exactly robust to what behavior.**\n\nThanks for the suggestion. We define robustness as:\n\n1. **Consistent performance under input/task variations.** AutoToM shows markedly lower variance than large reasoning models across different input/task variants, including (i) context length, (ii) number of agents, (iii) recursion level, and (iv) question type, as shown in Fig. 4. AutoToM is also more resilient to wording styles. E.g., thought-tracing requires rewording for MMToM-QA, whereas AutoToM needs no such modifications.\n\n2. **Human-like uncertainty estimation.** AutoToM expresses human-aligned uncertainty in classic cognitive studies as shown in Experiment 2, unlike baselines.\n\n3. **Mental state inference useful for downstream tasks.** AutoToM can be applied to downstream tasks such as embodied assistance. Specifically, while LLM baselines tend to propose uniform goal distributions due to distraction of long context, AutoToM concentrates probability on likely goals that are consistent with agent behavior, enabling helpful assistance.\n\n4. **Human-in-the-loop correction.** AutoToM’s explicit agent models make reasoning interpretable and editable, enabling humans to provide feedback to correct errors when needed (Appendix B).\n\nWhy this robustness emerges:\n\n1. **Explicit modeling + BIP.** AutoToM constructs explicit mental models and performs probabilistic inference using Bayesian inverse planning, grounded in cognitive studies. This reduces sensitivity to superficial story edits that can disrupt direct LLM prompting (as observed in ExploreToM).\n\n2. **Adaptive model selection.** Rather than relying on fixed agent models, AutoToM adjusts its model based on a utility score that balances confidence and complexity. This enables AutoToM to adapt to different problems.\n\nWe will include this discussion in the revised Section 4."}, {"review_id": "ielQVExWbI", "reviewer": "Reviewer_Cfs5", "summary": "This 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.", "questions": "To 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?", "limitations": "AutoToM’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.", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The rebuttal provided clear and detailed clarifications that addressed my main concerns. In particular, it explained the flexibility of the model structure within principled constraints, the focused role of LLMs limited to local conditional estimation, and the use of probabilistic inference for the remainder of the pipeline", "rebuttal": "Thank you for your insightful review and for recognizing AutoToM’s novelty, state‑of‑the‑art results, and its generalization with human‑like confidence calibration. We appreciate your constructive feedback and will address your concerns as follows.\n\n> **W1 & Q2. While AutoToM claims to automate model discovery, the underlying causal structure is fixed, limiting the scope of structural generalization. Can the system be extended to jointly learn both variable structure and causal dependencies, rather than fixing the structure a priori?**\n\nAutoToM jointly learns both variable structure and causal dependencies, but only within a space of plausible causal relationships. It automates agent model discovery by identifying the necessary mental variables and timesteps while preserving causal dependencies within a family of structures grounded in standard decision-theoretic primitives (state, observation, belief, action, goal). For example, in partially observable scenarios, belief depends on observation, which in turn depends on state; in fully observable scenarios, AutoToM simplifies this by having belief depend directly on state.\n\nThese causal dependencies are constrained to align with causal structures established in prior agent modeling works (e.g., Kaelbling et al., 1998; Gmytrasiewicz and Doshi, 2005) and cognitive studies (e.g., Baker et al., 2009; Baker et al., 2017; Ullman et al., 2009). For example, actions may depend on observations or beliefs, and optionally on goals, but observations **cannot** depend on the agent’s own actions, since observations reflect what the agent perceives, regardless of its own actions. Rather than introducing implausible causal dependencies, we selectively include and dynamically adjust dependencies within the model space. While extending beyond classical MDP/POMDP/I‑POMDP formulations, AutoToM's model space supports 30 configurations per timestep and allows for arbitrary levels of recursive reasoning, as discussed in Appendix A.5.\n\nEmpirically, AutoToM’s model structure generalizes across domains and modalities: we evaluated it on five ToM benchmarks, two cognitive studies, and an embodied assistance task, demonstrating strong overall results and stable behavior across diverse contexts, number of agents, modalities, and recursion level.\n\nTo further enhance generalization, domain-specific hypothesis libraries or other inductive biases for proposing variables can be incorporated, without changing the core algorithm. For instance, to enable affective reasoning—specifically, attitude inference as in the OpenToM benchmark—we extended the set of latent variables to include preference and attitude, and constructed new causal dependencies accordingly. This modification allowed AutoToM to perform affective inference effectively, achieving a significantly higher Macro-F1 score than GPT-4o (0.56 vs. 0.48).\n\n> **W2. The framework does not learn the variables or their dependencies; instead, it selects and scores hypotheses using LLM outputs without probabilistic grounding.**\n\nThe hypotheses are scored and selected using probabilistic inference rather than by LLM outputs alone. This selection process is illustrated in the key steps in Figure 3: Step 1 is formulated by our model and estimated with the help of an LLM, while Steps 2 and 3 do not involve the LLM.\n\n**Step 1:** Estimating local conditionals: $P(b_1^t  | b_1^{t-1}, o_2^t)=0.4$ \n\nIn this step, the local conditional is estimated by an LLM. Given the clear dependencies, it's significantly more accurate for an LLM to estimate this than to tackle the entire problem directly.\n\n**Step 2:** Calculating posterior probabilities: $P(v_i^t| X^{t_{s}:t}) \\propto \\sum_{V_{-i}^{t_s:t}} P(v_i^t, V_{-i}^{t_{s}:t}, X^{t_{s}:t})$ \n\nThis step relies purely on probabilistic inference to explicitly compute the final probability distribution, without using the LLM.\n\n**Step 3:** Model adjustment based on utility: $U(M, q) = R(M, q) - C(M)$ \n\nThis step involves model adjustment based on the computed utility, which accounts for both the model’s confidence and cost. The LLM is not used in this step.\n\n> **W3. Some of the evaluation gains may stem from prompt-level tuning or LLM scoring behavior, rather than principled modeling.**\n\nThe success of AutoToM mainly comes from constructing agent models (Bayesian networks) and conducting Bayesian inference based on these constructed agent models. It substantially outperforms all previous prompting-based methods, such as SymbolicToM and SimToM, particularly in long-context and complex scenarios. These gains result from a principled agent modeling approach, decomposing the problem and clearly modeling mental dependencies.\n\nConcretely, AutoToM extracts observable variables, constructs explicit mental models, and performs probabilistic inference using Bayesian inverse planning, grounded in cognitive studies. The agent models it constructs align with those commonly used by cognitive scientists. AutoToM iteratively refines these models to maximize confidence while minimizing complexity. The model adjustment is guided by a formal objective defined by our model utility (negative entropy of posterior distribution as reward and model complexity as cost), rather than relying on LLM outputs alone.\n\n> **Q1. To what extent does AutoToM's performance depend on the specific LLM used? Could smaller models offer similar benefits with prompt engineering?**\n\nTo test AutoToM’s performance sensitivity to LLM backends, we conducted additional experiments using alternative models. Note that we used the **same prompt** for each backend LLM. Specifically, we replace the GPT-4o backend with Qwen3-235B (open-sourced), DeepSeek-V3 (open-sourced), and Gemini-2.5-flash (thinking disabled) on the most challenging MMToM-QA benchmark. Notably, AutoToM with any LLM as the backend outperforms the corresponding LLM performance by a large margin (Rebuttal Table 1). Crucially, we achieve this without extra prompt engineering.\n\n\nRebuttal Table 1: Model performance comparison on MMToM-QA. LLM indicates the model itself; AutoToM represents our method with corresponding model as the backend.\n| | LLM | AutoToM |\n| :--- | :--- | :--- |\n| GPT-4o | 44.0 | 83.0 |\n| Qwen3-235b-a22b-2507 | 45.0 | 67.5 |\n| DeepSeek-chat-v3-0324 | 34.8 | 71.1 |\n| Gemini-2.5-Flash (thinking disabled) | 44.7 | 71.7 |"}, {"review_id": "QXK4aWpVTJ", "reviewer": "Reviewer_9KJy", "summary": "This 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.", "questions": "Cost 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.", "limitations": "NA", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thanks for the rebuttal. It addresses my questions. I raised my ratings accordingly.", "rebuttal": "Thank you for your insightful review and for recognizing that AutoToM removes manual model specification with automated model discovery, generalizes to open‑domain and higher‑order reasoning, and is backed by thorough evaluations and case studies showing practical utility and interpretability. We appreciate your constructive feedback and will address your concerns as follows.\n\n> **W1. 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.**\n\nWhile AutoToM outperforms large reasoning models in terms of performance, it is also faster or comparable in real-world scenarios. For embodied tasks, AutoToM takes 30 seconds per timestep, which is only slightly slower than its backend LLM, GPT-4o (20 seconds per timestep). o3-mini, by contrast, requires 100 seconds per timestep, making it impractical for embodied decision-making. Additionally, we analyzed the inference speed and computational cost of AutoToM and reasoning models on the most computationally demanding ToM benchmark, MMToM-QA (due to long context). The comparisons, as shown in Rebuttal Table 1, show that AutoToM requires fewer or comparable tokens and inference time. \n\nRebuttal Table 1: Token cost and inference time comparison on MMToM-QA.\n| | Average #Tokens per Question (thousands) | Average Inference Time (seconds) |\n| :--- | :--- | :--- |\n| AutoToM | 8.0 | 8.5 |\n| o3-mini-high | 10.9 | 21.6 |\n| Gemini 2.0 Flash Thinking | 8.8 | 6.1 |\n\n> **W2. A more detailed explanation or visualization of how the system alternates between variable and timestep adjustments would improve transparency and reproducibility.**\n\nThank you for the constructive feedback. In the paper, *Figure 1* overviews the AutoToM procedure; *Figure 3* illustrates example hypotheses and calculations for Bayesian inverse planning; and *Figure 5* shows a real example of AutoToM’s model adjustment and inference in a BigToM false-belief scenario. *Appendix C.3* presents two real examples demonstrating variable adjustment (Example 1) and timestep adjustment (Example 2) in AutoToM.\n\nFollowing your suggestion, we will add an additional example figure that clearly presents every element (each hypothesis, each calculation) and every step of the process (initial model → inference results → variable adjustment → inference results → timestep adjustment → …), with easily digestible annotations.\n\n> **Q1. Cost Function Clarification**\n\nThanks for this question. No, our complexity cost is C(M)=α∣M∣, where ∣M∣ counts only the number of latent mental variable types; it does not include timesteps. We start from the last timestep and only add earlier steps if the utility remains low, so parsimony is enforced by this conservative timestep expansion, and with the cost term.\n\n> **Q2. Hi-ToM Performance**\n\n**Challenges with higher-order tasks.** For higher-order tasks, AutoToM samples one possible state based on b(s) at level *l* to approximate the state at level *l* - 1 as imagined by the agent at level *l*. This process is applied recursively down to level 0, where a standard automated BIP is performed.   However, **as recursion deepens, errors may accumulate**: inaccuracies in belief estimation at any level or state can propagate downward, potentially affecting reasoning at level 0. To further improve the accuracy of recursive reasoning, we need to reduce errors introduced during BIP when sampling beliefs—specifically, by enhancing the accuracy of non-recursive belief inferences.\n\nOther errors occur when AutoToM fails to recognize the relevance of certain mental variables, resulting in an insufficient model. This may be improved with better LLM backends, and we will also explore broader improvements at the framework level, such as learning-based approximations to replace the computationally expensive nested BIPs.\n\n**Deciding on higher-order inference.** The initial model proposal assesses the required level of recursive reasoning for higher-order ToM inference. This level is more fixed and easier to determine early on compared to the necessary variables and timesteps, which we adjust dynamically based on inference uncertainty.\n\n> **Q3. LLM Backend Sensitivity**\n\nTo test AutoToM’s performance sensitivity to LLM backends, we conducted additional experiments using alternative models. Note that we used the **same prompt** for each backend LLM. Specifically, we replace the GPT-4o backend with Qwen3-235B (open-sourced), DeepSeek-V3 (open-sourced), and Gemini-2.5-flash (thinking disabled) on the most challenging MMToM-QA benchmark. Notably, AutoToM with any LLM as the backend outperforms the corresponding LLM performance by a large margin (Rebuttal Table 2). Crucially, we achieve this without extra prompt engineering.\n\nRebuttal Table 2: Model performance comparison on MMToM-QA. LLM indicates the model itself; AutoToM represents our method with corresponding model as the backend.\n| | LLM | AutoToM |\n| :--- | :--- | :--- |\n| GPT-4o | 44.0 | 83.0 |\n| Qwen3-235b-a22b-2507 | 45.0 | 67.5 |\n| DeepSeek-chat-v3-0324 | 34.8 | 71.1 |\n| Gemini-2.5-Flash (thinking disabled) | 44.7 | 71.7 |"}, {"review_id": "1JksFiPLHH", "reviewer": "Reviewer_B8dt", "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.", "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?\n- How does the performance of AutoToM vary when using different base LLMs?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n- Each aspect of AutoToM is well-motivated, explained clearly, and well-evaluated with the ablation studies.\n- The quality and significance of this contribution is well supported by the breadth of benchmarks and baselines that are considered.\n\nWeaknesses:\n- 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.", "was_revised": true, "final_justification": "The authors have clearly addressed the my questions/concerns with further results.", "rebuttal": "Thank you for your insightful review and for recognizing AutoToM’s clear motivation and explanations, strong ablation studies, and the wide set of benchmarks and baselines that support its quality and significance. We appreciate your constructive feedback and will address your concerns as follows.\n\n> **W1. 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.**\n\nDue to budget constraints, we were unable to run every benchmark multiple times. However, for the most challenging benchmark in terms of baseline performance, MMToM-QA, we evaluated AutoToM using three different random seeds. The average performance had a mean accuracy of 82.56% with a standard error of 0.45%. This is consistent with the accuracy of 83.00% reported in Table 1 and indicates relatively stable performance across different runs. Similarly, we also ran o3-mini-high with three different random seeds. It had a mean accuracy of 65.94% with a standard error of 0.59%. This result shows that the evaluation is quite stable and our findings still hold after multiple runs.\n\n> **Q1. 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?**\n\nThis is indeed an insightful question.\n\n1. We do not begin with an overly simple model—we start with an initial model proposal. For example, when addressing a question about belief, we start with models that incorporate beliefs. Utterances and actions are also explicitly considered during the initial model proposal. In practice, this initial model is usually rich enough and requires only minor adjustments over 1 or 2 iterations.\n\n2. For AutoToM to be confident, the model needs to produce high action/utterance likelihood, P(a | relevant variables) or P(u | relevant variables), to fully account for the observed behavior. It’s difficult for the model to produce high action/utterance likelihood if crucial variables are missing. E.g., if the agent has only partial observations but belief is missing in the agent model, the model would not explain its behavior caused by perishable observability. \n\n3. The only exception we observed in the experiments is that there were very few instances (2 out of 600 questions) in MMToM-QA, where a simpler model (MDP) is used when a more complex model (POMDP) would be more appropriate. This is caused by errors in the GPT-4o’s estimation of action likelihood. As a potential solution, AutoToM supports improving the model using human feedback (see Appendix B). We can also use MCMC for model adjustment to better escape local optima. \n\n> **Q2. How does the performance of AutoToM vary when using different base LLMs?**\n\nTo test AutoToM’s performance sensitivity to LLM backends, we conducted additional experiments using alternative models. Note that we used the **same prompt** for each backend LLM. Specifically, we replace the GPT-4o backend with Qwen3-235B (open-sourced), DeepSeek-V3 (open-sourced), and Gemini-2.5-flash (thinking disabled) on the most challenging MMToM-QA benchmark. Notably, AutoToM with any LLM as the backend outperforms the corresponding LLM performance by a large margin (Rebuttal Table 1). Crucially, we achieve this without extra prompt engineering.\n\nRebuttal Table 1: Model performance comparison on MMToM-QA. LLM indicates the model itself; AutoToM represents our method with corresponding model as the backend.\n| | LLM | AutoToM |\n| :--- | :--- | :--- |\n| GPT-4o | 44.0 | 83.0 |\n| Qwen3-235b-a22b-2507 | 45.0 | 67.5 |\n| DeepSeek-chat-v3-0324 | 34.8 | 71.1 |\n| Gemini-2.5-Flash (thinking disabled) | 44.7 | 71.7 |"}]

Author rebuttal:

Meta-review: The paper introduces AutoToM, which automates agent-model construction and applies Bayesian inverse planning with an LLM backend. Reviewers generally agree that the problem studied is important, the approach is novel, and the model-based methods, and the empirical coverage is strong.  
There were some concerns regarding the clarity and scope of the claims, and questions about additional analysis and results. 
The rebuttal addressed these points with added FANToM and OpenToM results, backend-sensitivity studies showing consistent gains, token and time cost comparisons, multi-run results on MMToM-QA, and clearer distinctions from thought-tracing. All reviewers remain highly positive about the submission.
Decision: Accept (spotlight)