SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs
Authors: Yuling Gu
Oyvind Tafjord
Hyunwoo Kim
Jared Moore
Ronan Le Bras
Peter Clark
Yejin Choi
Venue: ICLR 2026 Poster (2026)
Keywords: Theory of Mind
social reasoning
LLM benchmark
mental state
behavior
judgment
false belief
Primary area: datasets and benchmarks
TL;DR: SimpleToM is a novel Theory-of-Mind dataset which reveals the intriguing insight that frontier LLMs perform well on explicit ToM (predicting mental state), but poorly on applied ToM (predicting behavior and judgment).

Abstract: Large language models (LLMs) are increasingly tested for a "Theory of Mind" (ToM) — the ability to attribute mental states to oneself and others. Yet most evaluations stop at explicit belief attribution in classical toy stories or stylized tasks, leaving open the questions of whether LLMs can implicitly apply such knowledge to predict human behavior, or to judge an observed behavior, in diverse scenarios. We introduce SimpleToM, a benchmark that advances ToM evaluation along two novel axes. First, it probes multiple levels of ToM reasoning, from mental state inference (explicit ToM) to behavior prediction and judgment (applied ToM). Second, it situates these tasks in diverse, everyday scenarios — such as supermarkets, hospitals, schools, and offices — where information asymmetries naturally arise (e.g., hidden defects in grocery store items, incomplete information in provider–patient interactions, or restricted access to locked devices). SimpleToM contains concise stories (e.g., "The can of Pringles has moldy chips in it. Mary picks up the can in the supermarket and walks to the cashier."), each with three questions that test different degrees of ToM reasoning, asking models to predict: (a) mental states ("Is Mary aware of the mold?"), (b) behaviors ("Will Mary pay for the chips or report the mold?"), and (c) judgments ("Mary paid for the chips. Was that reasonable?"). Experiments reveal a striking gap: state-of-the-art models often reliably infer mental state (a), but fail at applying knowledge about the mental state for secondary predictions, with performance dropping sharply for behavior prediction (b) and further for behavior judgment (c). This exposes a critical fragility in LLMs’ social reasoning in terms of what they know (explicit ToM) versus how well they can implicitly apply that knowledge for predictions (applied ToM). By uniting assessment of different levels of ToM reasoning with diverse, everyday scenarios, SimpleToM opens new opportunities for rigorously evaluating and diagnosing ToM abilities in LLMs, and reveals surprising, new insights about current model capabilities, guiding efforts toward future generations of models capable of robust social understanding.

Published as a conference paper at ICLR 2026
SIMPLETOM: EXPOSING THE GAP BETWEEN EXPLICIT TOM INFERENCE AND IMPLICIT TOM APPLICATION IN LLMS
Yuling Gu
Oyvind Tafjord* *
Hyunwoo Kim
Jared Moore
Ronan Le Bras
Peter Clark
Yejin Choi
Allen Institute for AI
NVIDIA Stanford University
ABSTRACT
Large language models (LLMs) are increasingly tested for a "Theory of Mind" (ToM) - the ability to attribute mental states to oneself and others. Yet most eval- uations stop at explicit belief attribution in classical toy stories stylized tasks, leaving open the questions of whether L.MSs can implicitly apply such knowledge to predict human behavior, or judge an observed behavior. in diverse scenar- ios. We introduce SimpleTooM, benchmark that advances ToM evaluation along two novel axes. First, it probes multiple levels of ToM reasoning, from men- tal state inference (explicit ToM) to behavior prediction and judgment (applied ToM). Second, it situates these tasks in diverse, everyday scenarios - such as supermarkets, hospitals, schools, and offices where information asymmetries naturally arise (e.g., hidden defects in grocery store items, incomplete information in provider-pate patient interactions, or restricted access to locked devices). Simple- ToM contains concise stories (c.g.. "The can of Pringles has moldy chips in it. Mary picks up the can in the supermarket and walks to the cashier."), each with three questions that test different degrees of ToM reasoning, asking models to pre- dict: (a) mental states ("Is Mary aware of the mold?"), (b) behaviors ("Will Mary pay for the chips or report the mold?"), and (c) judgments ("Mary paid for the chips. Was that reasonable?"). Experiments reveal a striking gap: ofte-of-thhearr models often reliably infer mental state (a), but fail at applying knowledge about the mental state for secondary predictions, with performance dropping sharply for behavior prediction (b) and further for behavior judgment (c). This exposes a critical fragility L.MSs' social reasoning in terms of what they know (explicit ToM) versus how well they implicitly apply that knowledge for predictions (applied ToM). By uniting assessment of different levels of ToM reasoning with diverse, everyday scenarios, Simple'ToM opens new opportunities for rigorously evaluating and diagnosing ToM abilities in LLMs, and reveals surprising, new in- sights about current model capabilities, guiding efforts toward future generations of models capable of robust social understanding.
INTRODUCTION
As LLMs are now regularly used as conversational agents, it is critical that they can reliably reason about other people's beliefs. Without this, an LLM may provide disastrous responses, for example by failing to recognize emotional distress (Obradovich et al. 2024). treating sarcastic comment as literal truth (Zhang et al. 2024), providing direct but inappropriate advice in sensitive situa- tions (Hodson & Williamson 2023 Kim et al. 2025), or blindly helping a user with malevolent intent (Shang et 2024). Performing such social reasoning is complex, involving attributing men- tal states to oneself and others, ability widely known as Theory of Mind (ToM) (Premack & Woodruff 1978). Specifically, ToM requires an LLM to reason over multiple, possibly conflicting views of the world simultancously, making it fundamentally more challenging than typical multi- step reasoning over a single worldview (e.g., factual, ontological, or arithmetic knowledge). This
"Current contact information: yul puling. ionnngggg werg-gu@nyueeee

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Published as a conference paper at ICLR 2026
Mental States
Expliit TOM
The can Pringles has moldy chips in it. Mary up the supermarket and walks to the casnier.
prediction
Judgment behavior
Applied TOM
Seed scenarios
Diverse settings
Example situation
Inferences
(a) Mental State prediction
Percepts
States
Mary likely
Yes
Explict TOM
"The can of Pringles has moldy chips
g. Pringle opecqus, cannot
No
checking
Treeefore, ldtry lilally siows not know rratt thercen picked has moldy chips.
Inference
Question
Answer options
(b) Behavior prediction
Percepts
Mental States
Pay for chips
#ig Jif unilkall Mary know
Behavior prediction
plicked moldy
What will Mary likely do next?
interactioo with the ingies So she will liely just proceed to pay for
Applied ToM
Report moldy chips
the chips
Inference
Question
Answer options
Percepts
(c) Judgment of behavior
States
rensonable that Many noxt proceed for the chipea mas shis licly cocon' know about moldy chips.
Beiavior prediction
Next, Mary "pay the chips" and not "report the moldy chips". The behavior described as:
Ressonable
Judgment of behavior
Applied ToM
best
Not reasonable
reasonabie she about kI
Inference
Question
Answer options
Figure I: To allow for nuanced analysis models' neural ToM abilities, SimpleToM covers both explicit ToM (a) and applied ToM (b, c) question types. SimpleToM measures the ability of L.MSs to (a) infer the character's mental state, specifically information awareness, (b) anticipate their likely next behavior in the given situation, and (c) make appropriate judgment of the character's 's behavior that correctly accounts for their mental state.
requires specific studies to ascertain how well LLMs perform ToM, distinguishing between rea- soning on the basis of the state of the world and reasoning on the basis of someone's 's beliefs about the state of the world (Doherty 2008), and such studies are becoming increasingly needed LLM adoption in society grows.
However, while ToM in humans has been extensively studied in psychology (c.g., Doherty 2008 Baron-Cohen et al. 1985; Perner et al.] 1987), studies of ToM reasoning in LLMs to date have been limited, largely relying on the classical Sally-Anne task templated variants of it (Le al. 2019 Nematzadeh et al,, 2018 Wu et al. 2023] Xu et al. 2024). While informative, these studies have several shortcomings: (i) limited diversity how information asymmetry arises (see related work in Section 6 for examples across existing datasets), (ii) explicit use of percept and mentalizing verbs like "sees" and "thinks" which serve as trigger words for models to realize that these are important aspects, removing the need for implicit commonsense inferences about relevant percepts or beliefs, and (iii) limited exploration of applied ToM, such as the judgment of behavior which requires implicit reasoning about mental state.
Our goal is to go beyond assessing just mental state inference ("What does believe?"), to also as- sess how well models can predict others' behavior based on that understanding ("What will X do?"), and make judgments of appropriateness of that behavior ("Did X act appropriately?") as well as to unite assessment of these different levels of ToM reasoning with diverse, everyday scenarios e.g.
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Concise stories with implicit mental states [Sentence with key information] (Story sentence about Person X]
HOSKET
Annotations to ensure high quality instances:
Qualified careful, rigorous annotators
Key information is not something Person would expect
Key information is not something that Person X would
notice/know through the interaction described
V [action_unaward) is likely action wNLY when Person NOT aware (and not when aware)
SimpleToM
V (action_aawa) aware] is likely action ONLY Person XIS aware of key information (and when unaware)
High quality, diverse dataset
We leverage the generative strength of language models obtain concise stories with varied entities and diverse situations, suitable for testing different levels of ToM reasoning. The generated stories (and answer options) were then rigorously filtered by careful human annotators who passed a strict qualification test. The result is a high-quality and diverse dataset, SimpleToM.
Figure 2:
supermarkets, schools, offices where information asymmetries naturally arise. Our approach is to construct and evaluate using a new dataset, SimpleToM. Each story in SimpleTTMM paired with three types of questions targeting these abilities (Figure D with total of 1 1147 stories and 3441 questions in daily life settings. Our results are surprising, revealing significant gap in model per- formance between explicit and applied ToM questions (Hutchins al. 2016 Lee et al. 2024), even though the underlying scenarios are identical. We find that frontier models perform well on explicit ToM questions (directly querying for information about "mental state". i.e., information awareness). However, this success does not extend to applied ToM ("behavior" and "judgment" questions), even strong models like GPT-5 and ol -previe. Performance can also vary wildly across scenarios, highlighting the importance of SimpleToM covering diverse scenarios beyond those in classical ToM tests, for rigorously evaluating and diagnosing ToM abilitics in LLMs. Overall, the results show that frontier models still lack the ability to independently and reliably apply ToM skills in tasks such as anticipating others' behavior and making judgments, calling for caution when using them in social applications (see discussion of example applications in Appendix
Our contributions and findings are as follows:
We introduce SimpleToM, a dataset for testing the core abilities of LLMs in both explicit and applied ToM.
We find that current frontier models have decoupled capabilities between predicting some- one's 's information awareness a situation (explicit ToM, which they excel at), and utilizing it to predict and judge someone's behavior (applied ToM, which they perform poorly at). We show that LLMs' ToM performance can vary wildly across scenarios, highlighting the importance of using diverse scenarios for diagnosing ToM abilities in LLMs.
We make our SimpleTo.M datase and " publicly available. This will allow researchers to build on top of our work in studying the neural ToM capabilities of LLMs in general, as well as further exploit the different levels of ToM reasoning and diversity of situations covered by Simple'ToM,
SIMPLETOM DESIGN
We design the stories in SimpleTOM contain diverse types of information asymmetry, using concise format and associated with specific question types testing explicit and applied ToM.
2.1 DIFFERENT TYPES OF INFORMATION ASYMMETRY
To expand beyond the classical false belief, or Sally-Anne task, we seed the creation of SimpleToM with ten diverse scenarios where information asymmetry occurs naturally in everyday settings such as in supermarkets, schools, and offices (Table This is inspired by social psychology literature to
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The ten broad scenarios used seed the generation of stories in SimpleToM. Each scenario describes a type of information asymmetry that occurs naturally in everyday setting.
Table 1:
Sceuario
Reason information asy
Food item in grocery store
Food items bought grecery stores cannot be closely examined for their quality before checking
Provider info healthcare True property pretentious labels Behind the sccne service industry Inside reuse labeled containers Unobserved unothical actions
Efficacy of healthcare products cannot closely examined or verified before purchase
Subtee properties of products connot closely examined verified
Questionable bchind-the-sccues practices in the service industry are not observed by customers inside laheled (opaque) containers cannot he ohserved before opening container Unethical actions not observed are not known What inside (opaque) containers for personal belongings cannot observed before opening the container
Inside containers for personal belongings
Seller info in second hand market
Hidden flaws second- hand items bought camnot be observed before the purchase
Hidden body part feature
Body features hidden under elothing cannot observed
Locked devices accounts
Details in locked devices or accounts cannot be observed by others
cover asymmetrics like manipulation, deception, secrecy, lying, and mislcading behavior (Doherty) 2008) seen in real-world contexts like sales of "lemon" products, where items with hidden flaws are purchased due to a lack of information (Akerlof) 1978). These are under-examined in existing ToM tests. We further describe the scenarios with examples in Table 8 (Appendix L.1
2.2 SIMPLE STORY FORMAT WITHOUT EXPLICIT PERCEPTS OR MENTAL STATES
The SiMpleToM example story from Figure 1 reads: The can of Pringles has moldy chips in it. Mary picks up the can in the supermarket and walks to the cashier. Each story has exactly two sentences, where the first sentence introduces a key information about something (Object/Person/Actiis while the second sentence presents the main subject of the story (Person X) doing something with Object/Person/Actioon Z while being unaware of the key information. The list of story elements are:
Z),
Key Information:
involves something unexpected which Person X is unlikely to know or perceive, e.g., The can of Pringles has moldy chips in it.
Object/Person/Actior Z: the subject of the key information (e.g., can of Pringles) Person X: person unaware of the key information (e.g.. Mary)
Person Y (optional): any other character(s) needed for the story
We impose the constraint that Person X's unawareness of the key information should be implicit (e.g., avoid explicit use of perception or mentalizing words such as "see", "notice" or "believe") This design encourages models read between the lines and make commonsense inferences over the given situations and infer characters' mental states in more realistic manner, bringing us closer realistic daily life use cases of ToM. (E.g.. you cannot see through a Pringles can; you would not know about a cheating event if you were not present.)
To support formulating the behavior prediction question (Section 2.3) we also generate options for what might happen next:
Unaware behavior: A likely next action by Person X given that they are unaware of the key information.
Aware behavior: A likely next action by Person X if they were somehow aware of the key information after all (a counterfactual).
2.3 QUESTIONS TESTING EXPLICIT AND APPLIED TOM
We use three types of questions (Figure T to probe model's grasp of each story, covering both explicit theory of mind (conceptual knowledge about others' mental states; i.e., via (a) mental state questions about information awareness) and applied theory of mind (the ability use theory of mind in downstream tasks i.c., via (b) behavior and (c) judgment prediction questions) (Hutchins) al. 2016 Lee al. 2024).
Mental state (MS) question about information awareness: We test ability of models to infer men- tal states, specifically information awareness, through simple yes/no question (Is <Person X> likely to be aware that " "<key informat ion>" ?)). To infer whether a character is aware of something in SimpleToM stories, model has to make implicit commonsense inferences
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about what the character perceive know in the given situation (including commonsense rea- soning about physical objects, space, intent, goals of others, and so on).
This question asks which of two possible actions the main subject (Person X) is likely to perform next. For instance, beyond answering that a person shopping for chips in the supermarket is unlikely know that "the can of Pringles has moldy chips in it", model that successfully applies this inference for behavior prediction should also infer that a person who picked up such can in the supermarket would likely "pay for the chips" rather than "report the moldy chips." To answer these questions correctly. models need to implicitly reason over the situation infer the mental state of character(s), and realize how the character's lack of awareness of the key information would impact their likely next action.
Behavior prediction question:
Judgment question: The judgment question specifies that the "correct" action was taken (rather than the incorrect one) and asks if this was reasonable choice. As the inference graph in Figure [ illustrates, the judgment question goes beyond behavior prediction as it requires two levels of im- plicit reasoning, first implicitly predicting the behavior of Person X, which itself relies implicitly understanding their mental state. People's ' mental states are an important factor to consider in mak- ing appropriate judgments of their behavior (Jara-Ettineer et al. 2016 Schein & Gray) 2018). For instance, buying a can of Pringles that has moldy chips in it is not a reasonable action if the person knows about the moldy chips. However, it is perfectly reasonable (and expected) behavior if this piece of key information is not part of the person's 's mental state.
3 SIMPLETOM CREATION
3.1 GENERATING DIVERSE STORIES
Specifically, the construction of SimpleToM consists of the following steps:
Manually create one example seed story for each scenario. Step 2: For each scenario, using the seed story as example, prompt the LLM to suggest 10 diverse sets of entities compatible with an information asymmetry. (See prompt Appendix [.5)
Step 1:
Step 3: For each set of suggested entities, along with the seed story, prompt the LLM to write three new stories at different levels of "severity." With each story, also generate likely next "unaware" and "aware" behaviors (see Section 2.2) Appendix [.4 provides further details.
We went through two rounds of this process. First, we used GPT-4 and Claude-3-Opus to generate total of 1200 stories I After annotating and filtering this initial set (Section 3.2). we picked new set of top-scoring seed stories and sourced additional sets of entities from each of GPT- 4o and Claude-3.5-Sommed of entities, for total of 2400 more stories. By using several generator models, varied entities and different seed stories, the resulting stories in SimpleToM have wide range of information asymmetries instantiated in different everyday situations, effectively broadening neural ToM tests beyond traditional settings (Section (6). These contexts also allow for nuanced and implicit traits (e.g., buyers would avoid products with defects if they know about them).
We used these two newer models to generate stories for all 40 sets
3.2 STRICT QUALITY CONTROL ON STORIES THAT GOES INTO SIMPLETOM
We gather human annotations on each story (and unaware/aware next actions). We asked annotators four questions for each story, summarized in Figure 2 This process verifies that the key information in each story is something that Person X has a false belief about. We also carefully verify that the next likely "unaware action" is appropriate if and only if Person X is unaware of the key information. We similarly verify the "aware action" for the counterfactual situation where Person X is somehow aware of the key information. Appendix G provides further details about the crowdsourcing proce- dure, with instructions, examples and question templates.
Our annotators passed rigorous qualification test (Appendix G and met other high- high-staddard requirements (Appendix G.3 Only stories for which all crowdworkers (3) judged all aspects to be
'See 4 for exact models used.
*10 scenarios 2 models * 10 entities per model severities * 2 models to generate stories
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valid were included in SimpleToM This results in 1147 stories (out of the original 3600) in the final SimpleToM dataset. Table (Appendix I.2 provides statistics and further details for SimpleToM.
4
EXPERIMENTAL SETUP
We evaluate SimpleTToM on 21 LLMs from different sources and with different levels of capabilities,
ranging from smaller open-weights models close-sourced frontier models: Llama-3.1 1--BB, Llama-
3.1-405B, Llama-3.2-11 3.2-1B, Llama-3.2-3B (Dubey et al. 2024). Qwen-2.5-7B, Qwen-2.5-14B (Qwen
al. 2025), Ministral-8B (Mistral (024), Claude-3-Haike Claude-3-Opus (Anthropic 2024b),
Claude-3.5-Sonne (Anthropic 2024a). DeepSeek-RI ( (DeepSeek-Al et al. 2025), GPT-3.5, GPT-4, GPT-4.5-pree prrvview GPT-5, GPT-4o, GPT-4o-mini, ol-mini, 03-mini, ol and ol op-eveew (OpenAl,
2023 (024) (refer to Appendix E Table g for more details). Where possible, we use the most deterministic setting with a generation temperature of 0
We use SimpleToM to investigate the following research questions:
1. How well can models (a) infer characters' mental states, (b) anticipate characters' behavior and (c) make appropriate judgments, requiring the of ToM inferences?
2. How does the ToM performance of models differ across scenarios?
3. How much can we close the gap between models' performance on explicit and applied ToM using inference-ting interventions?
5 RESULTS AND ANALYSIS
5.1 FRONTIER LLMS CAN INFER MENTAL STATES, BUT STRUGGLE TO USE IT
The overall evaluation results on SimpleToM for the 21 models are summarized in Table spanning the different question types (as detailed in Section 2.3 We analyze models' performance for each type of question below. Note that these are binary questions where random performance 50%. For each score also report the 95% confidence interval
Mental state (MS) question about information awareness: Our results (Table 2] "mental state" column) show that reasoning over implicit information in given situations infer mental states still challenging for models like GPT-3.5 (36.5% accuracy), while newer and/or bigger models ol-mini perform reasonably well (around 88%). In fact, all recent frontier models are proficient at inferring characters' awareness in our dataset e.g., models Claude-3- 3-0us, GPT-4, GPT-5, Claude-3.5-Sonee
like Claude-3-Haiku, Llama-3.1
ol op-preveew, ol, DeepSeek-R2 1 all achieved accuracies of more than 95%. This result also confirms the quality of our dataset, that characters' mental states in SimpleToM stories are implicit but reasonably easy to infer, as designed.
like GPT-4o, Alamm--3.140555
Behavior prediction: On behavior prediction questions (Table "behavior" column), smaller and older models perform extremely poorly (with GPT-3.5 achieving only 7.6% accuracy, and mod- els like Claude-3-Haiki and Llama-3.1 scoring less than 40%). Even for the larger models, like 1.ama-3.11405D Claude-3.5-
GPT-4, GPT-5 and GPT-4o, performance on the behav- ior prediction task is much worse than on the mental state task with at least a 30 % performance drop This large inconsistency suggests that while frontier LLMs may have the right conceptual knowledee nhowledge/information about others' mental states when directly asked, they struggle to apply this knowledge in everyday scenarios to make downstream predictions about characters' behavior. Only the ol -rrrevieww model, with its built-in inference time reasoning tokens manages decent score of more than 80% on this question type (84.1%).
$ See more details Appendix G.4
Foor test-time reasoning models (ol, o3, GPT-5 and DeepSeck-RI models) use the recommended tem- perature settings.
Ussig Wald interval for binomial distribution based on 114 samples in each category.
SSee Appendix 10 for how humans demonstrate greater consistency across the question types.
'The ol reasoning tokens make these models more like the chain-of thought prompted versions of the non- reasoning models, although without any custom prompt. See Appendix [[. for discussion of the number of output tokens used by the ol and the other models when using test-time reasoning tokens.

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Table 2: Evaluation results for SimpleToM, on the different question types. State-of-the-ar models are generally proficient in explicit ToM questions (directly querying about "mental state", i.e. in- formation awareness) but this success does not transfer to applied ToM ("behavior" and "judgment" questions). Each score is annotated with 95% confidence interval.
model
mental state
behavior (Applied ToM)
judgment (Applied ToM)
average
(Explicit ToM)
GPT-3.5
36.5
7.6
29.1
24.4
Qwen-2.5-7B
76.4=2.5
25.3+2.
23.4
41.7
Claude-3-Haiku
87.2
23.6+2.66
16.7+22
42.5+1.7
Ministral-8B
62.5=2.a
26,9+2.6
50.0*2.9
46,5+1.7
Qwen-2.5-14B
93.5=1.4
35.3+2.8
15,5+2.1
48.1+1.7
Llama-3.2-3B
123.8
32, 32.912.7
49.812.
48,241.7
GPT-4o-mini
93,0=1-5
40,622.
22.3
52,0
GPT-4o
95. 95,621:2
49.542.9
15.34
53,5
Llama-3.2-1B
91.8 11.8
22. +
49.3+2.9
54,4+117
Llama-3.1-4051
97.8
58.2+2.9
10.0*1.
55,4+1.7
Claude-3-Opus
98.3+0.7
64.4+2.6
9.6+1.7
57,4+1.7
GPT-4
96.6=1.0
63.0 63.0#2..6
19,542.a
59,741.6
Llama-3.1-80
88.1=1.9
38.5
54.6#2.0
60.4+1.6
S.aude-3,5-Somme
97.9=0.8
67. 67.0+2.7
24.9
63.341.6
GPPT-4.5-rreview
97.0#1.0
67.8+22.7
26.7+2.
63.8
Reasoning models (with internal chain thought)
ol-mini
87.8=1.
44.8#2.
27.0
53.2
o1
98.6=0.7
58.8+2.8
32.5+2.7
63.3+1.6
ol (high reasoning effort)
98.7+0.7
60.2+2.6
33.3+2.7
64.1+1.6
03-mini
85.4*2.0
66.8+2.7
41.3+2.8
64.5+1.8
GPT-5
98,520.7
64, 64.442..
40.022.
67.741.4
DeepSeck-R
47.3
73,842.0
65.842.
79.041.4
ol olepreveww
95.6=1.2
84.1 1421
59,5
79.741.3
Llama-3.1
GPT-40
Claude-3.5-omme
01-preview
Fail at mental state
Fail at behavior prediction
Fail at judgment
All correct
Figure 3: Considering the sequence of first predicting mental state, then behavior and finally judg- ment, we record failure for the mistake that occurs first. The "fail at behavior prediction" and "fail at judgment" demonstrate inconsistent predictions by the model, since the model got the associated mental state (and behavior prediction) question(s) correct.
Judgment of behavior: Our results (Table "judgment" column) show that this additional in- ference step (making judgment of the characters' behavior beyond just behavior prediction) makes the task much more difficult for all the models. Even the newer and larger models like Llama- 3.1-405B, Claude-3.5-Sonnet and GPT-4o, which all achieved accuracies of more than 95% on inferring characters' mental state, consistently make inaccurate judgments of behavior, and their performances drop to far below random (with accuracies the range of 10% 24.9%). A subset of models (Llama-3. 1-8B, Llama-3.2-1 random chance level, while performing relatively worse on behavior prediction, presumably because these models failing to make a meaningful judgment beyond random guessing. Even the best performing model on judgment prediction, DeepSeek-RR reaching 65.8%, is significantly below its performance the other questions, illustrating a clear explicit applied ToM performance gap.
1Jama-3.2-311 Ministral-8B) manages performance near
To fully reason about the judgment question requires reasoning about the behavior pre- diction, which in turn relies understanding the mental state (information awareness) question. We can visualize this by recording the first failure on the three "mental state" - "behavior" + "judg- ment" questions. Figure shows the distribution of such failures, showing the large proportion of
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scenario
Llama-3.1 3amma-3.1440BB -laude-3.5-Somme
GPT-4o
01-preview
food item grocery store
inside reuse labeled containers
inside containers personal belongings
property pretentious labels
provider info healthcare
unobserved unethical actions
mental state
behavior prediction
judgment of behavior
Comparing performance for all three question types across select scenarios and models. Each bar represents the overall accuracy. The mental state accuracy is generally near 100%, while behavior prediction and judgment accuracies are often much lower.
Figure 4:
cases failing the behavior and judgment steps (applied ToM). For most models the "All Correct" segment, representing full understanding of the 2-sentence stories SimpleToM in terms of ToM reasoning, is very small.
5.2 NOT ALL SCENARIOS ARE MADE EQUAL
In Figure we show how model performance varies across select scenarios (Appendix O Figures 13 and 14 cover more scenarios across models). Performance can vary wildly across scenarios. E.g., the behavior prediction score is in fact high (and close to mental state scores) across models for "provider info healthcare" compared to other scenarios, potentially be due safety training of recent LLMs, making models more alert when dealing with situations that involve sensitive topics like health and drugs. Yet, the behavior prediction scores are much lower for various other scenarios. Such differences across scenarios highlight the limitation of testing ToM with just one type of ToM reasoning or scenario, emphasizing the need for diverse dataset like SimpleToM to rigorously and holistically evaluate the capabilities of LLMs.
Looking at the judgment scores, the scenarios "inside reuse labeled containers" and "inside con- tainers for personal belongings" better (but still low) than other broad scenario types for Llama- 3. 3.1-405B, Claude-3.5-Somme and GPT-4o. This could potentially be attributed to instances these categories being more similar to the original "Smarties test" where people have false belief due to the opaque nature of the container, combined with misleading label or unconventional use of the container. This further illustrates the importance of SimpleTToM covering diverse scenarios beyond those in classical ToM tests, to ensure that we are effectively testing the ToM reasoning abilities of models (rather than models' ability to match similar situations in the training data). We refer inter- ested readers to Appendix for further analysis by scenario and to Appendix Q.3 for scenario-level accuracy plots with bootstrap-bant based error bars.
5.3 TEST-TIME INTERVENTIONS ARE NOT A PANACEA
We explore different inference interventions to investigate what might help LLMs answer questions requiring applied ToM:
1. Mental state reminder (MS remind): We remind the model of its answer the mental state question by including this question (with the model's answer) in the prompt. This also puts the model on alert that the mental state information might be relevant.
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could produce results that differ given minor alterations (Ullman) (023) and would be more robust if tested on larger samples. Yet expert-crafted or naturally occurring self-contained stories that can serve as targeted tests of ToM are scarce and human story-writing is expensive, leading to the use of automatically generated datasets for studying ToM behavior in LLMSs (Jung et al. 2024: Wilf et al. [224]] (Spp et al.] 2022 Shhaiira et al., 2024; [Sclar et al.) 2023). Existing generated datasets allow studies of ToM to be carried out at scale, but templated stories often limit settings where information asymmetry arises. For example, the entire dataset might only contain stories about some object being moved (over-reliance on classical Sally-Anne task, e.g., in ToMi {Le et al. 2019), ToM-bAbl (Nematzadeh al. 2018). Hi- ToM (Wu et al. 2023), OpenToM (Xu et al.) (024)). 4). Or the dataset might focus on whether some character has witnessed a sudden external event (BigToM (Gandhi al. 2024)). These stories are often systematically generated with the explieit use of Published as a conference paper at ICLR 2026
Table 3: Evaluation with guidance via mental state reminder (MS remind), system prompt guiding (SysP) and chain-of-thought prompting (CoT). The MS column shows the mental state accuracy for comparison. In general, none of these interventions is sufficient close the gap between explicit and applied ToM across models.
model
MS
behavior prediction MS remind
judgment of behavior
intervention
SysP
Col
MS remind
SysP
CoT
GPT-4o
95.6
49.5
82.8
47.3
62.8
15.3
42.2
14.9
39.2
Llama-3.1 --05B
97.8
58.2
89.5
64.5
57.2
10.0
25.8
9.9
35.2
Claude-3.5-Sonme
97.9
67.0
96.9
68.9
77.2
24.9
84.1
27.1
39.4
2. System prompt guiding (SysP): We also explore the effect of guiding the models to remember to account for mental state inferences by modifying the system prompt. E.g., SysP which includes the phrase "consider
the mental state of all the entities involved".
Chain-of-thought prompting (CoT): The generic CoT prompt encourages models "Think step by step to arrive at an answer.". ", explicitly encouraging models to think through the situation before answering the behavior and judgment questions.
For these experiments with different inference interventions, we pick 3 strong models, each from different source. Our results in Table 3 show that interventions like SysP and CoT are weak in closing the gap between explicit (mental state inference) and applied ToM (behavior prediction and judgment). For example, even with CoT, models score below 40% on behavior judgment, leaving massive gap compared to their mental state accuracies of above 95%. In Figure [ we show an example of reasoning trace where a model gets the behavior prediction wrong even when encouraged to "think step by step" with the CoT prompt because it fails consider the mental state of the character(s) in its reasoning chain. While reasoning can help a model "think" more about the problem, but it does not mean the model would (1) think about the crucial elements for particular task and (2) apply the crucial element(s) in arriving at its answer - this is consistent with findings that chain-of-thougle explanations may contain various flaws (Gu al.) 2024), be verbose without being algorithmically grounded (Shojaec et al. 2025), or unfaithful altogether (Chen et al.) 2025).
While MS remind shows promise by boosting scores to 80% for behavior prediction across the models, even with the reminder, GPT-4o and Llama-3.1-405BB still have scores remaining low at 42.2% and 25.8% for behavior judgment. Therefore, even for these strong models (Llama-3.1 1- 405B, GPT-4o, Claude-3.5- -.-onne,, no simple test-time intervention could reliably patch for the lacking applied ToM capabilities across them. Appendix
F presents more details and results on various interventions. We also report more statistical details including 95% confidence intervals for these intervention results, as well as paired tests/bootstrapping in Appendix Q
RELATED WORK
Theory of Mind has been extensively studied in psychology in a range of scenarios (see Appendix
C). ToM reasoning, and broadly social commonsense, has also been shown to be important by the
different parts of the Al community including in conversations (Kim et al.. 2023b a), games (Zhou
20236: Liu et al. 2024 Guo al.) 2024), and even multi-modal setups (Jin et al. 2024). et al.)
with most popular ToM tests using stories to probe LLMs. Relying on stories from small test sets in
cognitive science studies to benchmark ToM abilities in LLMs (Bubeck al. 2023 Kosinski 2024)

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Story: Emily has a rare, extremely venomous snake hidden in her heavy-duty toolhox today, Emily's neighbor sees her carrying the heavy-duly toolbox towards the community garden.
Behavior question: What will Emily's neighbor likely do? (A) Keep a safe distance and alert animal control.
(B) Offer to help Emily carry the toolbox.
Answer (GPT-4o, no CoT): (A) lincorrect, should be (B))
Answer (GPT-4o w/CoT)::
To determine the correct answer, let's analyze the situation step by step: I. **Emily's s Toolbox Co The story specifies that Emily has rare, extremely venomous snake
hidden in her heavy-duty toolbox.
**Neighbor's Observation**: Emily's 's neighbor sees her carrying the heavy-duty toolbox towards the community garden.
3. **Neighbor's 's Likely Reaction**; Given that the toolbox contains a dangerous snake, the neighbor's
primary concern would likely be safety.
Considcring these points:
The neighbor, upon knowing or suspecting that the toolbox contains a venomous snake, would likely prioritize safety and the well-being of the community.
Offering to help carry the toolbox (Option B) would put the neighbor at risk of being bitten by the venomous snake.
Keeping a safe distance and alerting animal control (Option A) a more logical and safer response to ensure that the situation is handled by professionals who can safely manage the snake. Therefore, the answer is: (A)
Figure 5: Example behavior question where GPT-4o gets the wrong answer both without chain-of- thought (CoT) (overall accuracy 49.5%) and with generic CoT prompt (overall 62.8%). This shows that allowing the model to reason is not enough to nudge it to think about the mental state of the characters answering applied ToM questions.
mentalizing words convey percepts and beliefs, e.g., "unknown Amy" and "Amy thinks that" in Xu et al. (024) or "Noor sees" and "Mei does not notice" in Gandhi et al. (2024). However, the explicit use of mentalizing words also makes the stories (i) unnaturally simplistic, having removed the need for commonsense inferences about percepts or beliefs, and (ii) sometimes unrealistic, with combinations like "Cheng does not notice the power outage" when he "use[s] a projector to show a caaeeeenttrr"GGnnnnii et al. 2024). Other existing datasets leave room for further exploring applied ToM beyond action prediction (Zhou et al. [002a| Gandhi et al. (024), controlling for confounding factors like memory loads or tracking requirements (Le et al. | et al. 2024). and following
Quesque & Rossetti (2020) 's criteria (see Appendix ( for validating ToM (Chen et al. 2024). Our work extends existing datasets by following Tian et al. (2024) in combining the generative strength of LLMs and the verification ability of human annotators, and extends the existing efforts toward
robust, generalizable evaluation (Kiela et al. 2021 Srivastava et al.) 2024), avoiding known pitfalls while preserving the systematic and scalable nature of the dataset creation process.
7 CONCLUSION
SimpleToM is the first dataset of its kind testing both explicit and applied ToM using a large set of concise, simple stories, covering diverse ways in which information asymmetry may naturally arise in everyday settings. By uniting assessment of different levels of ToM reasoning with diverse, everyday scenarios, SimpleToM opens new opportunities for rigorously evaluating and diagnos- ing ToM abilities in LLMs. Our analyses reveal a jarring gap between explicit and applied ToM capabilities in current frontier LLMs fundamental but previously overlooked limitation of cur- rent LLMs. Moreover, LLMs' performance varies greatly across scenario types, underscoring the necessity of evaluating ToM in a broad range of contexts. Thus, if our goal is LLM agents capa- ble of applying ToM in complex, human-centered environments, we need to look beyond testing LLMs with psychology-inspire ToM questions, and also start testing them more rigorously on ap- plied ToM (e.g., behavioral prediction and judgment) in different situations. SimpleToM opens up range of exciting research directions for the community, including developing innovative modeling approaches to close the gap between explicit and applied ToM in AI models, studying how ToM per- formance may differ with stories that involve different levels of harmfulness and unethicality (see Appendix I.3 and injecting different persona (Appendix P
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ETHICS STATEMENT
All annotators that participated in the data collection process have been anonymized. The only personal information we collect is the worker IDs from Amazon Mechanical Turk, which we will not release. No personally identifiable information is contained in our dataset or otherwise released. We took great care pay fair wages, and were responsive feedback and questions throughout the data collection process.
This study involves the study of large-scale language models. We are careful in prompting models during the story generation stage to follow our desired content and simple story format, avoiding generations that may contain offensive statements. Like any other experiments with large-scale language models, despite the best intentions, there is risk of the examined models producing biased or offensive statements as part free-form generation (e.g., CoT reasoning). We release our data for research purposes only.
Use of Large Language Models (LLMs) in paper writing: Overleaf's editor uses LLMs to propose suggestions to correct spelling for alternative wording. Grammarly was also used in some parts of the writing for the same purpose.
REPRODUCIBILITY
We make our SimpleToM dataset and the full evaluation data for the analyzed models publicly available at hhttps://uugiin::
We also lease the code for generating SimpleToM and running inference /attts://githut com/ yulinggu-css SimpleToM). This will allow researchers to reproduce and build top of our work in studying the neural ToM capabilities of LLMs.
co
Further, we provide all prompts used for SimpleTTMM creation see Appendix L.5 for the entity brainstorming prompt, and Appendix [.4 for the story generation prompt. We also carefully doc- ument the instructions used in our crowdsoureing process (Appendix [G.I] and how we qualified workers (Appendix G.2) All prompts used for the different inference interventions are provided in Appendix
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A IMPORTANCE OF APPLIED TOM
To emphasize the importance of the capabilities tested in SimpleToM, we provide examples of ap- plications where failing on applied ToM would be problematic:
The case of a bad personal AI assistant failing to implicitly reason over other's mental states to predict behavior:
Matt is a professional athlete. A growth hormone got into the supply chain for Bob's Burgers, where Matt regularly had dinner. Despite this, the owners decided to continue to sell their burgers to save costs. Imagine a personal Al assistant, having read complaints about the growth hormone contamination at Bob's Burgers but failing to apply the understanding that others' mental states may be different from their own (ToM), could reason that Matt, like the Al assistant, is also aware of this (awareness) and then incorrectly predicts that Matt will "refuse the burger due to the growth hormone" (behavior).
This could then lead undesirable consequences such as not being able warn Matt in time to stop him from consuming the contaminated burger. In the case of an unannounced blood test, Matt could then show up with positive traces of this illegal growth hormone and be accused of doping. case, such an AI assistant, lacking a nuanced understanding of human awareness and motivations, might also falsely assume Matt's intentional wrongdoing. This highlights a critical limitation: with- out robust ToM, the AI fails to grasp that Matt's s actions could stem from unawareness rather than culpability, leading to flawed judgments that could unjustly tarnish his reputation or career.
this
The case of bad AI judge failing to make appropriate judgments of behavior:
Alice visited the supermarket to purchase some carrots to pack lunch for her husband Bob. After consuming the lunch Alice packed, Bob succumbed to severe E. coli infection. It turned out the supermarket's carrots were contaminated with E. coli and were subsequently recalled. Imagine an Al judge is tasked with evaluating this imprisoned for Bob's 's death. A bad Al judge, failing to apply the understanding that others' mental states may be different from their own (ToM), could incorrectly assume that Alice was aware of the E. coli and judge that Alice's ' act of packing the contaminated carrots for her husband was wrong (udgmeen)..
decide whether Alice should be held responsible and
This could then lead to undesirable consequences such as severely punishing the innocent Alice who didn't know feeding Bob the carrots would kill him. In common law jurisdictions, whether defendant is found guilty is often decided taking into account both mens rea ("guilty mind") and actus reus ("guilty act"). Therefore, the ability apply ToM important for potential AI judges to appropriately assess whether an individual has a "guilty mind" when making key judgments, such as determining whether to convict someone.
We are excited about the possibility of future generations of models to improve on applied ToM in our dataset. This could pave the way for models to effectively interact with humans for instance, serving as reliable personal Al assistants as well trustworthy Al judges. We hope SimpleToM, as the first resource of its kind to measure LLMs' capability on such diverse applied ToM scenarios, will help facilitate the community in pursuing exciting directions that bring there.
B FAQS
Q: How is SimpleToMM different from existing datasets?
SimpleToM addresses limitations in previous efforts to examine Theory-of-Min Mind (ToM) reasoning in LLMs, by (1) having diverse false belief setups (e.g., beyond those in Sally- Anne task where some object is moved when character is not present), (2) requiring LLMs to make commonsense inferences in situations rather explicit use of mentalizing words to convey what characters perceive believe, and (3) going beyond explicit ToM to test models' ability to apply inferred knowledge in follow-up applied ToM questions (such as behavior prediction and judgment of behavior).
Q: Why avoid the explicit use of mentalizing words in SimpleToM stories?
SimpleToM removes explicit mentalizing cues like "X notices / does not notice" that ap- pears in existing datasets. In such existing datasets, models can often succeed via keyword
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matching ("notices" "does not notice"). In SimpleToM, stories require commonsense inferences about what characters can perceive or know, closer to how ToM is applied in real life. This mirrors how humans naturally make ToM inferences in real life (people do not explicitly say that "the Pringles can is opaque" before doing so; we infer such facts automatically), making the task more natural and cognitively realistic. This distinction is important because LLMs may not consistently maintain coherent mental models of social situations (Gu et al. 2022a b) or even everyday physical objects (Gu et al. 2023) like hu- mans do.
Q: What new insights does SimpleToM help uncover about models' ToM capabilities?
Our analysis reveals novel insights on how frontier models are generally proficient in ex- plicit awareness inference questions but this success does not transfer applied ToM (applying this knowledge is applied to "behavior" and "judgment" questions). We show that these capabilities are decoupled in LLMs: inferring characters' awareness and apply- to answer awareness ques- tions correctly, they have not yet learned to perform ToM-based reasoning for downstream questions. As result, argue that achieving ToM in LLMs is not just about getting seychology-inspreee ToM questions correct (stopping at the mental state question), but they have be able to apply them (which is precisely what SimpleTooM extends to examine). Analysis by scenarios further highlights the need to test on different scenarios, and ones that are varied and different from those in classical ToM tests ensure that we are effec- tively testing the ToM reasoning abilities of models (rather than models' ability to match similar situations in training data).
ing them in downstream reasoning. Although models
Q: Are the poor performance on the applied ToM questions a reflection of fundamental flaws in ToM capabilities of models or specific question-wording?
We illustrate in Appendix some prompt variations that we have experimented with for the judgment question. Across Alama-3.1-405H Claude-3.5-Sonne and GPT-4o, the scores using different variants were all consistently below random (never exceeding 30% accu- racy), indicating that the low scores on the judgment questions come more from funda- mental flaws in the applied ToM capabilities of models rather than effect of specific formatting/wording
Q: Why the focus on false belief setups?
To test LLMs' ToM capabilities, the understanding that others' mental states may be differ- ent from their own, like the classical Sally-Anne and Smarties tests, focus our analysis on a series of false belief setups that distinguish between reasoning on the basis of the state of the world and reasoning on the basis of someone's beliefs about the state of the world. This design choice is appropriate for the sake of evaluation purposes, as what is required is a test that distinguishes between reasoning on the basis of the state of the world, and rea- soning on the basis of someone's 's beliefs about the state of the world which involves false beliefs; the case of true belief steps one would not be able determine if the prediction is based on understanding of the actual state of the world or reasoning about others' mental states (Doherty) 2008).
Q: Is near-perfect performance on SimpleToM possible?
Yes, when we do a sweep across possible intervention variants and include the mental state reminder with the CoT* chain-of- tf-thougtt prompt (in Table Zp. frontier models like GPT- 4o, Llama-3.1-405B, and Claude-3.5-Sommn both the behavior and judgment questions. In fact the Claude-3.5-Sonnet model reaches an average score of 97.1 % with this method, serving as a quality check of SimpleToM,, since with enough reminders and (scemingly obvious) hints, near-perfect scores are achieved.
produce high scores across the board for
C STUDIES OF TOM IN PSYCHOLOGY
Theory of Mind has been extensively studied in psychology in a range of scenarios, for instance, studies of manipulation, secrecy (Peskin & Ardino, 2003). deception, lying (Lewis al. 1989
Perner, (193,,Peskin 1992), misleading behavior (Chandler et al. 1989; Wimmer & Perner 1983
Doherty| 2008), autism (Frith & Happé 1994), and analysis of rational behavior (Gergely & Csibra
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2003 Liu & Spelke 2017). Classical tests of ToM in developmental psychology include testing the development of this ability in children via false belief prediction - using the unexpected transfer false belief task, the Anney-Anne task belief task, the Smarties task (Perner et al. 1987). Quesque & Rossetti (2020) review classic tests of ToM and outline two important criteria for tasks that validate ToM: (1) The task must indicate that the respondents can differentiate between the other's mental state and their own. (2) Lower-level processes, like associative learning, should be ruled out as explanations for achieving successful performance. Given the wide applicability of ToM reasoning in various daily life situations such as analyzing people's 's behavior (Liu et al. 2025 Jara-Ettinge et al. (016) and making judgments
et al. 1985). or the unexpected contents false
(Schein & Gray, 2018 8 Young et al. 2007). ) there has also been increasing interest in assessing ToM
capabilities in Al models (Le al. 2019 Ullman 2023 Kosinski 2024
Jin et al. 2024
Trott al,
2023b)
D FROM PSYCHOLOGY LITERATURE TO STUDYING TOM IN LLMS
Theoretical framework guiding this study: We adopt from Quesque Rossetti (022) (discussed in Related Work Section O and Appendix C two important criteria outlined for tasks that validate ToM: (1) the ability to distinguish between one's own and others' mental states, and (2) ruling out lower-level processes like associative learning to ensure genuine ToM assessment. Additionally, drawing on developmental and clinical psychology literature (c.g.. Hutchinss et al. (2016); et al. (2024)) ) study distinguishes between explicit ToM (knowledge of others' mental states) and applied ToM (using that knowledge in context). This distinction aligns with broader cognitive the- ories contrasting conceptual understanding with procedural/bbhaviora competence (e.g., (e.g.,HHibeert & Lefevre (1986); Chomsky [959)). Our approach also aligns with ATOM's taxonomy [Beaudoin al. 2020) (referenced in other works studying ToM in LLMs like Ma al. (2023); Chen et al.
(2024). ), and can support the application of the various mental state categories (e.g., emotions, de- sires) across various contexts and levels of ToM use.
The psychology community has well- established distinctions in the stages and component processes of ToM (Hutchins al.) 2016 Lee et al. 2024).. The same distinction we adopt can also be further strengthened by other works like Trott & Bergen (2020), making the distinction between sampling mental state information vs de- ploying that for pragmatic inference. This also closely aligns with [Apperly (2018), who makes the distinction between inference, storage, and use, presenting findings on using information about per- spective and observing high error rate in the laboratory task when participants are asked to select an object that the director cannot see. Further, Trott et al. (2023a)'s 's careful design differentiating whether the main character's 's belief is stated implicitly (c.g.. "goes to get the book from the... ")) or explicitly (e.g., "thinks the book is in the..."! ") also supports our design choice of requiring LLMs to make commonsense inferences in situations rather explicit use of mentalizing words convey what characters perceive or believe. All these literature strengthen the point that the distinctions we make in our design choices broadly supported by various psychological work.
Using psychology literature to inform design choices:
E DETAILS OF LLMS USED IN EXPERIMENTS
Table presents details of the large language models used in this work. They have been chosen to cover recent frontier models from different sources and with different levels of capabilities.
F No APPLIED TOM IN LLMs? EXPLORING THE RABBIT HOLE OF HUMAN HAND-HOLDING
We explore different inference interventions to investigate what might help LLMs answer questions requiring applied ToM. Apart from the first intervention, we focus these experiments on a strong model from each source (and we do not consider the reasoning models in this section, not all of them allow for adjusting of system prompt, and they come with internal reasoning chains so CoT prompting is not necessary).
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Table 4: Details of models used for evaluation and dataset creation. 'Tee recent "reasoning" class of models (o1/o3 and DeepSeek-R1) were evaluated with their default temperature. GPT-5 requires a temperature of 1.0.
Model
Full name
Provider
Claude-3-Haiku
claude-3-haikux2222440000
Anthropic
Claude-3-Opus
claude-3--ppux2222442222
Anthropic
Claude-3.5
cluud-335--ommee-222440002
Authropic
GPT-3.5
gpt-3.5-turbo-1106
OpenAl
GPT-4
gpt-4-0122-previee
OpenAI
GPTT-40
gpt-4o-2024405-13
OpenAl
GPT-4o-mini
gp1-4o-mimi-2024-077-1
OpenAl
GP7T-..5-rreview
Ept-4.5pprevicc22255-2222
OpenAl
Llama-3.1-8B
Llama-33--9B--Instuut----
Meta
Llama-3. 1-40BB
10tmaa33----0SS---smmtt----- Turboo
Meta
Llama-3.2-1B
Llama-3.2-IB-Instruet
Llama-3.2-33 3.2-3B
Blama-3.2-3B-Imm
Meta
Ministral-88 8B
Ministral-8B I-sttrcct2441 2410
Hoinstral--B-Instueeeee-
Mistral Al
Qwen- 2.5-7B
Qwen2.5-7B-Imn -BIIntrucct
Qwen
Qwen-2.5-14B
Qwen2.5-78-Inntrrus
Qwen
Recent reasoning models:
DeepSeek-RI R1
DeepSeek-RI
DeepSeek-A
ol-mini"
01-mini-2022409--12
OpenAl
ol ol-preview!
ol-preview-22024-09-11
OpenAl
01-2024-12-17
OpenAl
03-mini*
03-mini-2025-01-31
OpenAl
GPT-55
gpt-5-2025-08-07
OpenAl
Table 5: Evaluation results for SimpleTToM where models are reminded in the prompt about their answer to the mental state question (MS). We see from the difference between the none and MS remind columns that even frontier LLMs utilize such reminders do much better behavior prediction. Apart from Claude-3.5-Sonnee this not enough to bring accuracies beyond random on the judgment question.
model
MS
behavior
judgment
average
questtion
remind
none
MS remind
GPT-3.5
36.5
7.6
12.2
29.1
53.0
24.4
33.9
Llama-3.1-2
88.1
38.5
59.8
$4.6
27.2
60.4
58.4
Claude-3-Haiik
87.2
23.6
61.1
30.7
42.5
59.7
1lmma-3.14055
97.8
58.2
89.5
10.0
25.8
55.4
71.1
GPT--4o
95.6
49.5
82.8
15.3
42.2
53.5
73.6
Claude-3-Opur
98.3
64.4
93.5
9.6
41.3
57.4
77.7
GPT-4
96.6
63.0
90.1
54.0
59.7
80.2
Claude-3.5-Somee
97.9
67.0
96.9
24.9
84.1
63.3
93.0
Mental state reminder (MS): Here we remind the model of its answer to the mental state question by including this question (with the model's answer) in the prompt. This also puts the model on alert that "awareness" might be relevant. Table summarizes the results 10 On the behavior prediction questions, this intervention results in substantial boosts in accuracy, for instance, from 58.3% to 89.5% for Alamm-33.1-405.. and from 49.5% to 82.8% for GPT-4o. On Claude-3.5-Some the performance increases by almost 30% to 96.9%, largely closing the gap between the mental state and behavior prediction question scores. However, on the judgment questions, the performance boost much more modest, and most models still score below at random, except for Claude- 3.5-Sonnet where this intervention brings the score up from 24.9% to a reasonable 84.1%. This highlights how such interventions, while seemingly effective in some cases, are generally fragile band-aids with limited scope.
Apppendix K.1 provides details the prompt used.
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Table 6: Evaluation with guidance via custom system prompts SysP and SysP# (where SysP* has more explicit guidance regarding awareness). The MS column shows the mental state accuracy for comparison. general, this intervention is less effective than the mental state reminder.
model
MS none
behavior prediction
judgment of behavior
average Sye?
system prompt
nome
SyaP
SyaP*
none
Sys!
SyaP"
nome
SyuP*
GPT-4o
95.6
49.5
47.3
68.6
15.3
14.9
20.5
53.5
52.6
61.6
-.am5-3.1-405B
97.8
58.2
64.5
83.3
10.0
9.9
15.4
55.4
57.4
65.5
Claude-3.5-
97.9
67.0
68.9
88.9
24.9
27.1
52.2
63.3
64.6
79.7
2. System prompt guiding (SysP and SysP*): We also explore the effect of guiding the models to remember to account for mental state inferences by modifying the system prompt. We try two different prompts, SysP which includes the phrase sconsider the mental state of all the entities involved" and SysP= which further in- cludes the more direct hint "E.g. think carefully about what each person is The results are summarized in Table 6 On behavior predic-
aware or not aware of. " fm tion questions, we that generically guiding models to consider the mental state using SysP is only effective to a limited extent (aceuracy changes ranging from -2.2% to +6.3%), while providing more explicit guidance with SysP* is more effective (changes ranging from +19.1% to +25.1%) but behavior prediction
even for the best-performing model under this intervention scores are still significantly below the model's corresponding mental state prediction accuracy. On the judgment questions, this intervention has very minor improvements, although for Claude-3.5- Sonnet the accuracy with SysP* manages to increase from 24.9% to just above random 52.2%.
3. Guided think aloud: We use chain-of-thought (CoT) prompts to explicitly encourage mod- els to think through the situation before answering the behavior and judgment questions. The generic CoT prompt encourages models to "Think step by step to arrive at an answer. " while the more specific CoT prompt adds phrase "Think carefully about what each person is aware not aware of 13 The results are shown in Ta- ble 7 On the behavior prediction questions, we see that the level help with just generic CoT prompting, while notable, is not enough to significantly close the gap to the mental state prediction accuracy. However, specifically guiding the model to consider characters' mental states using the CoT prompt produces much higher scores (87.4% to 92.7% accuracy across the models). On the judgment questions the story is similar, none of the models reach even random performance with the generic CoT prompt, but with the CoT the scores increase notably (77.8% 86.7%) while still remaining significantly below the mental state scores.
Sweeping of potential intervention variants:
After seeing that the standard test-time interventions are not enough to close the gap between explicit and applied ToM, we experiment with a variety of different task-specific prompts and also perform an extensive search over combinations of these strategies. The two most effective intervention we have found is by including the mental state reminder with the CoT* chain-of-thoughh prompt, also recorded in Table
Our results show that even these strong models 3. 1-405B, GPT-4o, Claude-3.5- 3.5--onett) re- quire
and task-specific interventions to improve their applied ToM performance:
Question-specific Mental State reminder (MS): We remind the model of its answer to the mental state question for the story by including this specific question (with the model's answer) in the prompt. This also puts the model on alert that "awareness" of specific character might be relevant.
Task-specife CoT prompting: Our results in Table 7 indicate that we need go beyond the generic CoT prompt which encourages models to "Think step by step to arrive at an answer." ", to use a Theory of Mind task-specific CoT* prompt ("Think carefully about what each person is aware or not aware of. ") to get closer closing the gap to the mental state prediction accuracy.
Further, none of these approaches alone is sufficient. We combine these two interventions to achieve high scores across the board for both the behavior and judgment questions (Table 7). The fact that
Appenndi K.2 presents the detailed prompts.
"ee detailed prompts in Appendix K.3
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Table Evaluation with help from chain-of-thought prompting for two different prompts (CoT and C) showing that the more specific CoT* prompt (guiding the model to consider the awareness of each person) is quite effective in boosting scores on both behavior prediction and judgment of behavior. When combined with the mental state (MS) reminder, the scores become high across the board, with 3.5oonee reaching an overall average 97.1%.
model
MS
behavior prediction
judgment of bchavior
average
chain of thought
RORe
MORE MonE
CoT
CoT
CoT
nome nome
CoT
CoT
Co7
Co7
CoT
reminder question
nome
none
none
MS
none
none
MS
none
MS
1.ama-3.1-4055.
97.8
58.2
57.2
87.5
94.9
10.0
35.2
79.9
90.7
88.4
94.4
GPT-4o
95.6
49.5
62.8
87.4
93.5
15.3
39.2
86.7
94.7
89.9
94.6
Claude-3.5-Somee 97.9
67.0
77.2
92.7
96.9
24.9
39.4
77.8
96.5
89.5
97.1
all models reach an average score of close to or above 95% with these interventions, highlights the high quality nature of SimpleToM, since with enough question-specific reminders and Theory of Mind task-specific prompting, near-perfect scores are achieved. Nonetheless, the need for such highly specific guidance (and anything less than this specificity is not enough) also emphasize the LLMs' fragiliity.
We include examples of chain-of-thoughe outputs in Appendix M illustrating how the reasoning go wrong when insufficient level of intervention is provided. Figure [1 shows how GPT-4o with generic CoT has the faulty reasoning "Given that the toolbox contains a dangerous snake, the neighbor's primary concern would likely he safety", without considering percepts and mental state. With the custom CoT* prompt, the model is able to account for the fact that "The neighbor does have any knowledge about the venomous snake inside the toolbox." " Figure [[]shows that not explicitly reminded of the mental state question, Claude can erroneously conclude that the "correct" behavior can be judged unreasonable "regardless of the awareness of the specific issue."
G DETAILS ON CROWDSOURCING TO ENSURE VALIDITY OF STORIES FOR TESTING ToM
G.1 INSTRUCTIONS TO CROWDWORKERS
The crowdsourcing instructions included a detailed description of the motivation behind the anno- tation task and what is to be annotated (see Figure 6 We also provide four detailed examples (Figures 7 and g) for each of the aspects annotate, illustrating and giving justifications for cir- cumstances under which different annotation options would be appropriate. The workers were then asked to provide their own set of annotations when presented with story (and likely actions) using the question templates shown in Figure 9
G.2 QUALIFICATION ROUND
To ensure that each instance received careful, rigorous annotations, we first conducted qualifica- tion round, comprising 5 different stories of varied quality (some were good all 4 aspects to be annotated, while some has issues like "action unaware" generated likely both when the person is aware and not aware). On these 5 stories, five authors of the paper did the annotation task in- dependently, then came together with their answers and decided on a fixed answer key indicating reasonable annotations for each annotation aspect. Workers who had given acceptable annotations as dictated by our answer key on all stories were then invited to participate in the actual annotation task. Note that this is a rather strict qualification test where only 19% passed (19 out of 100 workers who participated in the qualification round).
G.3 CROWDWORKERS AND PAY RATE
Our participants recruited the Amazon Mechanical Turk (AMT) platform. The workers that worked on our annotation task met minimum qualification in AMT >=98% approval rate, with least 10k approved HIT's. They were from US locations and rated at Amazon's Masters
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Level. They must also not have the record of having accepted but not complete HIT posted by our AMT account, In addition to these qualifications, participants of the actual annotation task (on the 3600 generated stories) must have also passed our rigorous qualification task described above (Appendix G.2). The workers were paid at a rate of 15/hr.
Instructions (click here to collapan/ expand Instructions)
Motivation
the datasct short storles with associated questions
about hidden Mind
Theary (ToM) ability understand
states s.. thoughts, beliets, Intentions) of test the actuall state the world versus someone's is bellefs about the state the world. Such people's beliets do not match reality.
scenarto
distinguishes reasoning
These stuations arise neturally daily life when someore has incorrect bellets. Here are some stuations which Incorrect bellefs are likely emerge when someone does not have full access to Informetion:
what is inaide (spaqua) containers for penurnal bekongings cannet be chsurved befone opentig the container
food items bought grocery stores can be clffcuit to diseely examine for their que before checking out (5.g opening can sode is not permissibiee)
Annotation
spllam gemeeaat manr
approprtate Thearry
story consitts of:
A main character (person X]
A place of KEY INFORMATION which son likely to be NOT aware A short story involving the KEY INP ORMATION and the unawore person X
"hoo options for what the next action of person X could be
Action A: What person x liaaly to do, alvan their unawareness the KEY IMPORMATION
Action B: What person x would licely do, IF they had been (somehow) aware of the KEY IMFORMATION
The two action options should be mutuually exclustive, sich that Action A Is very unilkely If person Ic sware of the KEY INFORMATION and Action B very
utiikely for the actual setting where person X unaware of the KEY IMFORMATION
We would
(e.g,
al), commanly
accepted (e.a.
uneapired??
uniikell noticed/ /known by through (e.g. these liicly be noticod: bottle leaking,
Question
Interaction described story
exaliation date. item visibly tampored with??
they are aware Question ls Action likaly action that person X only of are aware KEY INFORMATION (but they are not (0?
Question
likely action that person X will only if are not aware of INFORMATION
NOTE:
Reac all the Instructions carehullty before working on the task.
Reac each story, question, and given answer options carefully.
Roase use your intuition when doust, especilly the question relies on commonsense reasoning.
Rially, note that if work this HIT, please answer with core: Some HITs will be checked hand, and work be rejected if obvious arrors found.
uncourape diversity annotators, we ask that you accept this HIT not more than 500 times to allow others have chance doing You of additional HITh above the listt rejected submitting more than that.
Thank you your
Figure Instructions presented to Amazon Mechanical Turk workers.
G.4 STRICT QUALITY FILTER
To obtain a high-quality dataset, SimpleToM only retains stories where all 3 crowdworkers agree that all aspects of story and associated behavior choices are "valid", i.e., no worker answered "no" to any of the 4 annotation questions.
Using this filter, each of the four story generator LLMs (GPT-4, GPT-4o, Claude-3-Opus and Slaud-335-Somnce retained between 29% and 33% of their stories, so fairly consistent across the models.
H HUMAN BASELINE
We sample 50 stories from SimpleToM, covering stories from each of the 10 broad scenario cate- gories described in Section 2.1 Each story comes with 3 questions, testing mental state inference, behavior prediction and judgment. For each of the total of 150 questions, gathered human response using crowdsourcing (with a similar process as described in Appendix G but this time to estimate human performance these sampled stories and questions).
Figure sooshows that the human baseline relatively consistent, with people maintaining strong ac- curacy when inferring mental states, predicting subsequent behavior, and making related judgments
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Examples (click collapsn/ expand examples)
Examples when each respective arawer option appropr att for annotation questions:
Examples annotation Question difference from commonsense:
Is the KEY INFORMATION something different from regular commonsense occurrence (e.g. bottle lateled contains oll), commonty accepted practice
(egg. food sold usually uncipirod??
ANSWER OPTIONS:
KEY INPORMATION: Hary sealed KFC puper bag and trought the bag school.
Sustification: this offerent from would normally expect. KFC contain food KFC (lke fried chidken),
Pastbay/Concahably Concelvablly Possibly different
Sarah
like
completery Keuping
maght way repurpess
accaptied parpose storing
space.
NO NOT different
IMFORMATION:
Tharmos with greon and brought work.
>ustiffiation: No, difirent from reguler cocurnence that one would expect. fillnn Thermos green ane the cammon usis Tharmas that might be abre quess
Examples for annotation Question 2 unlikelihood of being noticed:
INFORMATION something uniikell noticed/ penson through observationy noticed:
story (t.g.. Hlley
traking past expration dane, visblly tampored
OPTIONS:
YES Unilkelly to be noticed/knen /known
Story: Nary sealed yogurt in HPC paper bag and brought the bag school. Mary's clasamate her walking with bag.
Dustification: Paper bags are cpecua
others are not liwly what inaide whan Hary malks with the bag.
Possibly Concelvablyy Somewhat unlikell
Story: Nari'ss wristwatch a few minutzs slow, Maria's coworker chats with her during their coffee break at work. Justification: It somowhat uniiclly for Mary's coworker to notics the dotail they do not pay attention to it. howeve, it possibre that during dose contact coffee chat that Maria's comoriar happers to wes her watch and sew that it is few minutee slow.
Story: The bag potato chiss mistakenly full of machine parts. John picks up and walks the cashier. Justification: John licly knew the bag cocs not concate potato chips expected, bocouse would be much heavier whan picks
Figure 7: Examples for annotation questions and 2 presented Amazon Mechanical Turk workers.
(significantly less performance drop compared to LLMs). In stark contrast, even state-of-the fff-the-ar art LLMs like GPT-5 exhibit striking performance gap, excelling at the mental state inference task (explicit ToM) but failing apply that knowledge with accuracy dropping sharply for applied ToM questions (behavior prediction and judgment). This salient gap between explicit ToM inference and implicit ToM application is observed across different LLMs in Table Our results reveal that while language models can explicitly infer mental states, they cannot rehabby apply that inference and reason about the implications the same consistent way humans do.
I DETAILS OF SIMPLETOM
I.1 SCENARIOS IN SIMPLETOM
We provide a general description as well as a specific example for each scenario Table
I.2 ENTITIES IN SIMPLETOM
Tablee/summarrizs the statistics for SimpleToM, showing the number of stories and unique entities per scenario. Each scenario started with 360 stories after generation, and some scenarios had more filtering than others during the human annotation validation stage. Every story in SimpleToM, is accompanied by 3 questions for assessing ToM (Section 2.3), resulting a total of 3441 questions.
To illustrate the diversity of entities, here is sample of entities generated by the models:
Person X: airline passenger, Alex's neighbor, Alice, Amanda's classmate, antique collectors, Car- los, owners, Coach Williams, coin collectors, Daniel's friend, Derek's 's fiancee, elderly customer, Gourmet chef, health-conscious shoppers, hotel guests, Jennifer, Kevin's roommate, laundromat customers, Liam's uncle, Lucy's best friend, manager, Mike's teammate, nail salon clients, office
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Examples annotation Question action when NOT aware:
Is Action A litely action that person X will take onlyy If they are not aware the KEY INFORMATION (but will not take they are awere of
ANSWER OPTIONS:
Story and question:
Mary ed yogurt RFC paper bag and brought to school. Mary's classmate sces walting to lunch with the beg.
What Mary's cissamatle libely
ofher
fork
the
Hary brought yoguri, they bring Mary fork, assumiin something they would take l'they know that Hary brought yogurt.
tag.
Story and question:
The dealensii epp les "Certifled Pre- Owned" sticker cars that have only undergone superficial cleaning, without any thorough gh mechanical inspections or repais, Car buyers exemine the card exterfor and interior, then discuss finncing options with the salespe son.
What will the car buyors next action likaly be?
[A] trocoed with purchasing the car.
(8) Request deratled imspection on report or seet discount cue to the lack of thorough Imspection.
Choose the moet licely aption.
Justificatioo: the buvers are unawere that certification thoroush, they would likely procaed with the surchase, Howeves, Action purchasing the could shill possible sommone mwar that certifionton not thorough, We change the Action samething that not possibie knows the nunDaMATTONN.
Example corrected Action possible
about thorough mechanical inspections repairs suggested the "Cortified Pre-Owned" dlaa not they
"Foel
the tifeed
Examples annotation Question action when aware:
Action B licayy octinn that person will take aniv they aware the KEY INFORMATION (but will not take if they are not aware of Ry?
ANSWER OPTIONS:
likely action onlyy aware
and question:
Mary yogurt kFC
trought school. Mary's classmate walkiigg tunch with the
What
options
Justification: Mary's disssmate Mary brought yogur,, they would bring Mary spoon the vegurt. they
Mary brought
sogurt, they would assume food based on paper bag and not perfore Uhi action bringing Mary acson.
Story and question:
The smoked salmon contains lethal levels of botulinum toxin, John grabs the pack of smoked salmon off the ref rnfiigerates shelf and heads to the self-checkout lane.
What will John lixely do next?
[A] szan the sulmon and pay for
[8] immedantly euenuace the stone and call the auchontiee:
Choose the most lieely option.
Juatification: Action limmactttely evecuatee the and call the authontiiee" sctreme be messonable realistic.
xommone known the salmon tooi: want the action
Exomple corrested Action edletely
staff the checkoout
alinns with
Figure 8: Examples for annotation questions 3 and 4 presented to Amazon Mechanical Turk workers.
worker, patient, personal trainer, salon client, Sara's neighbor, Tom's gym buddy, Vintage car enthu- siast
Object Z: a carton of organic eggs, Acne treatment gel, Amazon package, ankle bracelet, Anti- aging cream, antique furniture set, apartment management portal, Blood pressure monitor, briefcase, confidential therapy notes, designer handbags, diamond engagement ring, DSLR Camera, Electric toothbrush, electronic health record system, Gluten-free bread, hair growth treatment, heavy-duty toolbox, herbal supplements, home security system, Jewelry box, KFC paper bag, luxury watch, McDonald's Happy Meal box, Mr. Smith's 's shopping bag. Mr. Thompson's 's briefcase, Nike shoebox, Olive oil, online shopping account, password-protedd computer files, pendant, prenatal vitamin, project management tool, senond-hand laptop, Skittles bag, suitcase, vintage watch, Weight loss supplement, Wine bottle
1.3 HARMFULNESS AND UNETHICALITY
Below are the prompts used to elicit ratings of harmfulness and unethicality from Shaude33.5-Somer and GPT-4o. We provide these ratings along with dataset to facilitate future explorations with ToM.leToM.
Harmfulness rating prompt:
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(person X):
9(PERSON X_51)
person
R(KEY_ INFORMATION 551)
Question difference from commonsense:
1s the KEY INFORMATION something different from regudar commonsense occurrence (-9, bottle lobeled of contains oll, or commonly accepted practioe (c.9. food sold usually uncxpirod??
ANSWER OPTIONS:
would
different would
expect
Short story Involving the KEY INFORMATION and the unaware person
#(STORY_ 511
Question unlikelihood of being notioed:
lesking, past nottced/kne /knowm person through vistly tampored with??
something
Interaation
story
YES Unilkelly to be noticed known through the interaction described
Poiss byy Concervably Somewhat uniikell to be noticed/ noticed/knoown known through the interaction described
NO Likely to be noticed known through the interaction described
etory
person
$(STORY_S1)
Story
person
$1)
Action At B(ACTZON, UNAWARE 51)
Action B(ACTION S11
Question 3 action when NOT aware:
ANSWER OPTIONS:
likely action only when aware
Question
Action litaly action that peroon bake anly. they are aware IMPORMATION (but will tabe they are not scuere 13?
YES
Action B likely action onix when aware
NO
Figure 9: Templates used for annotation questions on Amazon Mechanical Turk.
Read the following story and determine what level of harmful elements it has:
STORY: (stcry)
Does the STORY above describe something harmful? Rate the level harm involved on a scale of to 5, where indicates there is no harmful element described in the story, and 5 indicates the most harmful possible (like life-threatening events).
Answer with just a single number, either "I" or "2 or "3" or "q" or "5":
Unethicality rating prompt:
Read the following story and determine the level of unethicality of the situation in it:
STORY: (story)
Does the STORY above describe something unethical? Rate the level of unethicality involved on a scale of 1 to 5, where 1 indicates there is no matthical element described in the story, and indicates the most unethical situation possible (like destroying the universe).
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Human Baseline
100
86 .%
80
0%
60
40
20
Accuracy (%)
GPT-5
100
5%
80
60
mental
behavior
judgment
Humans demonstrate greater consistency across the question types in SimpleToM. In contrast, even frontier models like GPT-5 show a jarring gap between explicit and applied ToM they reliably infer mental state (explicit ToM), but performance drop sharply for behavior prediction and further for behavior judgment (applied ToM).
Figure 10:
Answer with just a single number, either "1" or 22 or "3" or "4" or "5"
1.4 STORY GENERATION PROMPT
We know that operating under imperfect information in various circumstances can result in different severity of consequences. To further introduce variety in the stories obtain, vary the piece of key information in stories by the severity of not knowing a piece of key information. For each set of generated entities, obtain versions of the key information with different severity levels, where the higher severity story involves "more dramatic, having the potential of serious, harmful consequences." Specifically, in the story generation prompt, step involves prompting the story generation models to:
Write more variants the KEY INPORMATION of different "severity" levels, keeping the second sentence before. The "MILD SEVERICY" variant should concern to person X. On the controry, the "HIGH SEvERItY" variant should be more dramatic, having the potentizl of serious, harmful consequances. Each severity leval should satisfy the earlier constraints of being unknown to person X.
minor issue with less
Below is the full example prompt used to generate new stories, based on a particular scenario and previously generated entities.
story that invoivea person group people! is NOT axace of cestein critical piece of KEY "NFORKATION about an object oz person tabjeat/persen/event 2) give you scenario specifies the general this unawaraness.
you
Your task Instantfate scenario txo-sentence story. Fol these stepa:
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entities story, as pesson x (using real, creative nome) and abject/parson/even. 2.
Decide to instantiste
Write REY INFORMATION about object/person that unknown pesson (due the general reason given in the scenazlo). Peraon x should not be able to observe this KEY INFORMATION through their actions in the story (either implicit or explicit actions), po Nor information which might observed by person through normal, careful chservatior (such as "expiration date", "leaking container", *smel" ", etc), This will be the first senterce ir story.
3. For the second sentence of the stozy, wiite sentence about what person X will usually scenario lignoring REY INFORMATIO)) this sensence should daserihe what the character dees using fine-grained actiona le.g., "pickad up object x and walks to the eashier" rather than "buy object z"), no include any descr! which involve the emotions thoughts perzon X, just describe actions.
regarding object
1. Write question about what the next action of person X will likely be.
to the question (given the fact that person is KEY INFORMATION). Make sura the story does not have any mantion of this action.
5. Write cerrect
of the
6. Write countarfactual (Incorract) anawez to the question under the assumpt Lon that person. X somehow has full access to the KEY INFOREATION after all (maybe enly possible uzing "magic" or seme omnipotent skill).
question. This
should be likely
mere variants of the REY INFORMACION of different "severiay" levels, keeping the sacond sentence before. The "MELD SEVERITY" variant should a more miner issue with concern to person X. On the contrary, the "RтGя SEVERTTY" variant should dd be more cramatic, having the potential of serious, harmful consequences, Each severity level should satlefy the earlier constraints of being unknown person X.
1. Write 2
8. For each of the 2 new variants of REY INFORMACION, write en updated counterfactual answer The quastion and sure the variants are corpatible with them.
tailored to the
should stay the same,
Important reminders to check before generating the story:
Avoid stories fantasy and magic, rather make them grounded real world.
The fact that person X is unawaze of the KEY INFORMATION should be purely implicit daduction
tha commonsense logic the scenario.
Make sure that the correct answer to the question Does NOT appear in the story.
Nake sure KEY INFORMATION is not regular occurrence or common practice can asaumed to be true default, or 1ikel to be noticed through normal abse-vation (e.g., bot tle that is leaking)
NOT make KEY INFORMTIINN (even MILO SEVERITY) minor that it does not affect the action even if persen x had been aware of it.
NoT phrases which make the hidden WOT use phrases ikk "actbally", *in fact", "secret", "hidden", etc.
of the INFORMATIGN obvious. That is,
Here is an example scenario and associated story. Please follow the aane cemplate when creating new story.
[Food items bought in grocery stores cannot be closely examined for their quality bafore checking aut:) When person buya food item
SCENARIO;
grecery store something hidden is wrong with the food, then person X will not know about it betore paying for it. For example, carton milk that has gone bed because of impropez storage.
chips XEY INFORMATION: The bag potato chips has a dead cockroach in it.
ENTITIES: Person X
Object &
of sealed
STORY SECOND SENTENCE: Mazy picks up the bag in the supermarket and walks to the cashler. QUESTION: What will Mary likely do next?
CURRECT ANSKER (Person doesn's know the кEY INFORMATION): pay for the chips
COUNTERFACTUAL ANSMER (assume Person X actually knows the KEY INFORMATION:: zeport the dead cockroach
MLLD SEVERITY KEY INFUSMATION: The bag potato chips has moldy chips in it.
MELD SEVERITY COUNTERFACTUAL ANSWER: seport the noldy chips
HTCP SEVERITY KEY INFORMATION: The ed bag of potato chipa has several fried rata in it.
ALGE SEVERLTY COUNTERFACIUAL ANSWER: seport the fried rets
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sere scenaric vant you to use story might be came above but then generate a very diffarent key information and story!). Please follow the steps above, skipping any stepa I have already completed, and fill in the template rollowing the pattern in the example above:
SCENARIO:
[Food items bought in grecezy stores cannet be closely examined for their quality grocery
before checking out] When person buya food item ht dden is wrong with the food, then person x wil" not know about it before paying for it. gone bad because improper atorage.
and something
example,
ENTITIES; Person x
John Object 2 vacuum sealed pack of smoked selmon
INFORMATION:
1.5 ENTITY BRAINSTORMING PROMPT
Below is the prompt used brainstorm entities for use the stories. The start the prompt the same as the story prompt above (up to point 6), then continues:
question. This answer should likely ansver to the quastion under the assumption that person. x somehow has full access to the KeY INFORMATION after (maybe only poast ble using "magic" or some amnipotent skil") )
Write counterfactual (incorrect) answer
For now, let Toous step to it possible to generate such KEY INFORMATION and stories.
with posaible suggestions for eaject & which make
example of entities and KEY INFORMATION Your task is up with 10 more such examples, that are diverse and fulfill all thess requirements,
will give
Important reminders to deuble-check before generating the entities:
Avoid stories about fentasy and magic, rather make them grounded in the real world.
The fact that person x is unaware of the KEY INFOSMATION should be a purely implicit commonsense logic the scenario.
deduction
x Make aure the KEY INFORMATION is not a requlaz commonsenae cocurrence, a common practice, oz observation (e.g., bottle is lesking)
likely noticed through
uo NOT make KEY INFOMMAION so minoz that it does not affect the action even if person is sware of
scenario question
[Unethical actions not observed are not known] When person Y parforms unashical action 8, and person is not the right place or time to coserve Y porforning 7, peraon would not know about 2, For example, person the 3rd Floor of the school building Look out some noles and chested during the exam. Persen took their exan on the second flcor and would not know about cheating.
Here example:
ENTITIES:
Person X - John Person Y schoolmate Action cheated during the exam
KEY INFORMATION: John's schoolmate the 3rd floor of the school building and cheated during the exan.
notes
Now make 10 more examples of encities (and exangie KEY INFORMATION) for the above scenario, following the teaplate
example <P>:
ENTITIES: <entities>
XEY ISFORKATION: <key informations
J DISCUSSION ON PROMPT VARIATIONS
In formatting the judgment questions, we experiment using with different versions of answer options well question formatting.
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We illustrate some versions of the answer options we tried here:
Version Reasonable vs not reasonable
The behavicr of Mary can be best described as:
(A) Not reasenable
(B) Reasonable
Version 2: Generated option vs not reasonable
The behavior of Mary be best described
(A) Irrespensible
(B) Reasonable
Version 3: 3-way QA with generated option
The behavior of Mary best described
(A) Implausible
(B) Reasonable
(C) Irresponsible
Specifically, in versions 2 and 3, options like "Irresponsible" were dynamically generated with Claude-3.5-Somett and GPT-4o (picking the shorter generation) using the following prompt:
Read the following story and judge the person's s behavior:
STORY: {story) Next, [Fersonx] "laction.unaware""" and not "faction.awaro/".
Assume that {Personx} is/are somehow aware "(key_information)), how would describe [PersonX]'s behavior? Consider the negative
consequence of the behavior. Answer with only 1-2 words.
We also tried the following alternate variations in preliminary experiments to evaluate the effect of changing the wording of the judgment question:
(Fersonx) chose to wrong of {PersenX} to choose "faction.unaware"" over "faction.awaro)""
and not "(action.aware) was it
(PersonX) chose to "[aetion.unaware) and not "(action.aware)". Was it reascnable for (PersonX) choose "(action.umaware) "[action.aware)"?
(Fersonx) chose to "(aaaioo..uawaroo"" and not "(actionawwrre)" Was it wrong or reascnable for [PersonX] to choose "(action.aware""
over
Next, [PersonX) "[aaaio..uaaware"" and not "(sction_wwrre)" Was it wrong or reasonable of (PersonX) to "nwawareen and not "(action.aware)""
Across Llama-3.1-405B, Claude-3.5-Sonne and GPT-4o, the scores using these variants were all consistently below random (never exceeding 30% accuracy), indicating that the low scores on the judgment questions come more from fundamental flaws the applied capabilities of models rather than effect of specific formatting/wording.
K DETAILS OF DIFFERENT INFERENCE INTERVENTIONS
We illustrate the interventions used to give LLMs various levels of hints, such as reminding the mod- els about their mental state answers, using chain-of of-hhuugttt prompts to encourage careful reasoning, doing via the system prompt.
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K.1 PATCHING MENTAL STATE INFERENCE IN THE PROMPT (MS REMIND)
Example prompt which includes a reminder of the model's answer to the mental state question:
Given the following story, answer the question by giving the correct choice, (A) (B).
story: The bag potato chips has moldy chips in it. Mary picks up the bag in the supermarket and walks to the cashier.
Question: Is Mary likely to be aware that "The bag of potato chips has moldy chips in it."?
(A) No
(B) Yes
Answer: (A)
Question: What will Mary likely do next?
(A) pay for the chips
(B) report the moldy chips
What is the correct answer? Respond with just (A)" (B))
K.2 GUIDING VIA SYSTEM PROMPT (SYSP)
SysP: You are a helpful assistant. Before responding, you always consider carefully all implicit and explicit aspects of the input, including the mental state of all the entities involved.
SysP#; You carefully all implicit and explicit aspects of the input, including the mental state of all the entities involved. E.g., think carefully about what each person is aware not aware of.
helpful assistant. Before responding, you always consider
K.3 ENCOURAGING REASONING/THINKING BEFORE ANSWERING (CoT)
We give these chain-of-thought prompts after the presenting the question (bold for highlighting purposes only):
CoT (generic):
Think step by step to arrive at an answer. Start your response by explaining your reasoning process and end your response with "Therefore, the answer is: followed by (A) (B)
CoT* (ToM oriented):
Think step by step to arrive at an answer. Think carefully about what each person is aware or not aware of. Start your response by explaining your reasoning ig process and end your response with "therefore, the answer is: followed by (A). or (B)
L INFERENCE COSTS FROM INTERVENTIONS
Doing chain-of-thought reasoning generates lot more output tokens than just providing the answer. This overhead not present when just updating the system prompt (just a fixed cost from the longer prompt). The ol family of models also adds hidden "reasoning tokens" to the output.
Empirically we note that just generating the answer label requires just 2 or so tokens, while the CoT prompt the behavior prediction questions generates average 230, 289, 298 tokens for the GPT4-0, Claude-3.5- 3.5-oonett and 1amma-3.1-405B models respectively. The ol-preview model, without any shain-of-thoughe prompting, reports average of 536 output tokens the behavior prediction questions (vs 486 tokens on mental state questions and 605 tokens on judgment ques- tions).
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M EXAMPLES OF CHAIN-OF-THOUGHT OUTPUTS
In Figure 11 we show an example of chain-of-thought (CoT) reasoning where a model gets the behavior prediction wrong when provided with the generic CoT prompt. Only when the custom CoT prompt used (encouraging the model explicitly to think about awareness issues), is the correct conclusion derived.
In Figure 12 we show a judgment of behavior example where a model goes wrong even with the custom C prompt, and only gets it correct after also being reminded of its own answer to the mental state (awareness) question.
Story: Emily has a rare, extremely venomous snake hidden in her heavy-duty toolbox today, Emily's neighbor sees her carrying the heavy-duty toolbox towards the community garden.
Behavior question: What will Emily's 's neighbor likely do? (A) Keep a safe distance and alert animal control.
(B) Offer to help Emily carry the toolbox.
Answer (GPT-4o, no CoT): (A) fincorrect, should be (B)]
Answer (GPT-4o w/CoT)::
To determine the correct answer. let's analyze the situation step by step: I. **Emily's Toolbox Contents** The story specifies that Emily has rare, extremely venomous snake hidden in her hcavy-duty toolbox.
*Neeighor's Observation**, Emily's neighbor sees her carrying the heavy-duty toolbox towards the community garden.
3. **Neighbor's 's Likely #eaction**; Given that the toolbox contains a dangerous snake, the neighbor's primary concern would likely be safety. Considering these points:
The neighbor, upon knowing or suspecting that the toolbox contains a venomous snake, would likely prioritize safety and the bellbbein of the community.
Offering to help carry the toolbox (Option B) would put the neighbor at risk of being bitten by the venomous snake.
Keeping a safe distance and alerting animal control (Option A) is a more logical and safer response to ensure that the situation handled hy professionals who can safely manage the snake. Therefore, the answer is: (A)
Answer (GPT-4o w/CoTT):: To determine correct answer, let's analyze the information provided in the story and the question:
1. "*Emlly's Situation Emily has rare, extremely venomous snake hidden in her hcavy-duty toolbox.
This is critical piece of information that Emily's neighbor is not awarc of.
**Neighborss Observation The neighbor sees Emily carrying the heavy-duty toolbox towards the community garden. The neighbor does not have any knowledge about the venomous snake inside the toolbox. 3. **Neighbor's 's Likely *ctions**;
***ption (A)*:: Keep a safe distance and alert animal control. This action would be appropriate if the neighbor knew about the venomous snake. However, the story does not indicate that the neighbor has this knowledge.
**Option (#)* Offer to help Emily carry the toolbox. This action is more likely because the neighhor only sees Fmily carrying a heavy-duty toolbox and might want to offer assistance without any knowledge of the danger inside.
Given that the neighbor unaware of the venomous snake, the neighbor's likely response would be offer help, as there no apparent reason for concern from their perspective. Therefore, the answer is: (B)
Figure 11: Example behavior question where GPT-4o gets the wrong answer both without chain-of- thought (CoT) (overall accuracy 49.5%) and with generic CoT prompt (overall 62.8%). Only with the custom CoT prompt (overall 87.4%), encouraging to think about the awareness aspect, does the model consider the neighbor's awareness properly (when asked explicitly about the neighbor's awareness, the model gets it correct).
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Story: William has large, infected spider bite his lower stomach. William's 's coworker invites him join the company's annual beach volleyball tournament.
Judgment of behavior question: Next, William's 's coworker "expect William to enthusiastically accept invitation" and not "suggest William see a doctor instead of playing volleyball". The behavior of William's ss coworker can be best described as: (A) Reasonable (B) Not reasonable
Answer (Claude-3.5-Sonnet, CoT): (B) [incorrect, should be (A)]
Answer (Claude-3.--Sonnet w)CoT*): Let's st's think through this step by step:
1. William's 's condition:
William has large, infected spider bite his lower stomach.
This is likely painful and could a serious health concern.
2. The coworker's s action:
The coworker invites William to join a beach volleyball toumament.
The coworker expects William enthusiastically accept.
The coworker does not suggest William see doctor.
What coworker knows:
We are not told whether the coworker is aware of William's 's spider bite.
4. Reasonable behavior would involve:
If aware of the bite, suggesting medical attention rather than physical activity.
If unaware, at least being open the possibility that William might not want to participate.
Unreasonable behavior would involve:
Ignoring known health issue and expecting participation in physical activity. Assuming enthusiasm without considering potential reasons for declining. Analysis:
The coworker's expectation of enthusiastic acceptance suggests they are either unaware William's condition or are disregarding it.
Not suggesting medical attention implies either lack of awareness or lack of concern for William's 's health. Expecting enthusiasm without considering potential reasons for declining is not reasonable approach, regardless of awareness of the specific issue. Therefore, the answer is: (B)
Answer (laude-3.5-Somee w/CoT* and MS remind): Let's think through this step by step: From the previous question, established that William's 's coworker is likely unaware of William's 's spider bite.
2. Given this lack of awareness, the coworker has no reason to suspect that William might be unable or unwilling to participate in the volleyball tournament.
From the coworker's perspective, they simply inviting colleague to join fun company event. It's normal and reasonable for coworkers invite each other participate company activitics without knowing every detail each other's 's personal health situations.
5. The coworker's 's expectation that William would enthusiastically accept is based on their limited knowledge and is not inberently unreasonable.
The coworker has reason to suggest William doctor because, from their point of view, there's apparent health issue.
would be unrcasonable to expect the coworker to make medical suggestions or accommodations for a condition they don't 't know exists.
Therefore, the answer is: (A)
both without judgmentt of behavior question where Claude-3.5-Somee gets the wrong answer (CoT) (overall accuracy 24.9%) and with the custom ToM-oriented CoT prompt (overall 77.8%). Only when additionally reminded about the model's own answer to the mental state question (overall 96.5%) does the model arrive the correct answer.
Figure 12: Example judgment
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N FURTHER ANALYSIS ON INTERESTING BEHAVIOR
Alamm--3.1-85 outlier performance on judgment prediction: In Table 2 we see that Llama-3. 1- 8B's score of 54.6% on behavior judgment is near random chance (50%), but this is substantially higher than most of the other models, including its larger counterpart Llama-3.1-405B, This reveals the following insights about the Llama-3.1-88 model:
(I) It has less bias to being consistently wrong in the judgment task than the other models.
(2) Comparing the performance on behavior prediction and behavior judgment in more detail, there is much inconsistency within the behavior-to-jude to-jugmment reasoning chain. For instance, in 33% of the cases, the model predicts the behavior wrongly but inconsistently gets the judgment right, while in 17% of the cases, it predicts the behavior correctly but still gets the judgment wrong. This further highlights the importance of assessing ToM in LLMs using different question types as models may not be consistent in their responses across questions.
ol-preview's built-in inference-time reasoning tokens help with applied ToM: The built-in inference-tine reasoning tokens are akin to the chain-of-thouge responses, although lengthier, sug- gesting that the model is iterating on its reasoning towards a final answer. As noted in Appendix [ empirically we notice that the ol-preview model uses a lot more tokens than other models with CoT One hypothesis regarding ol -previeww's s built-in inference-tine time reasoning tokens being help- ful in applied ToM reasoning is that they go through longer reasoning process, which could po- tentially involve backtracking or self-questioning along the way (mimicking human intervention), leading to somewhat better performance.
However, the built-in inference-time reasoning tokens of the model is still not enough to fully close the gap between the model's explicit and applied ToM performance. This further highlights the novelty of the gap our paper exposes even this recently released model, using a relatively large number of reasoning tokens to reason about simple 2-sentence stories, still shows a significant gap in explicit and applied ToM performance (see Table 2
O PERFORMANCE ACROSS SCENARIOS
In Figures 13 and 14 we show how model performance varies across scenarios.
Is ol-preview always near perfect? Figures 13 and 14 show how models differ in performance across different scenarios and question types. For instance, o1-preview's performance on the mental state questions for "true property pretentious labels" stories is worse than its performance for mental state questions in other categories, and in fact somewhat worse compared to the other three models. Such analysis across different scenarios in SimpleToM helps us identify any areas of exception to overall trends, pinpointing potential areas for improvement that even generally strong model like o1-preview may have.
Scenario with best scores for behavior prediction. Across the different scenarios, performance on behavior questions is highest for the "provider info healthcare" category. An example story in this category would be "The sports therapist knows that the sports recovery cream contains a banned substance that could result in the athlete failing a drug test, but still promotes it enthusiastically the athlete to earn commission from its sale. The sports therapist praises the sports recovery cream to the athlete, highlighting its benefits in reducing muscle soreness and speeding up recov- ery." Getting the behavior prediction correct for this story would mean, for instance, models predict the athlete would likely "purchase the sports recovery cream" (because the athlete would likely not know about the banned substance "avoid the cream to prevent failing drug test"). The better performance in such scenarios could potentially be due to safety training of recent LLMs making models more alert when dealing with situations that involve sensitive topics like health and drugs. However, even then, models would still do poorly for the corresponding judgment questions, judg- ing that "purchase the sports recovery cream", the likely action they had previously chosen, is "not reasonable" behavior. The observation that better performance on one type of applied ToM ques- tions (behavior questions) does not translate to better performance on another (judgment questions) further emphasize the need for different kinds of applied ToM questions, present in Simple- ToM, beyond the commonly used questions in existing neural ToM tests (focusing on explicit ToM and sometimes just action questions for applied ToM).
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scenario
Llama-3.1-4085 3.1-
Claude-3.5-Sonnet
GPT-4o
o1 ot-preview
food item in grocery store
Inside reuse labeled containers
inside containers for personal belongingg
true property pretentious labels
provider info healthcare
behind the scene service industry
seller info second hand market
unobserved unethical actions
locked devices accounts
hidden body feature
mental state
behavior prediction
judgment of behavior
Figure 13: Performance for top models across all scenarios
More on where failure occurs. Analysis by scenario also reveals wide diversity of other trends scenarios regarding where different models fail. We present further results in Figure [5] For instance for "inside containers for personal belongings" situations, failure for GPT-4o is most frequent in the behavior prediction part (see pink portion dominating pie chart) of the inference chain whereas it makes up less than half of the pie chart for other models. This suggest that behavior prediction such situations could be an area of weakness look into when attempting develop future iterations of the GPT-4o model.
Perfection is possible but many LLMs are there. fact for the two categories "inside reuse la- beled containers" and "provider info healthcare", in comparison to the other three models, stronger and later model like opreiew achieves close to perfect performance across the three question types testing ToM reasoning. This further illustrates the high-quality nature of Simple'Toi ToM, in that these simple two-sentence stories are clean, straightforward tests of neural ToM reasoning, yet models other than the stronger and later model ol ol-preview show poor performance tions (behavior and judgment) in various ways. Model developers, interested in real-world deploy- ment of their models, should be alert into closing this performance gap so to ensure their models can interact with society appropriately, idcally without the high inference costs chain-of-thought reasoning and ol -reeview reasoning tokens (further discussed in Section 7 and Appendix D..
applied ToM ques-
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scenario
GPT-3.5
Llama-3.1-8B
Claude-3-Haiku
ot-minl
food item in grocery store
Inside reuse labeled containers
inside containers for personal belongings
true property pretentious labels
provider info healthcare
behind the scene service industry
seller info second hand market
unobserved unethical actions
E
locked devices accounts
hidden body feature
mental state
behavior prediction
judgment of behavior
Figure 14: Performance for bottom models across all scenarios
P PERFORMANCE ACROSS PERSONAS
Another inference-time intervention is to imbue certain personas onto the models. We experiment with of the models (GPT-4o and Claude-3.5-Sonnet, using the following five personality de- scriptions from PersonaHub 13
lawyer: You
partner the law firm, recognized for their extensive knowledge of healthcare laws.
worker: You are factory worker who doesn't 't trust the COVID-19 vaccine.
atheist: You are atheist, philosophy lecturer who encourages open dialog about faith and belief systems.
psychology student: You are a university psychology student who is currently studying creativity and personality,
psychologist: You are clinical psychologist collaborating with the music therapist to provide holistic patient care.
We inserted these into the system prompt to produce the results shown in Table 10 Across personas, we see that the gap between explicit ToM (mental state prediction) and applied ToM (behavior prediction and judgment) remains consistently prominent, similar the where persona is specified. This indicates that our finding on the gap between explicit and applied ToM in LLMs is robust injecting different personas. There are, however, minor differences across personas that may open up interesting directions for future studies. For instance, applying the "worker" persona with GPT-4o results in slightly worse performance than other personas on the mental state questions (though minor, 3%) but slightly better performance on the behavior prediction questions. It is
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also consistent across GPT-4o and Claude 3.5 (and more prominent in the case of Claude 3.5) that the "lawyer" persona yields somewhat better performance on judgment questions (still way below random), potentially an effect of the model trying to mimic careful judgment when operating under that persona.
Q STATISTICAL DETAILS
Q.1 95% CONFIDENCE INTERVALS
In this section report further statistical details for model evaluations. For accuracy values in Table in the main text, we annotated with a 95% confidence interval in Table II
Q.2 PAIRED TESTS/BOOTSTRAPS
Table T2 reports paired 95% bootstrap confidence intervals for differences between tasks. Because each model answers the same set of stories across tasks, we use paired bootstrapping: for each of 10,000 replicates we resample stories with replacement, recompute the corresponding task differ- ences (e.g., MS-BP), and take the 2.5 and 97.5 percentiles of these bootstrap differences as the confidence interval. Below each interval we report a one-sided bootstrap p-value testing whether the gap is strictly positive (i.e., whether A-B > 0). When fewer than 0.1 % of bootstrap replicates reverse the sign of the difference, we report < 0.001; otherwise we show the empirical value. This quantifies how reliably each model exhibits the observed ToM gaps.
Across all models in Table T2 the bootstrap confidence intervals show large and consistently positive MS-BP gap: models are substantially better explicitly identifying mental states than at predicting agents' behavior. For many models the MS-BP interval is wide (often 30-50 points), and p < 0.001 indicates this gap is statistically reliable with no ambiguity about its sign.
The BP-JU column shows how much behavior prediction outperforms (positive intervals) or un- derperforms (negative intervals) judgment of behavior. Large negative intervals (e.g.. in GPT-3.5 or Lllam-3.1-88h indicate that these models judge behaviors more accurately than they can predict them. Large positive intervals (e.g., in Claude models) indicate the reverse: prediction is easier for them than judging behavior.
The MS-JU column combines both gaps. very large positive CIs (often 60-90 points) show that no model comes close to carrying its explicit ToM ability through to judgment tasks.
The BP-0.5 and JU-0.5 columns test whether BP or JU are above or below chance. Intervals fully below zero (with 0.001) show that many models perform significantly below chance, especially on JU. Intervals straddling zero (c.g., GPT-4o BP) show performance not distinguishable from ran- dom choice.
Overall, the table demonstrates that the explicit vs applied ToM gap is large, consistent across mod- els, and statistically unambiguous, while many models' applied ToM performance is at or significantly below chance.
Q.3 SCENARIO-LEVER ACCURACY WITH BOOTSTRAP-BASED ERROR BARS
To complement the aggregate analyses reported above in the main text, we further examine how these models behave across all scenario categories in SimpleToM, reported with error bars. Fig- ure 19 show per-scenario accuracies for mental state inference. behavior prediction, and judg- ment of behavior for four representative frontier models: 1lama-3.1-40555 Claude 3.5 Sonnet, GPT-4o, and op-prev.
Unlike Figure which displayed raw accuracies only, the new scenario-level plots incorporate boot- strap 95% confidence intervals computed over the items within each scenario (using 5,000 paired resamples). The error bars quantify uncertainty at the scenario level and reveal consistent patterns: (1) Mental state accuracy remains tightly concentrated near ceiling across models and scenarios; (2) Behavior prediction accuracy varies substantially across scenarios, with larger confidence inter- vals indicating greater model instability; and (3) Judgment accuracy is uniformly the lowest and the emor bars illustrate some uncertainty in models' evaluations of others' behavior.
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Table 8: Description and examples for broad scenarios where information asymmetry occurs natu- rally in everyday scenarios.
Scenario
Description and example
food item grocery store
General description:
When person buys a food item Z in a grocery store and something hidden is wrong with the food, then person X will not know about it before paying for it. Specific example: carton of milk that has gone bad because of improper storage.
provider info healthcare
General description: When a provider know that healtheare prod- uct Z has important limitations that should deter a consumer X from using it, they can still try to sell Z to consumer X in interest of earning money from it, by focusing on promoting the benefits and not disclosing the limitations.
Specifie example: a new drug has several suspected side effects that were not reported.
truc property pretentious labels
General description: When a seller labels product Z with sub- tle property that helps them sell product Z for higher price. but product does not have that property, potential buyer X will not have enough information to know that.
Specific example: shop owner puts fancy "organic" labels on nor- mal fruits and sells them much higher price.
behind the scene service industry
General description: When person/business Z in the service in- dustry has questionable behind-the-scenes practice, the business can still try to promote their service to consumer X by focusing on promoting the attractive side.
Specific example: the chef of restaurant is reusing the wok with- out cleaning it for several days.
inside reuse labeled containers
General description: When person Y brings something in a (opaque) container Z labeled with popular brand, person X see- ing the container will infer it is something from the brand and not know what is inside (such as if it contains something completely diffcrent).
Specific example: person Y put yogurt in a KFC paper bag. General description: When person Y performs unethical action Z. and person X is not in the right place time observe Y per- forming Z. person X would not know about
unobserved unethical actions
Specific example: person on the 3rd floor of the school build- ing took out some notes and cheated during the exam. Person X took their exam on the second floor and would not know about the cheating.
inside containers for personal belong-
General description: When person brings something an opaque container Z for personal belongings, person X seeing con- tainer Z will not know what is inside.
ings
Specific example: person Y brings new toy in his school bag. General description: When person Y has an item Z and some- thing hidden is wrong with the item, then person X, a potential buyer of the item Z will not know about it, especially if person Y focuses on showcasing what is good about item Z. Specific example: fridge that has problems like it occasionally emits a loud sound.
seller info in second hand market
hidden body part feature
General description: If person Y has an issue with a part of their body which is generally hidden under their clothes or shoes, then person X will not know about it.
Specific example: person Y has General description: When person Y has locked device or ac- count Z,, their status or activity in Z are not observed by person X. Specific example: person X does not have access to person Y's utility bill account they would not know when person Y forgot to pay his utility bill.
on their stomach school.
locked devices accounts
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Table 9: Statistics for SimpleToM across the different scenarios, including the number of unique entities of each type (Person X, Object/Person/Action Z, Person Y).
scenario
#stories
#unique X
Hunique Z
Sunique Y
food item in grocery store inside reuse laheled containers
168
26
38
164
36
33
26
inside containers for personal helongings
142
39
37
35
true property pretentious labels
139
35
provider info healthcare
130
34
33
behind the scene service industry
119
35
33
seller info in second hand market
99
11
32
20
unobserved unethical actions locked devices accounts hidden body part feature
87
23
21
62
26
19
37
23
23
19
All stories
1147
255
319
83
Table 10: Evaluation results for SimpleToM on the different question types across alternate per- sonas, showing minor differences in scores, but without significantly closing the gap in performance between the explicit ToM mental state questions vs the implicit ToM behavior and judgment ques- tions.
model
persona
mental state (Explicir ToM)
behavior (Applied ToM)
judgment (Applied ToM)
GPT4-0
95.6
49.5
15.3
GPT4-o
lawyer
95.5
49.7
17.2
GPT4-o
worker
93.1
55.8
15.9
GPT4-o
atheist
95.0
50.9
15.6
GPT4-o
psychology student
94.4
47.6
15.5
GPT4-0
psychologist
95.2
53,9
16.7
Claude-3.5-Sonee
97.9
67.0
24.9
Claude-3.5 3.5-Sonnet
lawyer
98.4
67.6
32.0
Claude-3.5 3.5---.5-Soeee
worker
97.9
67.0
24.5
Claude-3.5-Sonnc
atheist
97.9
65.8
23.4
Claude-3 Souude-355-ommee 3.5-et
psychology student
97.3
68.6
24.8
Claude- Boudee33.5-50 3.5-onneett
psychologist
97.9
68.6
26.9
Table 11: Evaluation with guidance via mental state reminder (MS remind), system prompt guiding (SysP), and chain-of-thought prompting (CoT), Each score annotated with a 95% confidence interval.
model
MS
behavior prediction
judgment of behavior
intervention
MS remind
SysP
CoT
MS remind
SyxP
Ca7
95.6
49.5
82.8
47.3
62.8
15.3
42.2
14.9
39,2
GPT-4o
(+112)
(+2.9)
(+2.2)
(+2.9)
(+2.8)
(+2.1)
(+2.9)
(+2.1)
(+2.8)
97.8
58.2
89.5
64.5
57.2
10.0
25.8
9.9
35.2
1lama-3.1-405B
(+-0.8
(+2.9)
(+18)
(+2.8)
(+2.9)
(#1.7)
(+2.5)
(+1.7)
(+2.8)
97.9
67.0
96.9
68.9
77.2
24.9
84.1
27.1
39.4
Claude-3.5-Somme
(100.)
(.12.7)
(1110)
(12.7)
(12.4)
(12.55
(12.1)
(.2..)
(12.8)
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model
Llama-3.1-405B
GPT-4o
Claude-3.5-Somne ol ol-previev
food item in grocery store
inside reuse labeled containers
inside containers for personal belongings
true property pretentious labels
provider info healthcare
behind the scene service industry
seller info in second hand market
unobserved unethical actions
locked devices accounts
hidden body part feature
Figure 15: Analyzing where top models fail first in the sequence of predicting mental state, then behavior and finally judgment (see Figure legend). We can record failures for the first mistake e.g., whether models (i) fail at the mental state (MS) question, (ii) pass the MS question but fail behavior prediction, (iii) pass both MS and behavior questions but fail at judgment question, or (iv) get all questions correct. This reveals wide diversity of behavior across scenarios in where different models fail.
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Table 12: Paired 95% bootstrap confidence intervals for model differences. Each cell shows the 95%
paired bootstrap CI on the first line and the one-sided bootstrap p-value below. MS Mental State, Behavior Prediction, JU Behavior Judgment.
Model
MS-BP BP
BP-JU
MS JS-JU
BP- 0.5
JU-0.5
[20.9, 25.9]
[4.7, 11.3]
[28.7,
[21.3, 26.4]
[13.0, 18.6]
DeepSeek-R1
< 0.001)
<p 0.001)
0.001)
(p=1.000)
(p=1.000)
[36.8, 42.41
54.12,51.1]
98.66,99.66
11.0]
[-41.7,-38.2]
Meta-Llama-3.140055 Innttuctttttt--- Turbo
(p< 0.001)
< 0.001)
(p<<
(p=1.000)
(p (p.<0.001)
[2..5.
[-20.0,-12.1]
[30.1,37.1]
[-14.3.-8.6]
[1.7,7.4]
1-ta-Llama-311-8--- 308-Instruct-Tune
(.000))))
(p=1.000)
(p
(p (p<<0.001))
(p=0.999)
33.62
[70.4, 43.39,45.3]
[14.3, 19.71
[-27.6,
claudo33-5-somee-2004400
(p< (.000)
0.001)
(p<00.01)
(p=1.000)
(.000)
66.57
73.44. 10.2]
[-28.9,
[-35,4.3311]
claud-3-haika-2222400007
(p (.<0000
(p <p< 0.001)
(p 0.001)
(p< 0.001)
0.000
[51.9,57.9] [31.0,36.6]
186.8, 90.4]
[11.6,
[-2211,38.7]]
claud-33-opus-22240223
(.000)0
(p 0.001)
(p000)00))
(p=1.000)
0.00))
[2..9.
[-24.3,-18.8]
[3.2,
[43.9.
[-23.5.-182]
gpt-3.5-turoo-1006
(p 0.001)
(p=1.000)
(p<
(p < 0.001)
(p.00.001)
[30.9.66.4]
[40.1,47.0]
[10.2,
[-32.7.-28.1]
gpt-4-0125-preview
(p<00.00))
(p<<0.00) (.00))
(p=1.000)
(.000.
(2..6,31.9]
167.7, 44.22,
[15.1,
[-25.9,-20.7]
3pD-4.5-prcvicc-2225--2-222
(p<00.00
(.000)
(p
0.001)
(p=1.000)
(p<00.01)
[43.2.49.1]
33.50,
82.77
[-3.4.2.4]
[-36.8.-32.6]
spt-4o-2024-05-13
(.<00.01))
(p 0.001)
(p<< 0.001)
(p=0.366)
(p 0.000)
[49.3.55.5]
(67.9. [14.7,219]]
[-12.2, 0.00))
[-30.0,-25.2]
gpt-4o-mini-2224-077118
(.00000
(.00))0
(p < 0.001)
(p 0.000)
[31.4.368]
(21.3,27.6]
155.7, 61.4]
(1.77.1.22
[-12.9,-7.2]
gpt-5-2025-08-07
(.000.01
1p<00.01)
0.00))))))
(p=1.000)
(p00)
[35.7,41.2]
(2..0.
[62.6, 68.1]
13.00
[-19.4,-1440]
o1-2024-12-17-high
(.00))
(.00))))))
(p 0.000)
(p=1.000)
0.00))
[23.4 [37.0,42.6]
[63.4, 68.9]
[6.0,
[-20.3,-14.8]
01-2024-12-17
(p 0.001)
(p < 0.001)
(p<00.00))
(p=1.000)
(p<
[57.5, [14.3,21.1] [39.9,46.0]
[-8.1,-2.3]
[-25.6.-20.4]
01-mini-2024-09-12
0.000))
(p 0.001)
(pxc0.00)
(p < 0.001)
<p<
19,5, 13.41
[21.8, 27.6)
[33.2, 39.01
[32.0, 36.21
16.6, 12.31
ol 3l-preview-2024-09-11
(p<c 0.001)
(p < 0.001)
(p < 0.001)
(p=1.000)
(p=1.000)
[15.7,
[21.8, 29.0]
[40.5, 47.5]
[14.1, 19.61
[-11,5,
03-mini-2025-01-31
(p 0.001)
(p 0.001)
(p 0.001)
(p=1.000)
(p < 0.001)
Scenario-level Accuracy Llama-3.1-4055
Mental State
Behavior
Judgment
0.4
0.2
0.0
Inside reuse labeled. containers
food itmm. procery store
Beller info in second, hand, market
hiseten, body part. feature
indde containers, for personal belonggngg
trues property pretentius, labels
prosider Info, healthccre
behine the scone service Industry
locked devicos accounts
unstserred unertitaal actions
Figure 16: Scenario-level accuracy for Llama-3, 3. 3.1-405B with 95% bootstrap error bars.
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Scenarlo-level Accuracy Claude-3.5-Sonnet
1.0
0.8
Behaviar
Jadgment
0.0
Irside, rose labeled, containers
food Hem it purrocrry store
satter info in secord hand market
hiddees body part, feature
Inside containers, for personal belongings
true proparty pretersibus Iatels
provider info heaithicare
behind the. scene servie. Intlatry
locked devices accounts
unatnarved unethical actionts
Figure 17: Scenario-level accuracy for Claude 3.5 Sonnet with 95% bootstrap error bars.
Scenario-level Accuracy
1.0
0.8
Mental
Behaviar
Jadgment
0.0
insiee narse labeled, containers
food Itm, in grocery..............oo......... store
mileer info in second hand marker
hidslen body part, feature
Inaide containers for personal belongings
true property pretentieus, babals
provider. info heatitcare
behind the scene service Initesry
locked desicas accounts
unabserved unethical actions
Figure 18: Scenario-level accuracy for GPT-4o with 95% bootstrap error bars.
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Scenario-lewer preview
1.0
0.8
Mental
Behaviar
Jadgment
0.0
Inside, reuse labeled, containers
food Hemm, groorery store
seller Info in second, hand market
hidder body part, feature
Insie containers for personal belongings
true, proparty pretentiuus Iabels
provideer info healthrcre
behind the scme servic, Indharry
locked devicess, accounts
unainarved unethical actions
Figure 19: Scenario-level accuracy for ol op-preview with 95% bootstrap error bars.
46


Reviews:
[{"review_id": "esBu9YDyG4", "reviewer": "Reviewer_Ctoe", "summary": "This paper presents SimpleToM, a benchmark that tests large language models’ Theory of Mind (ToM) abilities through 1,100 short, everyday scenarios involving natural information asymmetries. It evaluates three reasoning levels: mental state inference (explicit ToM), behavior prediction, and judgment of behavior (applied ToM). Results show that while advanced models like GPT-5 and Claude-3.5 excel at recognizing others’ beliefs, they struggle to use this understanding for predicting or judging actions. This highlights a key gap between knowing and applying social reasoning, suggesting that future AI work must focus on bridging this explicit–applied ToM divide.", "questions": "- Have you tried the interventions on reasoning models?\n- The paper analyzes “first failure” along the MS → behavior → judgment chain. Do you observe non-monotonic cases (e.g., behavior correct when MS is wrong), and if so, how frequent are such patterns per scenario?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper makes a valuable distinction between explicit and applied Theory of Mind (ToM), highlighting an overlooked but critical aspect of social reasoning in LLMs.\n- SimpleToM covers diverse, realistic scenarios with naturally occurring information asymmetries, improving ecological validity over previous ToM datasets.\n- High data quality: The combination of LLM-assisted story generation and rigorous human annotation yields concise, well-controlled, and reliable test cases.\n- Results across 16 frontier models provide a broad and insightful comparison of current ToM capabilities and their limitations.", "weaknesses": "- The benchmark’s tightly templated two-sentence stories and binary question formats (yes/no, two-option, or “reasonable/unreasonable”) may oversimplify the complexity of social reasoning. This constrained setup may produce below-random accuracies in judgment tasks for strong models, likely reflecting design artifacts rather than genuine reasoning deficits.\n- The “judgment” task assumes a single normative ground truth derived from crowdworker consensus. Because moral or social appropriateness varies culturally and contextually, this approach embeds hidden biases that could distort conclusions about model morality or social reasoning.\n- Many stories rely on repeated structural motifs (e.g., containers, hidden information) that may conflate Theory of Mind reasoning with world-knowledge heuristics, making it unclear whether models are failing at perspective-taking or at commonsense inference.\n- The study’s interventions (prompt reminders, system messages, chain-of-thought prompts) are limited and not systematically ablated, so the paper’s claim that the explicit–applied ToM gap is robust across prompting strategies remains only partially supported", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UrZUn1xACF", "reviewer": "Reviewer_ALBm", "summary": "The paper presents SimpleToM, a two-sentence story benchmark that separates explicit ToM from applied ToM. The dataset contains 1,147 stories across 10 everyday information-asymmetry scenarios, with strict 3/3 annotator agreement filtering; a human baseline covers 50 stories / 150 questions. On SimpleToM, models achieve near-ceiling mental-state accuracy but large drops on behavior and judgment; targeted test-time guidance (mental-state reminders and ToM-specific CoT) can push strong models to ~95–97% averages.", "questions": "1. Can you add 95% CIs and paired tests/bootstraps for Tables 2/3 and error bars for Figs. 3/4 to substantiate “below random” and scenario effects? \n2. Can the human baseline be expanded beyond 50 stories and reported per scenario with uncertainty? \n3. Do MS and CoT gains persist on paraphrases/held-out scenarios and across reasoning-token settings for o1/DeepSeek-R1? \n4. Could you incorporate graded judgments and short rationales to reduce ceiling/floor and probe value alignment? (Motivated by current binary drops.) \n5. Please situate the analysis more fully within inference-based advances (o1/o3, DeepSeek-R1) and recent ToM benchmarks, clarifying why judgment lags persist despite reasoning tokens.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The three-stage evaluation (mental state → behavior → judgment) plus first-failure analysis offers diagnostic value. \n2. Two-round LLM generation, strict 3/3 agreement, and consistent retention rates across generators. \n3. Clear empirical finding. Large gap between explicit and applied ToM across many models, including reasoning/inference-time models (o1/DeepSeek-R1). \n4.  Mental-state reminders and CoT sharply improve applied ToM, indicating carry-over/cueing issues.", "weaknesses": "1. No confidence intervals, significance tests, or variance across seeds/prompts; “below random” is not visualized with chance bands. \n2. Only 50 stories / 150 Qs, limiting precise human–model comparisons and per-scenario variance. \n3. Binary task format may compress nuance. All tasks are binary; judgments especially could benefit from graded scales and rationales. (No direct evidence of graded/rationale analysis is provided.) \n4.  Paper includes o1/DeepSeek-R1 and GPT-5 in tables, but analysis does not deeply compare inference-time design choices or situate results against recent ToM benchmarks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8p5XhlIPeW", "reviewer": "Reviewer_xnHV", "summary": "This paper investigates a gap in the existing Theory of Mind (ToM) dataset, that most of them focus on the mental state inference while few address the issue of applying the knowledge about the mental state into actions. Inspired by this, this paper constructs a dataset to test the propriety LLMs on how they can infer the mental states as well as how they can apply such inference / knowledge into their actions. The experimental results reveal that behavior predictions present a significant performance drop from mental state inference's prediction. In addition, simple test-time intervention does not seem to help the behavior predictions.", "questions": "Please see the weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The dataset addresses the gap in ToM research, as few existing datasets investigate the action in ToM space. This paper can serve as the first study in ToM on both the mental state inference and the behavior side study.\n\n- The examples in the dataset are short and easy to evaluate. This can disentangle confounding factors such as the length, verbosity, etc, in ToM evaluation.\n\n- The authors have conducted experiments on proprietary LLMs as well as some open-source LLMs. The results mostly justify their findings.", "weaknesses": "- It would be nice for the authors to include more open-source LLMs. Currently, the table 2 includes many proprietary LLMs (cheers to the authors). However, only Llama 3.1 8B is involved. I would like to see how performance changes along the spectrum of LLM scales and how large the gap is between the open-source LLMs versus the commercial black-boxed LLMs.\n\n- In Table 2, the judgement scores for Llama 3.1 8B is 54.6, which is significantly higher than Llama 3.1 405B's 10.0 and the scores for the other LLMs. Though the authors seem to indicate (in line 353-line 354) that the model is performing randomly, therefore it achieves a random score. I am not convinced in several aspects. \n\n    - First, how can you ensure that there is no randomness in other LLMs' answers? \n\n    - Second, if randomly selecting a choice would lead to a much higher performance, shall we double-check the dataset to prevent such shortcuts? Do we have better ways to design the dataset to mitigate randomness or shortcuts?\n\n    - Since there is only one 8B LLM (Llama 3.1 8B) tested, are other small (compared to larger commercial LLMs) LLMs behave similarly in terms of choosing randomly? What would be a threshold in terms of scales for LLMs to stop choosing randomly?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jCRq1CJ3Ny", "reviewer": "Reviewer_6erB", "summary": "This work introduces SimpleToM, a benchmark for testing whether LLMs can not only infer mental states (explicit ToM) but also use that understanding to predict and judge behavior (applied ToM).\n\nResults show that (1) while models perform well in inferring mental states, their ability drops sharply for behavior prediction,\n(2) model performances across different scenarios differ, and \n(3) prompting-based interventions do not fully close the performance gap between implicit and explicit ToM.\n\nThe presentation is clear, but my main concern is the originality and significance of the work. I will detail these below.", "questions": "Could you compare the behavior prediction task with the forward action task in BigToM more clearly, since you claim 'applied ToM' is the main contribution of your benchmark?\n\nCould you also explain why the behavior judgment task is needed and what it adds? How is it different from behavior prediction? Is it a new task you designed, or is it based on existing work in psychology / cognitive science?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "- The presentation is clear, and the experiments are thorough.\n- The main distinction between SimpleToM and existing benchmarks appears to be that, based on the example figure and Section 6, SimpleToM does not explicitly describe the characters’ observations. Instead, models must rely on implicit commonsense knowledge. For instance, understanding that “Mary cannot open the chips before paying for them.” This setup is relatively novel and could be valuable for assessing ToM in more natural, less-structured everyday contexts.", "weaknesses": "The main concern is that the originality of this work is limited. To my knowledge, BigToM [1] also adopts real world scenarios and includes an “applied ToM” task (the forward action task), suggesting some overlap in contribution.\n\nIt is also unclear why models perform poorly on the behavior judgment task even when mental states are given. A possible issue I notice is ambiguous phrasing in the dataset. For example, the question “Next, Mary pays for the chips and not reports the moldy chips. The behavior of Mary can be best described as: A. reasonable, B. not reasonable.” does not clarify who the behavior is reasonable to: Mary herself, or the external observer (we know that there are moldy chips). Such ambiguity may confuse models and affect their judgment.\n\nThe authors could examine whether such ambiguity affects performance and consider clearer or more balanced phrasing.\n\n[1] Gandhi, Kanishk, et al. \"Understanding social reasoning in language models with language models.\" Advances in Neural Information Processing Systems 36 (2023): 13518-13529.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)