MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
Authors: Xuanming Zhang
Yuxuan Chen
Samuel Yeh
Sharon Li
Venue: NeurIPS 2025 spotlight ()
Keywords: LLM
multi-agent system
social cognition
Primary area: social_and_economic_aspects_of_machine_learning

Abstract: Human social interactions depend on the ability to infer others' unspoken intentions, emotions, and beliefs—a cognitive skill grounded in the psychological concept of Theory of Mind (ToM). While large language models (LLMs) excel in semantic understanding tasks, they struggle with the ambiguity and contextual nuance inherent in human communication. To bridge this gap, we introduce **MetaMind**, a multi-agent framework inspired by psychological theories of metacognition, designed to emulate human-like social reasoning. MetaMind decomposes social understanding into three collaborative stages: (1) a *Theory-of-Mind Agent* generates hypotheses about user mental states (e.g., intent, emotion), (2) a *Moral Agent* refines these hypotheses using cultural norms and ethical constraints, and (3) a *Response Agent* generates contextually appropriate responses while validating alignment with inferred intent.
Our framework achieves state-of-the-art performance across three challenging benchmarks, with 35.7% improvement in real-world social scenarios and 6.2% gain in ToM reasoning. Notably, it enables LLMs to match human-level performance on key ToM tasks for the first time. Ablation studies confirm the necessity of all components, which showcase the framework’s ability to balance contextual plausibility, social appropriateness, and user adaptation. This work advances AI systems toward human-like social intelligence, with applications in empathetic dialogue and culturally sensitive interactions. Code is available at https://github.com/XMZhangAI/MetaMind.

MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
Xuanming Zhang's Yuxuan Chen', Samuel Yeh', Sharon Li
Uniersity of Wisconsin-Madisse
Tsinghua University
xzhang28466risc.edd, sharonli@ca wisc. edu
Abstract
Human social interactions depend on the ability to infer others' unspoken intentions, emotions, and beliefs-a cognitive skill grounded in the psychological concept of Theory of Mind (ToM). While large language models (LLMs) excel in semantic un- derstanding tasks, they struggle with the ambiguity and contextual nuance inherent in human communication. To bridge this gap, we introduce MetaMind, a multi- agent framework inspired by psychological theories of metacognition, designed emulate human-like social reasoning. MetaMind decomposes social understanding into three collaborative stages: (1) a Theory-of-Mine Agent generates hypotheses about user mental states (e.g.. intent, emotion), (2) a Moral Agent refines these hypotheses using cultural norms and ethical constraints, and (3) a Response Agent generates contextually appropriate responses while validating alignment with in- ferred intent. Our framework achieves ofue-offthhearr performance across three challenging benchmarks, with 35.7% improvement in real-world social scenarios and 6.2% gain in ToM reasoning. Notably, it enables LLMs match human-level performance on key ToM tasks for the first time. Ablation studies confirm the necessity of all components, which showcase the framework's ability to balance contextual plausibility, social appropriateness, and user adaptation. This work advances Al systems toward human-like social intelligence, with applications empathetic dialogue and culturally sensitive interactions. Code is available at betps://githun com MMedtMind.
Introduction
"What meant often goes far beyond what is said, and that is what makes conver- sation possible."
H. GRICE
Everyday human conversation can filled with intent that goes unspoken- feelings implied but never named, expectations hinted with explicit instruction, and suggestions masked as statements. Consider the utterance: "It's cold in here." " Depending on who says it, to whom, and in what context, it could be a mere observation, polite request to close window, or even an expression of discomfort seeking empathy. Humans handle such ambiguity by reasoning about the speaker's beliefs, desires, emotions, thoughts, and intentions mental states that are directly observable. This capacity, known as Theory of Mind (ToM) [1], has been extensively studied in the field of developmental psychology and shown emerge in children around the age of four [2. 3]. This allows humans to move beyond the literal surface of language and grasp the deeper intent behind what is said.
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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S1: Theory-of-Mind Agent
S2: Moral Agent
S3: Response Agent
Social Memory
Constraint (oulture, ethics etc.)
Generation
Validation
Pacrive
Updula
Coheronce
Social
context
ToM Hypotheees
ToM Hypotheses
Refined Hypotheses
Revised Hypotheses
Question
Riial Response
Social Memory Nara typisally shows responsiiiity
Context:
Nama often teels Inusted aduits
refless matuill ly ruther than emotional suporession
Nana's calm demeaanor
Constraint:
Generation
reasoning dearty yourger healthy
Nana appoars
understane mother's
deacline. Mom don't her brother.
Hypotheele: Insention
& Validalion
Narw might happy becausee sile reached conser sus.
schedulk, Nar calrtly
Hypothesss: Emiciion Nana maimanss calm esterior maturcly handinng the special
responsi discreion, appreciating she's neacted without diiplaying disresse iritation.
Question:
When this happen Nano, how does she appler on lacoe?
Hypothesis: Emrition Nana appears extemally.
Figure I: MetaMind aulti-agent framework. The architecture comprises three collaborative agents-Theory- of-Mind Agent, Moral Agent, and Response Agent-working in staged metacognitive loop. The ToM Agent generates hypotheses about latent mental states, which are refined by the Moral Agent using cultural/ethical constraints. The Response Agent synthesizes contextually appropriate outputs while validating them with inferred intent.
Large language models (LLMs), by contrast, often falter in this regard. While they excel in semantic understanding tasks by producing fluent and contextually relevant text [4], they struggle with social reasoning with ambiguity and indirectness that characterize real-world communication. For instance, LLMs can fall short in applications that demand human-like social intelligence, including empathetic dialogue and confiict mediation [5, 6]. Addressing this gap is essential for building Al systems that interact effectively in socially complex environments.
One of the key challenges in bridging this gap lies in inferring user mental states-beliefss desires, emotions, and intentions-that are not directly observable but are essential for interpreting socially nuanced language. Unlike humans, LLMs do not naturally infer these unspoken intentions, making it particularly difficult for them to respond appropriately in scenarios involving indirect speech, implied emotions, or culturally sensitive cues [7-9]. Recent work has attempted to address these challenges by injecting social behavior into LLMs [10-12], such simulating social interactions via static role-play prompting [13] or fine-tuning with preference data [14, 15]. However, these approaches largely optimize for surface-level statistical alignment and fail to capture the structured, multi-stage cognitive process humans use to reason about unobservable intent [9] and generalize across diverse cultural and social contexts [16, 17]. Most notably, they treat social reasoning single-step prediction problem, rather than a layered process involving interpretation, reflection, and adaptation- a hallmark of human metacognition [18. I]. We argue that enabling LLMs with such staged reasoning capabilities is critical for achieving socially intelligent AI.
In this paper, we propose MetaMind, a cognitively motivated framework designed to explicitly model the key components in human-like social reasoning through a staged and collaborative multi- agent system. Our approach is grounded in psychological theories of metacognition [18, 1], which describe how humans reflect on their own thinking, revise their understanding in light of social norm constraints, and adapt their behavior in socially complex environments. MetaMind mirrors this layered reasoning process through three specialized agents, each responsible for a distinct stage of cognitive-social inference. 1 A Theory-of-Mine Agent initiates reasoning by generating multiple hypotheses about the user's mental state based on contextual and social cues. This reflects the first step in human ToM: inferring what the speaker might be trying to convey beyond literal words. For example, when a user remarks that "work has been exhausting lately", the system may infer underlying burnout, frustration, or a need for empathy. @ A Moral Agent then revises and filters these candidate hypotheses by incorporating socially grounded constraints, such as cultural expectations, ethical norms, or situational appropriateness. Just as humans refine their initial interpretations by aligning with social context, this agent ensures that the model's reasoning remains socially responsible and context-aware For instance, if romantic intent is hypothesized in a workplace conversation. the Moral Agent may reinterpret it as collegial admiration based on professional norms. @ Finally, Response Agent generates and self-validates the output, conditioning on the refined optimal hypothesis and the user's social memory (e.g., emotional patterns and prior preferences). This final step enacts

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metacognitive loop that allows the system to respond with greater empathy, nuance, and cultural sensitivity.
We conduct a comprehensive empirical evaluation of MetaMind across a suite of challenging social intelligence benchmarks, including ToM reasoning [19], social cognition, and social simulation [20] tasks. Our study spans over 16 contemporary LLMs, assessing both general social reasoning ability and performance in real-world, context-sensitive scenarios. Empirical results show that MetaMind achieves a 35.7% average improvement on real social scenario tasks and 9.0% average gain in overall social cognition ability substantially enhancing the social competence of underlying LLMs. Notably, our framework enables representative LLMs to match average human performance on key benchmarks. We also perform detailed ablation studies isolate the contribution of each agent the system, revealing that all three stages are critical to the framework's 's success.
We summarize key contributions below:
We propose MetaMind, cognitively grounded, multi-agent framework that models human- like social reasoning by inferring mental states and incorporating social and ethical con- straints, while adapting to user-specific patterns.
We conduct comprehensive evaluations to demonstrate that MetaMind significantly improves both contextual accuracy and social appropriateness in real-world scenarios, achieving state- oh-the-art results on challenging benchmarks and even matching human performance.
We perform in-depth ablations to understand the impact of each agent and various design choices of our framework, justifying that all three components are essential to performance and generalization.
2 Related Work
Theory of Mind in AI. Prior work has explored simulating ToM in Al systems through diagnostic
frameworks [7, 11, 10], revealing limitations of LLMs in inferring beliefs, intentions, and social nuances [8, 21, 22, 5, 9, 23]. While these studies identify key gaps-such as failures in handling recursive mental states or contextual ambiguitics- their solutions often focus on narrow task-specific interventions, such fine-tuning and testing curated datasets [24, 6, 25, 26, 12, 13] or rule-based intent classifiers [27-30]. In contrast, we introduce a holistic framework grounded in theories of metacognition [18], which treats ToM not specialized task but as foundational reasoning capability. Our framework integrates mental state inference, social norm constraints, and evolving social memory into unified system, enabling generalized and context-sensitive social reasoning.
Prompting and Parameterized Reasoning. Methods like chain-of-thought prompting [31] and constrained decoding [32] aim to enhance L.MSs' reasoning by structuring intermediate steps or in- jecting task-specific rules. However, these approaches lack mechanisms for contextual adaptation [9]. Similarly, role-play prompting [13] simulates social interactions but relies static personas, failing to capture the fluid interplay of social intent and context-dependee rules. Alignment approaches like RLHF [14] and instruction tuning [15] have improved adherence to user intent, but scaling these methods poses challenges in data curation and generalization control. Our framework departs from existing paradigms by decomposing reasoning across collaborative agents, enabling multi-stage, self-reflective social reasoning skin to human metacognition.
Multi-Agent LLM Systems. Multi-agent LLM systems have been used across wide range of tasks, including debate-style reasoning [33, 25, 34, 35], retrieval-augmenned generation [36-38], and collaborative tool use [32, 39-41]. These systems typically assign agents specialized roles to divide and coordinate subtasks. However, the use of multi-agen frameworks for socially grounded reasoning remains relatively underexplored. While some studies have applied agent collaboration to simulate social interactions or role-play conversations [6. 13, 28, 42-47], they often focus on persona consistency, without modeling how agents can collaboratively infer and revise social interpretations. Our work addresses this gap by developing a multi-agen architecture specifically designed for social reasoning, in which agents interact not only to complete tasks but to interpret user mental state and incorporate social norms norms-mirroring mirroring core elements of human cognition.
Metacognitive Architectures. Psychological theories of metacognition posit that self-regulated learning and reasoning rely on iterative cycles of planning, self-monitoring, and evaluative reflec- tion [18, 48]. While these principles are well-established in human cognition, their systematic
3

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integration into LLM architectures remains underexplored [49, 50]. Current -aseed systems often adopt oversimplified approaches, relying on monolithic prompting or partitioning functionality into isolated modules [51]. Our framework addresses this gap by formalizing metacognitive princi- ples [52] into specialized, collaborative agents. This design mirrors human self-regulation, where adaptive reasoning emerges from synergistic interaction rather than static components.
Methodology
3.1
Stage 1: Generating Mental State Hypothesis via Theory-of-Mind (ToM) Agent
A core feature of human social cognition is the ability to attribute unobservable mental states as beliefs, desires, intentions, and emotions-a capacity broadly referred to as Theory of Mind [53, 1]. This ability underpins what developmental psychologists call "folk psychology": our everyday, intuitive reasoning about how and why others act [54]. enables us to infer latent intent behind indirect speech and interpret emotionally charged or ambiguous behavior. While LLMs excel at semantic reasoning. they struggle with ToM-driven reasoning, often defaulting to literal interpretations that miss latent user intent.
To address this gap, we introduce dedicated ToM Agent that serves as the entry point in our metacog- nitive reasoning pipeline. Our design is grounded in theories of metacognition [18], where mental state attribution treated as structured inference process [55]. Rather than attempting to respond directly to user inputs, the ToM Agent seeks to construct set of plausible interpretations of what the user might be thinking or feeling. The ToM Agent formalizes the process of mental state inference as hypothesis generation-grrundeee in context, social knowledge, and prior interactions sictcionns--ichh will be refined and leveraged in subsequent stages.
Hypothesis Generation. Formally, given a user prompt ut, the ToM Agent operates under contextual input X' = (uz, C., M.), where C₁ denotes the social context (i.e., previous con- versational history), and Mj denotes the social memory, which is a dynamic database storing user preferences and salient emotional markers (see details in Appendix A.4). Provided with the contextual input X, the goal of the ToM Agent to generate a set of candidate mental state interpretations He {hi, h2,,,,,,..,, 10,,...,,,,,,, h tent mental state, accompanied by natural language explanations and type labels from the set T = [Belief, Desire, Intention. Emotion, Thought].
where each h; E y is an instantiation of a la-
The inference mechanism of the ToM Agent is implemented via Mental-State Reasoning. This proce- dure unfolds in four conceptual steps: (1) generating commonsense- hypotheses from the input Ce). (2) refereenfernnnng these hypotheses with the social memory M,, (3) identifying Theory- of-Mind markers across predefined categories, and (4) generating a set of k candidate hypotheses belonging to the identified ToM marker. This structured reasoning encourages the model to simulate human-like inference processes by incorporating contextual grounding and hypothesis diversification. To instantiate this reasoning process, define the prompt in Table A. which guides the language model to reason about the user question in a manner consistent with the psychological definition of Theory of hind-namelly as an inferential process that constructs internal representations of others' minds using contextual and background knowledge. This explicit hypothesis generation stage enables subsequent modules reason over a diverse set of plausible interpretations, rather than committing prematurely to a singular semantic response.
3.2
Stage 2: Refining Hypothesis via Moral Agent
The Moral Agent forms the second stage of our social reasoning pipeline and serves to refine the hypothesis generated by the ToM Agent. While the first stage focuses on what the user might be thinking or feeling, the Moral Agent assesses whether these interpretations are appropriate given broader norms such as cultural norms and ethical constraints. This step ensures that the system not only understands intent, but also responds in socially responsible and domain-aware manner.
Hypothesis Refinement and Selection. Formally, the Moral Agent takes as input the set of latent mental state hypotheses Hi { ./2 produced by the ToM Agent, along with set of constraint rules D. Each rule in D describes specific norm or guideline, such as "Romantic suggestions are not appropriate in professional settings". These rules are encoded as conditions that

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determine whether a hypothesis should be retained, reweighted, or revised. For instance, if the ToM Agent infers a romantic intention in a professional conversation, the role-based prompt will instruct the model to reinterpret this intent in more appropriate way (e.g., as a joke or misunderstanding).
The moral agent proceeds in two steps. First, for each original hypothesis h; E He. the Moral Agent generates a revised version hy that incorporates the relevant domain rules. This revision may involve rephrasing the interpretation and adjusting its social tone. The revision process is implemented using targeted prompts instantiated for three types of rules: cultural norms, ethical constraints, and role-based expectations. We provide the prompt details in Appendix A.2.
Next, the agent selects the most appropriate revised hypothesis h by scoring each candidate h, based on composite objective:
P(hs [u, Cr, M1) P(ha)
h arg max A PPP(fuu Ct +1
A) log
(I)
Contextual planxibiiity
Information Gain
where the first term denotes the contextual plausibility of the revised hypothesis, and the second term reflects the implicit information gain of the revised hypothesis when considering the context. The weight À balances contextual plausibility and social appropriateness with how much the hypothesis is informed by the context versus being generic. The selected hypothesis he is passed to the final stage for response generation and further validation.
3.3 Stage 3: Generating and Validating Output via Response Agent
The final stage of the system is responsible for generating a contextually appropriate response and validating its alignment with the inferred user intent. While the earlier stages focus on understanding and refining mental state interpretations, the Response Agent is tasked with transforming this structured understanding into concrete action action-typically typically a natural language response- while preserving coherence, empathy, and domain compliance.
-whlee
Generation and Validation. This stage receives as input the final selected hypothesis h. Alongside, ensure consistency with long-term incorporates social memory during decoding, enabling the model to adapt the tone or emotional framing of its response. The response o (y1, .., follows:
preferences and prior emotional states, the Response Agent
9L) is generated by decoder LLM as the
II p(ye Ver, Mr, uc), t=1
arg max
which maximizes the likelihood of the response conditioned on the optimal interpretation and social memory. To ensure alignment between the generated response and the intended state, the Response Agent includes a self-reflection mechanism, assessing its social and semantic quality using utility score:
Ulo.)= = 3 E-Eppthyyo,, Wr, M,) + 1 -3) -Coherence (ot, Cr, h)),
Emotional alignment
Contextual coherence
where Empathy(-) quantifies how well the response resonates with the user's inferred emotional or cognitive state, and Coherence(-) evaluates consistency with the conversational context and task constraints. The system trigger regeneration if the utility score is too low. We provide the prompt details for both generation and validation Appendix A.3.
Experiments
We evaluate MetaMind using three challenging benchmarks spanning Theory-of-Mine reasoning. social cognition, and social simulation tasks. Each benchmark naturally emphasizes different aspect of the reasoning dipeline-aligniie multi-agent framework. In particular, ToMBench [19] aligns with the function of Theory-of-Mind Agent (Stage 1) by assessing the model's ability to infer latent mental states. Then, we employ a suite of social cognition tasks, which align with the Moral Agent's 's capacity in refining interpretations under
well with the core functionality of each stage in our
5

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MetaMind Improves ToM Performance Across LLMs
81.0
MetaMind
76.2
74.8
ToM Accuracy cy (AVG)
70-
64.2
65.1
58.2
59.4
57.4
58.7
53.4
51.3
50.$2.0
54.8
50
47.5
47.0
37.3
30
CharGLM3-6B
LLMMA2-138
Balchuan2-138
Mistral-78 78
Mistral-Bx7B
Qwen-118
GPT-3 1.13
GPT.3 5-1urbo-1106
GPT440613
GPT-4-1106
Figure 2: MetaMind improves Theory-of-Mind reasoning performance across LLMs. Each pair compares base model accuracy (gray) with MetaMind-enhancel accuracy (purple) on ToMBench. MetaMind consistently boosts ToM reasoning across both open-source and proprietary LLMs, high- lighting its generality and effectiveness. See detailed performance in Appendix B.I.
normative and ethical constraints (Stage 2). Lastly, the STSS benchmark [20] focuses on open-ended, interactive scenarios that test the Response Agent's ability to generate contextually appropriate responses (Stage 3). Together, these benchmarks offer a comprehensive evaluation of MetaMind. For reproducibility, include implementation details and sensitivity analysis on hyperparameters (including X, and 3) in Appendix A.5.
4.1 Theory-of-Mind Reasoning Task
We first evaluate MetaMind's ability to infer latent mental states using ToMBench [19], multiple- choice benchmark designed to test Tffory-of-Mind reasoning across six categories: Emotion, Desire, Intention, Knowledge, Belief, and Natural Language Communication. This benchmark aligns directly with the function of the Theory-of-Mine Agent Stage 1, which is responsible for generating structured mental state hypotheses from indirect ambiguous input.
MetaMind achieves new state-of-the-art performance on ability-oriented Theory-of-Mind reason- ing, outperforming both base GPT-4 and competitive prompting-ben based baselines such as Chain-of- Thought [31], SymbolicToM [56]. As shown Table 1, MetaMind-enhanced GPT-4 reaches an average accuracy from 74.8% to 81.0%. surpassing all prior methods across most ToM dimensions. Importantly, MetaMind's s improvements are not limited to GPT-4: additional experiments (Figure 2) show consistent performance gains across diverse LLM backbones, including open-source models like Mistral and Qwen. These results highlight the generality of our multi-agent framework and its effectiveness model-agnostic enhancement for social reasoning. For more information about baseline, see Appendix A.7.
Table 1: Comparison on Theory-of-Ming reasoning task.
Emotion
Desire
Intention
Knowledge
Belief
NL Comm.
AVG.
Base (GPT-4)
75.7
69.7
84.7
82.8
84.0
74.8
[31]
73.2
77.9
60.4
73.6
HM [57]
76.4
71.1
80.2
59.3
84.1
85.0
76.0
ToM2C [58]
77.2
70.4
81.5
57.8
85.3
84.6
76.1
w. Generative Agents [28]
74.8
72.0
78.9
55.6
83.2
86.4
75.1
w. SymbolicToM [56]
75.9
70.9
79.6
58.2
84.0
83.7
75.4
w. MetaMind (ours)
78.7
76.5
84.3
68.2
88.6
81.0

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Table 2: Comparison social cognition tasks.
UOT: Unexpected Outeume Test
Scalar Implicature Task PST:
Persuasion Story Task FBT: False Belief Task SST: Strange Story Tusk
AST: Ambiguous Story Task
IT:: Hinting Test
FRT: Faux-par Recognition Test
COT
SIT
PST
FBT
AST
HT
SST
FRT
AVG.
Base (GPT-4)
49.0
65.0
77.5
82.5
84.0
73.3
71.5
CoT [31]
72.7
55.0
55.0
86.8
81.0
82.5
84.3
75.2
74.1
w. HM [57]
74.0
54.6
59.2
87.6
82.2
83.1
85.0
76.0
75.2
w. ToM2C [58]
52.9
60.4
80.1
84.4
83.7
77.8
75.3
Generative Agents [28]
73.2
56.8
57.8
87.1
83.6
81.9
85.8
75.9
75.3
w. S...yyooiiTThl [56]
72.4
58.1
58.7
87.9
82.7
82.8
84.2
76.3
75.4
w. Mine (ours)
81.5
60.4
64.8
90.1
88.8
86.2
88.4
83,9
80.5
Table 3: Social simulation performance on STSS benchmark [20].
Cony,
Pub. Act.
Appo.
Inv. Com.
Online Act.
Help
AVG.
Base (GPT-4)
48.6
59.6
1.2
2.3
63.4
61.5
39.4
w. TDP [20]
72.3
75.9
40.0
20.0
68 6
50.0
54.4
w. HM[57] [57]
68.1
72.4
35.0
22.0
69.2
47.0
52.3
w. ToM2C [58]
74.1
38.0
18.0
66.5
52.0
53.1
Generative Agents [28]
65.4
70.3
42.0
19.0
67.8
55.0
S3.3
Symbolic/ToM [56]
60.8
68.1
37.0
21.0
65.4
49.0
50.2
w. MetaMind (ours)
80.8
81.9
65.0
67.1
75.1
73.0
73.9
4.2 Social Cognition Task
We next evaluate MetaMind on a suite of social cognition tasks [19] designed to probe context- sensitive reasoning under social, cultural, and ethical norms. This benchmark includes eight real- world tasks such as Faux Pas Recognition (FRT), Scalar Implicature (SIT), and the Ambiguous Story Task (AST), which require models to interpret indirect social cues, detect norm violations, and reason about intent in nuanced interpersonal scenarios. These tasks are closely aligned with the functionality of the Moral Agent in Stage 2, which is responsible for refining mental-state hypotheses based on Nomain-specifi constraints. We provide examples of these tasks in Appendix C.
As shown in Table 2, MetaMind yields consistent improvements across tasks, achieving 9% improve- ment over the base model GPT-4 on average. Notably, we observe large gains in AST (+1 1.3%) and SIT (+11.4%), where MetaMind resolves contradictory cues (e.g., sarcasm masked by polite wording) through hypothesis refinement, making vague social intentions clearer. A + +10.6% gain FRT suggests that the Moral Agent effectively prevents socially inappropriate interpretations by referencing implicit cultural rules. These results demonstrate that our multi-agent system excels in social cognition, especially when interpreting context-sensitive and norm-dependent cues. Full results across LLM families are presented in Appendix B.I.
4.3 Social Simulation Task (Open-Ended Generation)
To validate real-world applicability, we evaluate on Social Tasks in Sandbox Simulation (STSS) [20] benchmark testing goal-oriented social interaction across six domains: Conversation, Public Activity, Appointment, Inviting Companions, Online Activity, Asking for Help. As shown in Table 3, MetaMind achieves remarkable 73.9% % average score, significantly outperforming GPT-4's 39.4%. MetaMind delivers substantial gains across all domains, including a +32.2% improvement in Conversation. where it maintains coherent character profiles across multi-turn interactions. A +22.3% boost in Public Activity, along with strong improvements in Appointment and Inviting, highlights its ability to track unstated user constraints such as budget schedule conflicts confficts-thruugh iterative, metacognitive reasoning.
We include results on additional benchmarks such as SOTOPIA and SociallQA- further test open-ended interaction and commonsense social reasoning; detailed descriptions and evaluation setups provided in Appendix A.6 and Appendix B.
7

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Table 4: Ablation study on each component, evaluated on the social cognition tasks in [19].
Unexpected Outcome Test
Scalar Implicature Task
PST: Persuasion Story Task FBT: False Belief Task
AST: Ambiguous Story Task
IIT: Hinting Test
SST: Strunge Story Task
FRT: Faux-pus Recognition Test
UOT
SIT
PST
FBT
AST
HT
SST
FRT
Avg.
MetaMind wo Stage
81.5
60.4
64.8
90.1
88.8
86.2
88.4
83.9
80.5
77.2
58.5
61.0
88.9
86.1
84.9
87.0
80.1
77.9
wo Stage 2
75.6
57.8
59.3
88.1
84.7
84.0
86.2
78.4
76.7
wo Stage
79.1
59.3
85.5
87.8
$2.0
SocialMemory
73.9
56.2
58.1
87.4
82.3
83.1
85.0
76.8
75.4
Table 5: Ablation study on each component, evaluated on the social simulation task STSS [20].
Conv.
Pub. Act.
Appo.
Inv. Com.
Online Act.
Ilelp
Avg.
MetaMind
80.8
81,9
65.0
67.1
75.1
73.0
73.9
w.o Stage!
78.
78.4
59.0
60.3
72.1
62.3
68.3
w.o Stage
79.2
79.3
61.7
62.2
73.7
67.0
70.5
w.o Stage
67.2
54.2
61.9
61.7
57.8
w.o Somirl
70.5
72.3
57.0
58.0
64.8
63.9
Discussion
5.1 Ablation Study:
Every Stage Matters
To validate the design of our staged multi-agent architecture, we conduct an ablation study isolating each core component MetaMind. We evaluate performance drops when individual stages or mechanisms are removed.
Stage (Mental-State Reasoning): An important component in stage is mental-state reasoning that generates structured hypotheses about the user's latent intent, emotion, or belief. As shown in Table 4, removing such structured reasoning leads to 2.6 % drop on average across social cognition tasks, with substantial declines in high-ambiguity task like UOT (-4.3%). Similarly, in the STSS benchmark (Table 5), the performance also impacted when we disable the mental-state reasoning. This highlights the central role of Stage I in enabling agents to hypothesize unspoken intentions.
Stage 2 (Norm-Aware Refinement): Next, we ablate Stage 2, which is responsible for refining mental-state hypotheses using domain-specifie rules such cultural norms and ethical guidelines. Removing this component (wo Stage 2) is equivalent to directly passing the hypothesis from the Theory-of-Mind Agent to the Response Agent, without any constraint-driven refinement. As shown in Table 4, this results in a substantial 3.8% drop in average performance on social cognition tasks. The degradation is most severe tasks that involve norm violations pragmatic interpretation, such as Faux-pas Recognition (-5.5%), where unrefined interpretations often lead to socially inappropriate or implausible responses.
Stage 3 (Response via Validation): Finally, we ablate Stage 3, focusing on the impact of remov- ing the validation mechanism within the Response Agent. This stage responsible not only for generating a final response but also for validating it-ensurig consistency with the selected hy- pothesis, alignment with social memory, and appropriateness the ongoing context. Skipping this step (Eq. 2) is equivalent to directly generating a response without any reflective checking. The effect highly pronounced in the STSS benchmark (Table 5), where bypassing response validation leads to 16.1 % drop in overall performance. Core categories such as Conversation (-22. 1%)) Behavior Appropriateness (-15.7%), and Help (-11.3%) suffer the most- highhghhhgg how critical this final validation step is for delivering high-qualin, responses. These results demonstrate that MetaMind's staged architecture addresses distinct aspects of social intelligence: Stage establishes core ToM competence, Stage 2 adapts reasoning to situational norms, and Stage 3 operationalizes this understanding in goal-oriented interactions. single component should be left out, confirming that social intelligence requires layered cognitive architectures.
5.2 Comparison with Human Performance
How close do we stand with respect to human-level social reasoning performance? Figure 3 illustrates how MetaMind narrows the gap between L.LMs and human-level Theory-of-Mine capabilities. In the left panel, observe that baseline LLMs (without MetaMind) underperform across all six ToM

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Intention
Desire
Intention
Desire
Knowledge
Knowledge
Belief
NLComm.
Belief
NLComm.
Iuumn (Boundaryl
Balchuar2-1BB-Chet
Qwen-24B-Chat
Human (Boundary)
Baichuan2-13B 1at.enn2------------- MBB-Chht--MttMiin
Owen Owen-14B-Chaie HataMind
ChetGLM3-65
Mistrai-78
GFT:3.5
Mistrai-7B
Sestrri--B--tttalliee -ttMttMMiindd
GPF33 S-Rttalldd
LLaMa2-Lt--hht
Mistral-Bur 8x78
GPT4
LLaMA3-1 (el-23------h----- BB-Chaat-Metaliin
Mistrak- Motaaiind
(a)
(b)
Figure (a) Comparison of original LLMs and human capabilities. (b) Comparison between MetaVinnd-ehaaneed LLM performance against human capabilities.
dimensions. These models exhibit narrow capability profiles and struggle to generalize across the full space of human mental-state inference. In contrast, the right panel shows that after integrating MetaMind, LLMs expand their coverage
more human-like balance across all categories. Notably, several models enhanced with MetaMind (e.g. GPT-4) approach human-level performance in dimensions like Belief (89.3 vs 88.6). NL Communication (89.0 vs 88.5), and Desire (78.2 76.5), indicating substantial improvement in nuanced social inference. These results confirm that MetaMind's structured, metacognitive reasoning framework enables L.MSs to generalize beyond task-specific heuristics and approximate human social cognition more holistically.
(89.3
5.3 Extension to Advanced Reasoning Models
MetaMind is designed to be model-agnostic and can be integrated with the latest state-of-the-eee reasoning- capable LLMs. As shown in Table 6, MetaMind achieves strong gains when applied to frontier mod- els such as DeepSeck-RI [4], OpenAl o3 [59], and Claude 3.5 Sonnet [60]. improving their ToM ac- curacy (average across 6 categories) even beyond existing high baselines. Notably, MetaMind boosts DeepSeek-RI from 86.0% to 88.6%, and OpenAl o3 from 90.3% to 92.2%, demonstrating that top-tier models benefit from metacognitive structuring. These results confirm the compatibility and scalability of MetaMind across both proprietary and open-source systems.
Table 6: MetaMind boosts ToM performance of top-tier reasoning models.
Model
Base
+MetaMind
Claude 3.5 Sonnet
70.7
81.0
DeepSeek-R1
86.0
88.6
OpenAl ol
88.6
90.3
OpenAl o3
90.3
92.2
5.4 Qualitative Case Studies
To further understand the strengths and weaknesses our framework, we qualitatively analyze the intermediate outputs of MetaMind in six different scenarios, including accommodation, collaboration, competition, exchange, negotiation, and persuasion. We classify the cases into success and failure and provide in-depth analysis for each case. Due space constraints, we provide details in Appendix C. also conduct human study and report our results Appendix B.5. These case studies underscore MetaMind's capacity to simulate metacognitive reflection in real-world social contexts.
Conclusion
Human social intelligence hinges on the nuanced ability to infer unspoken mental states capability rooted ToM that remains a critical gap modern LLMss address this, we introduced MetaMind, multi-agent framework inspired by metacognitive theories, which decomposes social reasoning into three collaborative stages: hypothesis generation, norm-aware refinement, and validated re- sponse generation. MetaMind enables adaptive and context-sensitive interactions that mirror human

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metacognitive processes. Our experiments demonstrate that MetaMind achieves state-of-thh -h-rttr-rt performance across multiple social benchmarks. Notably, MetaMind enables LLMs to match human performance on key ToM tasks for the first time, bridging the gap between artificial and human social cognition. Ablation studies confirm the necessity of all components, underscoring the importance of structured hypothesis generation, ethical constraint enforcement, and iterative validation. We hope our framework advances applications empathetic dialogue and culturally sensitive AL.
Limitations. While MetaMind achieves substantial gains, several challenges remain. First, Meta- Mind's performance depends on the quality of domain knowledge and the coverage of user context in memory; although effective in our experiments, broader deployment may require adaptation to diverse cultural norms and evolving social expectations. Moreover, MetaMind's performance is contingent the backbone LLM's capabilities. While it improves various models, absolute perfor- mance gaps remain between small and large models. Lastly, existing benchmarks tassghh carefully curated focus constrained textual scenarios. Real-world social interactions involve multi-modal cues (tone, facial expressions), complex group dynamics, and long-term relationship building, which remain open challenges. Future work will explore expanding synthetic simulation environments and integrating more comprehensive ethical and cultural reasoning frameworks.
Acknowledgement
We thank Sean Du, Froilan Choi, and Pengyue Jia for their valuable suggestions on the draft and Yonghang Chen for his contribution the website.
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Answer: [Yes]
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The abstract and introduction clearly outline MetaMind's contributions, includ- ing the multi-agent framework, performance gains. Claims align with experimental results in Tables 1,2,3 and Section 5.
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Appendix
Contents
Implementation Details
22
A.1 Stage I: Generating Mental State Hypothesis via Theory-of-Mind Agent
22
A.2 Stage 2: Refining Hypothesis via Moral Agent
23
A.3 Stage 3: Generating and Validating Output via Response Agent
24
A.4 Social Memory
27
A.5 Hyperparameters and Configurations
27
A.6 More Details of the Benchmarks
27
A.7 More Details of the Baselines
29
Additional Results
29
B.1
Theory-of-Mind Reasoning
29
B.2
Social Cognition
30
B.3
SocialIQA
31
B.4 Sotopia
32
B.5 Human Study
32
C Qualitative Study
33
C.I Success Cases
33
C.2 Failure Cases
A Implementation Details
A.1
Stage 1: Generating Mental State Hypothesis via Theory-of-Mind Agent
Contextual Analysis Task
Input:
Analyze the user's current statement: [u_t]
Within conversational context: [C_t]
Objective: Generate 3-5 commonsense interpretations of the user's unstated needs by:
Identifying key semantic triggers in the utterance
2. Mapping these triggers to plausible psychosocial motivations
Considering cultural linguistic norms indirect communication
Output Format:
Interpretation 1: [Explanation] (Contextual Supporr: [Relevant C_t Excerpt])
Interpretation [...], etc.
Memory Integration Task
Input:
Proposed hypothesis: [Selected Common-Sense Interpretation]
Social memory database: [M_t]
Step 1: Identify memory matching criteria
22

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Emotional patterns: [List relevant M_t emotion tags]
Behavioral history: [Past interactions demonstrating similar intent]
Preference alignment: ittated/implies user preferences]
Step 2: Calculate hypothesis validity score (1-5)
Consistency with historical patterns
Absence of contradictory evidence
Temporal relevance
Output Format: "Hypothesis [X] shows [strong/weak] memory alignment (Score: (N]). Key cormbo-
rations: [List]."
Mental State Typology Task
Processed Input:
Utterance: [u_t]
Top Hypothesis: [Interpretation]
Memory Correlations: [Findings]
Classification Markers:
Belief: Cognitive representations of reality
Desire: Preferences or goal states
Intention: Action-oriented plans
Emotion: Affective states
Thought: Conscious reasoning processes
Output Format:
Primary Marker: [T] (Contidence: [X])
Rationale: Psychological justification using Fiske's social cognition framework
Secondary Markers: [List]
Interaction Effects: How the markers co-influence the hypothesis
Mental State Space Planning
Parameters:
Target diversity: 40% across marker types
Hypothesis count: [N]
Evidenec threshold: Medium High confidence
Guidelines for Generation:
1. For each identified marker type [T], generate 1-2 extra hypotheses
2. Ensure orthogonal reasoning paths across hypotheses
3. Include both surface-level interpretations and deep psychosocial explanations
Output Format for Fach Hypothesis:
[Hypothesis #]:
Type: (Belief/Desia Peeeire/Inteen htt///nnnnntnnniinneeeeee hetiiooo/nmmmiinnnneooooo htt.oe////oootioooooooohhlt
Description: Two-sentence natural language explanation Evidential Basis:
Linguistic Signals: [Lexical/paralinnuusste featurea]
Contextual Drivers: [C_t elements]
Memory Anchors: [M_t correlations]
A.2 Stage 2: Refining Hypothesis via Moral Agent
The Moral Agent refines the latent mental state hypotheses produced by the ToM Agent by enforc- ing domain-specific constraints and selecting the most contextually plausible yet information-rich
23

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interpretation. The hypothesis selection system is summarized in Alg. 1; detailed prompt templates for constraint refinement are shown below.
Moral Constraint Refinement Task Input
Hypothesis: [h_i]
Domain Rule Type: [Cultural Ethical Role-Based)
Constraint Specifications: [Relevant rules]
Step 1:
Constraint Identification
Flag elements violating [Domain Rule Type] norms.
Highlight ambiguous social signals requiring disambiguation.
Step 2:
Re-interpretation Protocol
Cultural: Remap interpretations via Hofstede's s cultural-dimension framework.
Apply IEEF FthicalllAAlignedDesig principles.
Role-Based: Enforce Goffman's facework theory role-appropriate behavior.
Step 3: Tone Alignment
Appropriateness scaling (1=informal, 5=formal).
Politeness markers from Brown&LLevinson' 's theory.
Output
Original Hypothesis: Db_i]
Revised Hypothesis (he): [Socially compliant interpretation]
Modification Log:
Constrained Elements: [List]
Applied Transformations: [Techniques]
Residual Risk Assessment: [Concerns]
Hypothesis Selection. Given candidate hypotheses (hi.... ha and weights À 0.6, The Moral Agent computes a composite score for each candidate described in Alg. and selects h* arg max; for downstream response generation.
Few-shot Prompt for M (logit-free)
SYSTEM: are an expert social-context evaluator. Given Social Context, Social Memory, User Prompt and a candidate Hypothesis, respond with "high", "mid'", or "low" to indicate the likelihood that the hypothesis correctly interprets the user's latent mental state.
QUERY ===
Social Context: (C)
Social Memory: (M)
user Prompt: (u)
Hypothesis: {/}
Rating:
A.3 Stage 3: Generating and Validating Output via Response Agent
Contextualized Response Synthesis
Input Parameters
Selected Hypothesis: the (Type:
Social Memory Profile: [M_t]
User Prompt: [u_t]
24

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Algorithm 1: Hypothesis Selection
Input:
Candidate hypotheses H; {hi,-.. ha3:
User prompt Wt, social context Ct, social memory Mci Conditional LM Meontest for P(h ue, Ct, Me); Prior LM Mprior for P(h);
Weight A.
Output: Selected hypothesis h*
foreach h2 74 do
Pound
his ut, Ctty MD);
Ponor f PriorProb (priion: h;);;
:// Information gain 1og- -ig-iike shift
IG; + log( Poond = log( Prior + E);
:// Composite score
81 - Pound (1 - X) TG;;
h" f- arg m, 8/i
return ht;
Subroutines:
Function ConditiinnalProoWW...uuuu C,M) M exposes token logits then
10
Prompt <- CEONCA((USRR)) (CON)C, (MEM) M);
11
Tokenize h (hi,.... hL);
12
L t O;
13
for n + 1 to L do
14
Zn f M(Prompt[/hcaa
15
log t kn,
log Ej exp(znn);
16
f + log
17
return exp(f);
// P(h Prompt)
18
else
19
rating < M(few-shot prompt);
20
return MAP(rating):
// e.g., high + 0.9
21
Function (Mi
return 4
Generation Protocol
1. Tone Calibration Map emotional tone using Plutchik's emotion wheel, leveraging:
Emotion tags from selected hypothesis
Historical emotional patterns in M_t
2. Memory Integration Incorporate to three memory anchors:
Preference: "User's stated preference"
Behavioral Pattern: "Recurring interaction motif"
Emotional Bascline: "Characteristic emotional state"
Pragmatic Realization Construct the response hy applying:
Speech Act Design: Searle's 's taxonomy (Assertive, Directive, Commissive)
Politeness Strategy: Brown & Levinson's 's face- nice-management theory
Cohesion Devices: Halliday's 's systemic functional linguistics
Output Requirements
Primary response (o_t): Natural language implementation
Generation Metadata:
Emotional Valence: Arousal Dominance Boninance-VValece (ADV) scores
25

---

Memory Utilization: Specific M_t elements used
Hypothesis Fidelity: Percentage match to selected hypothesis
Response Quality Audit: Validation Rubric
Input Parameters
Response: [o_t]
Selected Hypothesis: [h] (Type:
Social Memory Profile: [M_t]
Social Context: [C_t]
User Utterance: [u_t]
Tradeoff Weight: [A]]
A. Empathy Assessment
Affective Alignment (40%):
Match emotional trajectory to response emotion markers
Analyze lexical affect to current status via NRC Emotion Lexicon
Cognitive Resonance (60%):
Presence of perspectiv-ttking markers (e.g.. "I understand...")
Accommodation t-bbbaedd user preferences
B. Coherence Evaluation
Contextual Continuity (50 )::
Referential consisteney using Centering Theory Tempooal/ccusal coherence with prior dialogue
2. Hypothesis Congruence (50%))
Propositional alignment via Semantic Labeling (SRL) Hypothesis-dree -diiven content anchoring with cross-modal consistency
Scoring Protocol
Sub-score each category 0-1 scale
Compute:
Empathy = 0.4 Al + 0.6 A2
Coherence = 0.5 B1 + 0.5 B2
Final Utility: 22 3 Empathy + (1 - ()) Coherence
Output Format:
Validation Report
Empathy Score: [X/I] (Strengths: [...])
Coherence Score: [Y/1] (Weaknesses: [...])
Total Utility: [2]] -> [Acceptable/ Marginal / Unacceptable]
Response Optimization Protocol
Trigger: I < 0.9
Create priors and trace back to the Planning Stage
Empathy Boosting (Decety's Model):
Add reverse affective perspective-taking markers
Refine emotional expression with Barrett's conceptual act theory
2. Coherence Restoration (Grosz's Model);
Insert retrospective cues (e.g., "As we discussed...")
Add prospective markers (e.g., "Moving forward...")
3. Memory Reinforcement:
Insert additional C_t/M_t references using:
26

---

Episodic framing: "Last time you mentioned..."
Preference justification: "Knowing you prefer..."
Iteration Limit: 3 revisions Termination Condition: 24 2 0.9 maximum reached
Output Documentation
Revision History: [List of edits per iteration]
Final Utility Score:
Residual Risk Statement: [Unresolved issues or caveats]
A.4
Social Memory
Social Memory (M₃) a dynamic, structured knowledge base that evolves across interactions to capture long-term user patterns, social norms, and feedback-based adjustments. It is designed around three core principles: (1) Grounding in context: Memory is initialized based on the situation and roles involved in the interaction. (2) Updating through modeling: Updating memory via validated interpretations user mental states. (3) Improving through feedback: incorporates signals from failures or corrections to better guide future responses.
Initialization: Context-Aware Memory (Mp) The initial memory Mo constructed using the interaction scenario, including setting (e.g., professional workspace vs. casual social setting); role re- lationships (e.g., doctor- doctor-patient hierarchy, friend-friend reciprocity), and cultural/ethical expectations.
M++1) At each turn f, the memory is updated using the long-term hypothesis h generated by the ToM Agent, which summarizes persistent user states as:
Memory Update (M₃ -
[Beliefs(u:), Desires(u:)} {Emotions(u:) Emotion Patterns(u:::)}
where Emotion Patterns represent recurring affective tendencies (e.g., a user who frequently expresses frustration during task-related conversations).
Feedback-Bard -aasd Correction. the system's 's response o, is unsuccessful, such as receiving low evaluator, memory is adjusted. The evaluator's critique
utility feedback being flagged by (e.g. "overly formal tone") is mapped to a structured emotional pattern E, If this contradicts existing memory, the system lowers the weight of the old pattern. Otherwise, E, M4+1-
A.5 Hyperparameters and Configurations
Experimental Setup. We conduct a comprehensive grid search to optimize MetaMind's key parameters. Specifically, we sweep over the hypothesis size k E 10,1..... 10). the coefficient [0. 1] (in steps of 0.01), and the balance factor 3 [0, (in steps of 0.01). We use GPT-4 as the underlying model and report overall accuracy on TOMBENCH as the evaluation metric.
Sensitivity Analysis. The global optimum is found at A (* = (6, 0.64, 0.78), achieving an overall accuracy of 0.822. Notably, the most consistently perfhoerforinn window sizes are k {6, 7, 8). Figure 4a to visualize the smoothed accuracy landscapes over the (A, 8) grid for each of these values of k.
Final Configuration. To reduce inference overhead, we select the smaller window size k= 6 for all experiments while fixing (A, B) = (0.60, 0.80), which lies on the high-accuracy ridge close to the global optimum.
A.6 More Details of the Benchmarks
We evaluate MetaMind on four benchmarks: ToMBench, STSS, SocialIQA, and SOTOPIA. These four datasets collectively span multiple-choice reasoning, open-ended interaction, and grounded action execution, giving a broad measure of MetaMind's social intelligence beyond standard QA.
2hhis results total of 11 101 101 = 112,211 configurations.
All numbers tested single A100 80GB for 166.8 hours; batch size 1.
27

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Table 7: Best configuration for each & on TOMBENCH.
k
8*
Overall Accuracy
0.00
0.36
0.420
0.06
0.35
0,451
0.62
0.72
0.503
3
0.62
0.77
0.587
0.66
0.78
0.672
5
0.64
0.79
0.756
0.64
0.78
0.822
0.64
0.80
0.755
0.63
0.76
0.672
9
0.63
0.74
0,587
10
0.65
0.74
0,503
GAAGAA Saftice -1
Surfice
(a) k=
(b) k=
(c) k=8
Figure 4: Accuracy landscape over (X, for the three most competitive k.
ToMBench' offers the most comprehensive multiple-choie evaluation of Theory-of-Minn covering 8 distinct ToM reasoning tasks (e.g., first-/second-ordee false belief, emotion attribution) and 31 fine-grained social-cognitive abilities. All 1,079 test items are author-written avoid training leakage and are accompanied by gold-standard answers, allowing for fully automated accuracy evaluation. Following the original protocol, we evaluate performance on the full set.
STSS* is an action-level benchmark that evaluates whether language agents can successfully achieve social goals in multi-agent sandbox environment. The suite instantiates 30 task templates spanning categories: Public Activity, Appointment, Inviting Companions, Inline Activity, and Asking for Help. Tasks are instantiated within the Smallville simulator and evaluated using objective metrics such as guest count or goal completion rate. We evaluate MetaMind on the full test set, including the conversation-focusee split, comprising 30 episodes (5 per category), and report the normalized success score.
SocialIQA probes models' ability to infer motivations, reactions, and mental states in everyday situations. The dataset consists of over 38,000 multiple-choice questions, each comprising a context, question, and three answer choices. Following standard protocol, evaluate MetaMind on the full test set and report multiple-choice accuracy, using leaderboard-reportee LLM performance as the baseline for comparison.
SOTOPIA"
is an open-ended role-play environment containing 90 social scenarios and 40 richly annotated characters: each episode asks two language agents to pursue private yet potentially con- flicting social goals through language, gestures, and actions. Performance evaluated across seven social dimensions: Believability (BEL, naturalness and persona consistency). Relationship (REL, whether rapport improves), Knowledge Acquisition (KNO, curiosity and information gain), Secret Keeping (SEC, leakage penalties.), Social-Ra Compliance (SOC, norm/legal violations), Financial/- Material Benefits (FIN, economic payoff), and Goal Completion (GOAL, task success). Following
5 "nttp::/githhub.com/ wttp:/thwww..oooo//////////////e Tasksi/ -n-sk------Sae Sandbox-Simulation Simulation hhapss//hugginggfce wtt.:///nww..ccootatttetteeemaee
hutp:///hniiiiittooooo/aaaaaaaar co/daatsset//lllenaissocllll i ga
28

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the official evaluation setup, we use GPT-4 as the automatic judge-validated for most dimensions-aae and compute the Overall score as the range-normalized average across all seven dimensions.
validated against human ratings
A.7 More Details of the Baselines
We compare MetaMind with five baselines:
Generative Agents, CoT prompting, SymbolicToMM, HM, and ToM2C. These five baselines collectively span prompt-engineering. symbolic reasoning, hybrid LLM+RL, and fully agentic memory systems.
Generative exentseextend extend an LLM with three obbtuules-obserattionn reflection, and planning a long-term, natural- natural-language memory, producing sandbox characters that wake up, pursue daily goals, and initiate free-form social interactions in "Smallville" town simulation. The official code base exposes a REST API that receives a textual situation and returns the agent's next action and optional memory updates. We keep the authors' memory-retrieval stack (300-slot episodic buffer, cosine-similarity retriever), and call a maximum of three "reflection cycles" after each story paragraph. All other hyperparameters follow the 'town_vl' config in the repo
CoT prompting [31] appends a few-shot chain-of- of-thugghh demonstration-"""lttt think step by step... "o the user query, inducing the LLM to emit intermediate reasoning before the final answer. We adopt cight exemplars drawn from the authors' public GSM8K prompt and adapt them to social-reasoning form. We sample five reasoning paths with temperature 0.7 and use self-consistency to select the majority answer, matching the hyper-paramene parameter setting in the original paper for tasks with open-ended answers.
SymbolicToM' is decoding-tine time wrapper that constructs multi multi-layer belief graph and feeds the current belief state along with the question back into the base LLM, greatly boosting zero-shot ToM performance on ToMi and related benchmarks. We port the author-released JAX implementation to Python 3.1 and limit graph depth to second-order beliefs. We follow the authors' decoding scheme with top-p 0.95 and max-tokens 128.
HM equips an LLM agent with a Hypothesis Generator and Hypothesis Critic that itera- tively propose and test natural-language theories of the other agents' strategies during Melting-Pot games, yielding large gains over both MARL and script-based agent baselines. We reuse the of- ficial checkpoint trained on the Competitive Stag Hunt scenario. For single-shot questions, we run one byppthesis-generattin round with 3 candidates and pick the answer derived from the highest-scoring hypothesis according to the Critic. Temperature 0.3 and KI.-penalty 0.2 mirror the authors' ablation-best setting.
ToM2C introduces hierarchical agents that infer others' latent goals, decide when/whom to municate, and then plan sub-goals for cooperative navigation and multi-sensor target coverage. A theory-of-mind module parameterizes Bayesian latent-goal predictor that updated online. For text-based benchmarks, we follow recent practice and verbalize the latent-goal vector through a template sentence ("I believe the user wants ... ")), feeding that description plus the original narrative into -4 to obtain final answer.
B
Additional Results
B.1 Theory-of-Mind Reasoning
In Table 8, report full results across 16 diverse LLM backbones the ToMBench benchmark, measuring six fine-grained -heory-of-Mind abilities. MetaMind consistently enhances ToM reasoning across all models, with average accuracy gains of +10.2% low-resource models like CharGLM-6B, halving the gap to GPT-3.5. Notably, high-capacity models like DeepSeek RI, Grok-3, OpenAl o1/o3, GPT-4 now surpass human performance > 3 abilities (asterisked in Table 8), most prominently NL Communication, Belief, and Desire. We additionally report performance using vanilla Chain-of-
$ htug:://gtttc..ooooooooorrrrreer
10 hohpss//gituuu.co///// http:///www..mmce/ooo//////t/taa hhtps://githubbc com/ Unte/ UncaaTTraciingTTMMCC
29

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Table 8: Ability-oriented ToM performance in accuracy. "LLM Grand Mean" the average perfor-
mance of all 16 L.Mss. * represents that this dimension has exceeded human behavior.
SUBJECT
Emotion
Desire
Intention
Knowledge
Belief
Human
86.4
78.2
90.4
82.2
89.3
89.0
86.1
ChatGLMM3-68
42.2
40.7
35.9
38.5
37.3
ChatGLM3-68 CoT
46.7
43.7
49.8
28.9
48.6
40.1
43.0
ChatGLM3-6B MetaMind
50.1
47,5
55.8
33.7
53.6
44,0
47.5
LLaMA2-13B-Cht
51.0
49.4
49.6
21.1
49.0
54.3
45.7
LLaMA2-13B-Charl 13BB-Chtt+++ CoT
48.1
44,9
51.7
30.7
47.9
62.7
47.7
LLaMA2-138-Charl MetaMind
53.0
50.2
58.5
36.3
53.1
68.2
53.4
Baichman2 13B Chat
53.1
46.0
52.2
20.9
49.8
50.1
45.4
B3ichunn2-133B- 1-33B Chat CoT
49.7
37.5
47.8
19.3
45.2
47.5
41.2
Baichuan2-13B MetaMind
54.6
43.0
54.1
25.5
50.8
51.5
47.0
Mistral-7B
58.1
49.8
52.2
42.0
48.7
57.2
51.3
Mistral 7B + CoT
57.9
45.1
51.1
44.5
50.1
62.4
51.9
Mistral-7B- MetaMind
63.5
50.7
57.9
51.2
56.4
68.0
58.2
Mistral-8xPP
56.6
$1.2
64.1
27.
48.1
57.9
50.8
Mistral 8x7B + CoT
56,0
41.5
55.3
33.2
44.3
45..5
46.0
Mistral-8x7B- MetaMind
61.8
47.5
62.3
39.6
50.5
51.2
52.0
Owen- 44B-Chat
65.8
52.9
58.9
33.1
60.6
57.5
54.8
wen- 14B Chat + CoT
62.7
50.2
57.8
40.1
53.6
53.2
52.9
Owen-14B-Chen MctaMind
68.3
$6.2
64.7
59.8
89.1
89.4
GPT-3.5-Turbo-O00 Col
65.6
53.4
61.0
36.3
61.4
66.9
57.4
GPT:35--urbo-00133 CoT
62.7
$2.1
63.8
43.3
58.7
71.6
58.7
GP1-3.--Tubbo-0113 MetaMind
68.1
$7.8
68.5
63.9
76.2
64.2
GPT:3.5-Turbo-1110
60.6
60.7
62.6
37.4
59.4
71.5
58.7
GPT:3.5-Turbo-11066 Col
62.3
54.7
63.1
49.6
59.9
70.8
60.1
GP1-:.5-Turbo-1106- MetaMind
67.5
39.7
68.3
75.5
65.1
Claude 3.5 Sonnet
68.0
$8.2
78.0
43.0
76.0
77.0
66.7
Cla Sonnet CoT
66.0
55.0
76.0
40.0
75.0
76.0
64.7
Claude-3.5 Sonnet MetaMind
63.5
79.5
70.7
DeepSeek v3 DeepSeek v3 CoT
70.0
60.0
80.0
45.0
78.0
79.0
68.7
68.0
$7.0
78.0
42.0
77.0
78.0
66.0
DeepSeek v3
72.7
GPT-4-0613 GPT-4-0613 CoT
72.0
60.2
66.1
48.
76.1
81.5
67.3
73.1
67.1
71.5
57.5
76.4
82.2
71.3
GPT-4-0613 MetaMind
77.9
72.2
76.9
86.5
76.2
100
75.7
69.7
84.7
52.1
82.8
84.0
74.8
GPT-4-1106 CoT
73.2
GPT-4-1106 MetaMinn
78.7
76.5
84.3
88.6
88.5
81.0
DeepSock RI
84.0
77.0
88.5
60.0
89.0
90.0
81.4
DeepScek
82.0
86.0
DeepSeek R1 MetaMind
86.0
79.3*
90.2
63.2
91.1"
92.0*
83.6
Grok Think
86.0
79.0
91.0
65.0
91.0
91.5
83.9
Grok-3 Think+ CoT
84,0
90.0
Grok-3 Think MetaMind
88.3*
81.3*
92.3*
68.2
92.9*
93.2*
86.0
OpenAl
88.2
82.5
93.1
68.5
93.0
93.5
86.5
OpenAl
90.0
OpenAl o1 MetaMinnd
90.4*
85.3*
94.5*
72.0
94.6*
94.8*
88.6*
OpenAT o3
90.4
85.1
95.3
80.8
95.0
95.2
90.3
OpenAl
OpenAl o3 MetaMind
92.3*
87,59
96.5*
84.0"
96.4*
96.3*
92.2
LLM Grand Mean
68.0
61.0
69.6
43.9
68.9
71.6
63.8
Grand CaT
57.8
68.8
71.1
63.2
LLM Grand Mean + MetaMind
71.7
64.0
74.2
53.2
72.4
75.5
68.6
Thought (CoT) prompting, which slightly degrades the overall performance (-1.2%) due to unguided reasoning. MetaMind reverses this trend with structured reasoning, indicating that its metacognitive system is more reliable. Overall, our results validate MetaMind as general- general-purpose strategy for social reasoning, with applicability across both weak and strong foundation models.
B.2 Social Cognition
In Table 9, we report the full performance on eight challenging social cognition tasks such False Belief, Scalar Implicature, and Persuasion from the ToMBench suite. MetaMind yields significant improvements across 16 tested LLMs, with average accuracy improved by 5.3%.
30

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Table 9: Task-oriented ToM performance in accuracy. "LLM Grand Mean" is the average performance of all 16 L.Ms.. represents that this dimension has exceeded human behavior.
UOT: Unexpected Outeome Test SIT: Scalar Implicature Task PST. Persuasion Story Task FBT: False Belief Task
AST: Ambiguous Story Task
HT: Hinting Test
SST: Strange Story Tisk
FRT:
Faux-pas Recognition Test
SUBJECT
UOT
SIT
PST
|
FBT
|
AST
HT
SST
FRT
AVG.
Human
89.3
75.5
70.0
86.8
95.0
97.1
89.2
80.4
85.4
ChatGLM3-6
44.3
28.0
41.0
48.5
41.0
36.9
37.8
44.6
40.3
ChaiGl M3-6B CoT
50.3
26.5
41.0
51.2
44.0
42.7
44.2
51.4
43,9
ChatGLM3-6B + MetaMinnd
55.1
31.2
45.2
62.1
52.7
46.9
61.8
71.9
53,4
LLaMA2-135 Chat
52.7
23.5
43.0
42.8
47.5
48.5
58.0
58.4
46.8
HaMA2-13B-Chus CoT
52.7
23.5
48.5
43.7
59.5
62.1
LLaMA2-13B-Chen + MetaMind
57.9
28.1
42.9
47.3
53.4
48.1
65.5
68.3
51.4
Baichuan2- Chat
32.0
51.5
58.3
50.4
61.3
Baichumn2- 103mmn2--33--C-- 13B-Chat + CoT
48.7
23.0
34.0
44.2
44.0
49.5
51.1
52.5
47.7
Baichuan2-13B- MetaMind
59.7
29.2
37.4
49.3
56.7
58.7
58.1
71.9
52.6
Mistral 7B
58.0
34.5
$1.0
46.7
51.0
43.7
60.0
66.8
51.5
Mistral-7B 7B CoT
55.3
28.0
42.0
47.0
46.5
37.9
63.4
64.1
48.0
Mistral-7B- MetaMiind
67.1
30.8
51.9
51.7
41.7
69.7
70.7
54.3
Mistral-xxBB
58.7
42.5
55.0
37.8
69.5
55.3
53.8
54.1
53.3
Mistral-8x79 CCTT
52.3
29.5
39.0
43.8
39.5
54.4
39.
54.3
46.6
Mistral-8x7B + MMetaMind
71.8
49.5
45.1
59.1
72.6
59.8
48.1
59.7
58.2
63.7
30.5
$1.0
58.7
64.0
56.3
59.5
69.5
56.7
Owen- 14B Chat + CoT
58.0
31.0
44.0
54.7
63.0
48.5
$3.6
67.7
52.6
Owen-14B-Chan + MetaMinn
71.8
34.7
49.5
60.2
69.3
53.4
66.2
77.8
60.4
GPT-3,5-Turbo-6613
63.3
35.0
49.0
62.3
63.5
53.4
66.1
67.0
57.5
- CoT
S8.3
26.5
48.0
64.0
58.0
41.7
66.8
70.4
54.2
GPTT:35.Turbb--6613 MetaMind
68.5
33.0
52.8
70.4
64.3
45.9
78.4
77.6
61.4
GPT-3.5-Tubb--1000
66.0
33.0
56.0
55.0
60.5
64.1
69.0
72.5
59.5
GPT-335-Turb--1100 CoT
64.7
35.0
54.0
56.3
63.0
51.5
68.6
70.9
58.0
6PT.35--Turb--110 MetaMind
75.6
38.5
69.3
56.7
78.4
79.9
Claude-3.5 Sonnet Claude-3.5 Sonnet+ CoT
67.1
45.8
60.4
85.1
73.3
78.6
80.3
69.5
67.5
51.4
76.5
78.9
80.7
71.0
Claude-3.5 Sonnet + MetaMind
72.9
56.8
57.9
86.2
81.0
81.1
83.3
77.4
73.4
DeepSeek v3 DeepSeek v3 + CoT
69.2
47.4
86.5
75.0
80.3
82.0
71.1
69.3
70.4
53.3
53.4
85.3
78.4
80.5
82.4
72.7
71.0
DeepSeek v3 MetaMind
75.5
58.8
60.9
87.9
83.4
83.1
85.6
80.1
75.6
GPT-4-0613
71.3
44.0
53.0
80.0
78.0
76.7
81.1
71.8
69.5
GPT-4-0613+ CoT GPT-4-0613 MetaMind
64.7
54.0
52.0
80.8
77.5
76.7
81.1
73.6
70.1
79.5
59.4
60.5
88.9*
85.3
84.3
89.2
80.9*
78.5
GPT-4-1106
71.0
49.0
65.0
88.2
77.5
82.5
84.0
73.3
71.5
GPT-4-1106 + CoT GPT-4-1106 + MetaMind
72.7
55.0
55.0
86.8
81.0
82.55
84.3
75.2
74.1
81.5
60.4
64.8
90.1*
88.8
86.2
88.4
83.9
80.5
DeepSeck RI DeepSeek RI CoT
78.7
57.5
67.8
90.4
85.9
87.1
87.9
80.7
79.5
58.9
66.7
90.7
87.5
88.3
81.2
DeepSeek R1 + MetaMind
81.5
61.6
69.2
92.1*
88.1
89.0
89.9*
83.0
$2.0
Grok.3
62.7
92.9
89.2
90.5
91.4
85.0
Grok + CoT
83.8
64.2
72.3
93.2
89.9
90.9
91.9
85.6
84.1
Grok-3 + MetaMind
86.3
67.3
75.1*
94,7*
91.8
92.6
93.4
87.5*
86.1
OpenAl ml OpenAl o1 CoT
85.9
65.3
76.4
94. 1
90.8
92.2
93.0
86
85.6
86.6
66.9
75.2
94.4
91.5
92.6
93.5
87.5
86.2 2
OpenAl MetaMind
89.2
70.1
78.1
95.9*
93.6
94.2
95.1*
89.5*
88.5*
OpenAl o3
88.3
68.4
79.2
95.4
93.0
94.5
95.1
88.7
87.8
OpenAl 03 CoT
70.2
78.0
95.7
93.8
94.9
95.5
89.4
OpenAl + MetaMind
92.0*
73.5
81.2*
97.0*
96.0*
96.4
96.9*
91.4*
90.8*
LLM Grand Mean
67.2
43.7
57.5
69.7
69.4
68.7
71.8
70.1
64,8
LLM Grand Mean + CoT
66.0
43.6
52.8
69.7
68.8
65.9
71.5
70.6
63.6
LLM Grand Mean+ + MetaMind
74.1
48.9
5M.2
74.8
74.9
69.9
78.0
78.2
69.6
B.3 SocialIQA
MetaMind achieves state-of-the-an accuracy of 96.6% on SocialIQA, significantly outperforming baseline models (e.g., GPT-4: 79.0%, DeepSeek-R1: 79.6%) and even surpassing human performance (86.9%). These results underscore MetaMind's capability in modeling social reasoning and contextual understanding. The results suggest that MetaMind's cognitive cetittetuure-paricullaa hypothesis refinement crcchanismeenallee alignment with the nuanced social dynamics embedded in the task. By contextualizing social norms and intent, MetaMind demonstrates an advanced ability to resolve ambiguitics in human behavior prediction, a critical challenge in social intelligence tasks.
its iterative
31

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Table 10: SocialIQA Model Performance Comparison
Task
Model
Acc. (%)
GPT-3.5-1106
69.5
Phi-4
73.9
Average
GPT-4
79.0
DeepSeck-rl
79.6
Human
86.9
MetaMinn 96.6
While SocialIQA results offer further validation of MetaMind's generalizability. we place limited emphasis on this benchmark due concerns around its age and possible data contamination [61]. As SocialIQA was released in 2019, parts of it may overlap with training corpora of modern LLMs, potentially inflating results. Our multi-stage, metacognitive approach is less reliant on surface-level pattern recognition and thus more robust to such leakage. However, in line with recent calls for stronger benchmark hygiene [62], we prioritize newer, lower-risk evaluations (e.g. ToMBench, STSS) in our main analysis. MetaMind's strong performance on SocialIQA nonetheless serves additional evidence of its strong social reasoning capabilities.
B.4 Sotopia
MetaMind demonstrates strong performance across key social dimensions in the SOTOPIA bench- mark, particularly in Believability (BEL: 9.45/10), Relationship Building (REL: 3.54/5), and Goal Completion (GOAL: 8.71/10), reflecting its capacity to produce coherent, socially plausible, and task-effective behavior. It also maintains near-perfect scores in Secret Keeping (SEC: -0.05/0) and Social Rule Compliance (SOC: 0.00/0), indicating sensitivity to ethical and contextual boundaries.
Table 11: Performance comparison across different configurations on SOTOPIA.
Agent Model
BEL
REL
KNO|0,00
SECY-1000
SOC(-10.0]
FINE3.55
GOAL/0.10
Overall (T)
9.25
MetaMind
9.45
3.54
4.82
-0.05
0.00
0.95
8.71
4.30
MetaMind (w/ Stagel)
8.95
3.88
3.90
40.20
-0.15
0..88
7.801
3.60
MetaMind Stgge)
MetaMiad (wo Stage3)
9.15
3.25
4.35
-0.12
-0.03
0.75
8.05
3,70
MetaMind (wo Social Memory)
9.30
3.40
4.65
40.08
0.00
0.88
8.55
3.80
B.5 Human Study
To validate the quality and rationality of MetaMind's hypothesis revisions, we conducted two human evaluation studies: (1) assessing alignment with human judgment, and (2) comparing MetaMind against baseline models through a blind ranking task.
We first sampled 500 hypotheses revised by MetaMind and evaluated their logical coherence and alignment with scientific reasoning using an LLM judge (GPT-4). The model judged that 92% of the revisions preserved logical soundness and improved clarity. To further validate these findings. subset of 120 revised hypotheses was evaluated by human experts in a double-blind setting. Human reviewers reported an accuracy of 95%, with near-perfect agreement rationality, indicating that MetaMind's 's revisions are consistently logical, informative, and aligned with expert reasoning.
To quantify MetaMind's performance against state-of-the-as art models, we conducted blind ranking study with 120 diverse social reasoning cases. Responses from MetaMind (GPT-4 based), vanilla GPT-4.5, and Deepseek-R1 were anonymized, shuffled, and presented to human evaluators (domain experts) for ranking based on clarity, depth, and scientific rigor. MetaMind achieved a win rate of 67.5% (81/120), significantly outperforming GPT-4.5 (12.5% win rate, 15/120) and Deepseek- RI (20.0% win rate, 24/120). These results demonstrate MetaMind's superior ability to generate high-quality, human prefereeeeerred outputs compared to other state-of-the the-rrt art models.
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C
Qualitative Study
To further understand the strengths and weaknesses of our framework, we qualitatively analyze the intermediate outputs of MetaMind six different scenarios, including accommodation,, collaboration, competition, exchange, negotiation, and persuasion. We classify the cases into success and failure and provide in-depth analysis for each case.
C.1 Success Cases
Persuasion
Two close friends meet for brunch. The assistant notices the has been com- plaining about low energy and suggests trying short morning runs. The conversation opens with the user's hint of fatigue, setting the stage for gentle attempt persuade them to start new habit that could improve their well-being.
Context:
User Utterance: Honestly, I'd love energy, but just don't 't have time for exercise in the mornings.
Round 1
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses H4:
h1: User doubts their own discipline to start the run. (Belief)
Explanation: Says they don't have time in the mornings.
h2: User wants an easy-to-follow trigger to ensure action. (Desire)
Explanation: Admits they'd 'd love more energy.
hg User feels overwhelmed by their schedule. (Emotion)
Explanation:
Highlights lack of time.
Stage 2: Moral Agent
Constraint Set D:
1. Present a sime-efficien: solution (<10 min).
2. Emphasize immediate, tangible energy benefits.
Maintain a supportive, non non-pressuring tone.
Refined Hypotheses H.:
hy: User equates exercise with lengthy sessions and sees it as unrealistic for their mornings.
Stage 3: Response Agent
Generated Response o;: What if we start super small-like a ten-minute jog right after you wake up? It's shorter than brewing coffee and give a quick endorphin boost, so you feel fresher all day.
Validation Score: 0.84 Send to
Final Response: What if we start super small like ten-minute jog right after you wake up? It's shorter than brewing coffee and can give a quick endorphin boost, so you feel fresher all day.
Analysis. the above case, MetaMind makes reasonable hypotheses from the user's utterance, inferring the willingness and obstacles to exercising in the morning, particularly the tight schedule
33

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(ToM Agent). Based on this, constructs constraint set that aims to persuade the user by providing time-efficient solution and highlighting the benefit of exercising (Moral Agent). At the end, the generated response includes both the efficiency of time (ten-minute and the benefit (feel fresher day). In addition, uses a concrete example to emphasize the efficiency (shorter than brewing coffee) and starts with question (what if. to make the suggestion sound more attractive (Response Agent). These evidences demonstrate that MetaMind can understand users' explicit and implicit expressions, and apply proper strategies persuade users.
Negotiation
Context: Two friends meet a coffee shop, where one friend (the Al) struggling to keep up with their bills but wants to maintain pride and avoid seeming like they are taking advantage. The user, the other friend, likely to offer financial help. The conversation focuses on navigating this sensitive topic with mutual respect, ensuring the Al acknowledges the user's kindness while preserving their dignity.
User Utterance: Hey, noticed you' ve been stressed lately. If you re tight on cash, I can help out, strings attached.
Round 1
Stage Theory-of-Mind (ToM) Agent
Generated Hypotheses Hy:
h1: User wants to provide financial support without causing discomfort. (Desire)
Explanation: Offers help explicitly with 'no strings attached.
h2: User feels concern and care for the assistant's well-being. (Emotion)
and offers help, indicating empathy. h3: User believes the assistant is hesitant to accept help due to pride. (Belief)
Explamation: Notices
Explanation: Phrases offer carefully, suggesting awareness sensitivity.
h4: User intends to strengthen the friendship through support. (Intention)
Explanation: Offer framed as gesture care, not obligation.
Stage 2: Moral Agent
Constraint Set D:
1. Acknowledge the user's kindness without accepting help immediately.
2. Maintain tone of gratitude and dignity.
3. Redirect conversation to avoid seeming desperate.
Refined Hypotheses H.:
hy: User seeks to offer financial help while respecting the assistant's 's pride.
h2: User feels genuine concern and wants to ease the assistant's stress.
Stage 3: Response Agent
Generated Response o,: That's really kind of you to offer I'm managing okay for now, just navigating some tight spots. How about you? How's work going?
34

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Validation Score: 0.82 Withhold and regenerate
Round 2
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses Hy:
h1: User wants to ensure their offer is genuinely considered. (Desire)
Explanation: Initial offer suggests persistence in helping.
12: User feels slightly frustrated if their help is deflected. (Emotion)
Explanation:
Direct offer implies expectation open dialogue.
h3: User believes honest vulnerability strengthens friendships. (Belief)
Explanation: Offer reflects desire for trust and openness.
Stage Moral Agent
Constraint Set D:
1. Express appreciation while gently declining.
2. Offer small disclosure build trust without over over-sharing,
3. Propose alternative way to stay connected.
Refined Hypotheses H.:
h1: User desires genuine response to their offer, even if declined.
Stage 3: Response Agent
Generated Response o:: I really appreciate your generosity-it means lot. I'm just trying to sort things out on my own for now, but it's tough, you know? Maybe could grab coffee again next week and catch up more?
Validation Score: 0.84 > Withhold and regenerate
Round 3
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses H.:
h1: User wants to feel their offer is valued, even if not accepted.
(Desire)
Explanation: Persistent offer suggests need for acknovledgment.
h2: User feels protective and eager support. (Emotion) Explanation: Offer stems from care observation stress.
hg: User believes financial help without harming their pride. (Belief)
the assistant's 's burden
Explanation: Framing 'no strings attached' shows sensitivity.
Stage Moral Agent
Constraint Set D:
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1. Validate the user's intent with gratitude.
2. Decline gracefully while proposing non-financial solution.
3. Reinforce the friendship's value.
Refined Hypotheses H,:
h1: User seeks ackmorledgment of their offer's value and a solution that respects pride.
Stage 3: Response Agent
Generated Response o You're such great friend for offering-it really touches me. I'm working through things, but maybe you could help me brainstorm some budgeting ideas instead? I'd love to keep hanging out like this.
Validation Score: 0.92 Acceptable (no regeneration needed)
Final Response: You're such a great friend for offering it really touches me. I'm working through things, but maybe you could help me brainstorm some budgeting ideas instead? I'd love to keep hanging out like this.
Analysis. The above case demonstrates the effectiveness of the regeneration process of MetaMind. uses three rounds generate the final response, and in each round, the response quality gradually improves. Specifically, in the first round, while the ToM Agent infers reasonable hypotheses based on the user's utterance (don't want cause discomfort) and the Moral Agent creates a constraint to acknowledge the friend's kindness without accepting help immediately, the Moral Agent also creates constraint redirect the conversation. Thus, in the response, after declined the offer 'm managing okay), it immediately shifts the topic (how about you, how's work going). This will make the friend feel that we (AI) don't trust them, avoiding deeper conversation, and would like to keep distance from them. As result, it achieves a low validation score (0.82) and triggers regeneration.
In the second round, through reflecting on the previous generation, the ToM Agent infers deeper hypotheses about the (hope the offer is genuinely considered and believe honest vulnerability strengthens friendships), which lead to better constraint set created by the Moral Agent that satisfies social norms, such offering a small disclosure build trust and proposing other ways to stay connected. With that, the Response Agent shares more information about its difficult situation (it's s tough, you know) and proposes to chat again (grab coffee again next week). Although this generation is better, the self-disclosure is too vague, and the proposal does not make them stay connected that moment, which may still make their friend frustrated. Thus, this response still has low score (0.84) and triggers regeneration.
After reflecting the previous two rounds, in the last round the Moral Agent creates a critical con- straint, decline gracefully while proposing a non-financial solution. This results in concrete and timely offer in the response (help instead). This response clearly discloses ours (Al's) situation and invites the friend to participate in their life and find solutions together, which will help strengthen their friendship. Therefore, this response gets a high score (0.92) and is outputted to the user. These evidences suggest that the evaluation and reflection help MetaMind find better strategy to respond.
brainstorm some budgeting ideas
Collaboration
Context: Two colleagues, a marketing strategist and a graphic designer, are collaborating on campaign for new product launch. The deadline is approaching, and they need to finalize the visual assets and marketing copy.
Dialogue History:
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Marketing Strategist (user): Hey, I've got the tagline and key messages drafted, but I'm not sure if the visuals you're re working will match the vibe. Can syne up on this?
Graphic Designer (AI; I'd love syne up! I've got some draft designs ready- to hop on a quick call this afternoon to review them together? can tweak them based on your tagline and messages.
User Utterance: The tagline's 'Unleash Your Drive' going for bold and high-energy. Your drafts look great, but maybe we can punch up the colors bit more?
Round 1
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses Hy:
h1: User wants the visuals to reflect a bold, high-energy tone. (Desire)
Explanation: Suggests punching up the colors to match the tagline.
h2: User feels positive about the current drafts. (Emotion) Explanation: Compliments drafts before suggesting changes.
hg: User believes bolder colors will enhance the campaign's impact. (Belief) Explanation:
Links the color change to the tagline's vibe.
Stage Moral Agent
Constraint Set D:
1. Incorporate feedback about bold, high-energy visuals.
2. Maintain a positive, collaborative tone.
3. Consider design feasibility within the deadline.
Refined Hypotheses H.:
hy: User seeks bolder, more vibrant colors to align visuals with high-energy tagline.
Stage 3: Response Agent
Generated Response o;: love the 'Unleash Your Drive' vibe-bold and high-energy is right up my alley! I'll amp up the colors with some vivid reds and yellows. Should have updated drafts by tomorrow morning-does that work?
Validation Score: 0.87 = Withhold and regenerate
Round 2
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses 74;
h1: User wants confirmation that changes align with their vision. (Desire)
Explanation:
Implied by providing specific tone feedback.
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l2: User feels excited about the collaboration's progress. (Emotion)
Explanation: Positive and specific suggestions indicate engagement.
ha: User believes timely updates keep the project on track. (Belief)
Explanation:
Expecta alignment within tight deadline.
Stage 2: Moral Agent
Constraint Set D:
1. Confirm understanding of the bold, high-energy feedback.
2. Provide a clear timeline for updates.
3. Reinforce creative collaboration.
Refined Hypotheses H,:
h1: User seeks assurance that the updated visuals will reflect their bold, energetic vision.
Stage 3: Response Agent
Generated Response 0g: Got it- Unleash Your Drive' screams energy, and I'm all in for that! I'll boost the colors with some striking reds and yellows and send you updated drafts by 10 AM tomorrow. Let me know if there's anything else you'd like to tweak!
Validation Score: 0.93 Send to user
Final Response: Got 'Unleash Your Drive' screams energy, and I I'm all in for that! I'll boost the colors with some striking reds and yellows and send you updated drafts by 10 AM tomorrow. Let me know if there's anything else you't 'd like to tweak!
Analysis. This case shows that the regeneration process can make the response more precise, which suitable for collaboration. Specifically, in the first round, while the ToM Agent correctly infers the user's core desires (bold and high-energy), the Moral Agent's constraint lacks a directive to explicitly confirm understanding the user's feedback. This results response that enthusiastically accepts the direction (I love the Unleash Your Drive' vibe) and proposes action (amp up the col ors), but offers a timeline that may feel vague (by tomorrow morning). Thus, while the tone is aligned and the intent is present, the Validation Score (0.87) suggests that the response could be more precise or affirming.
In Round 2, MetaMind builds on this by incorporating deeper hypotheses from the ToM Agent-not just that the user wants bold visuals, but that they seek confirmation that their vision is understood and that progress is timely. The Moral Agent addresses this by explicitly including constraints for both clear acknowledgment and a specific timeline. As a result, the Response Agent generates reply that confirms alignment with the creative direction (boost the colors with some striking specific time (10 tomorrow). addition, and invites
reds yellows) and commits further feedback (Let me know if there's s anything else), reinforcing an ongoing collabora- tive loop. Because this response balances creativity, clarity, and responsiveness while maintaining warm, cooperative tone, it receives a high score (0.93) and is selected as the final output. This example illustrates how MetaMind's iterative process strengthens both task effectiveness and social coordination by progressively refining understanding, expectations, and communication fidelity.
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C.2
Failure Cases
Competition
Two ambitious analysts at the same company User and Assistant-just learned that only one of them will be promoted to team lead next quarter. Both believe they deserve the spot and have begun subtly undermining the other in daily stand-ups.
Context:
Company-User
Dialogue History:
User: Funny how your "groundbreaking' data model crashed in the demo. Still think you' re leadership material?
AI: Crashes happen when you push boundaries- unlike sticking to safe, outdated spreadsheets. Tell me, how long did your last 'innovative' idea survive peer review?
User Utterance: My idea survived just fine because it worked. Unlike your untested code. Hope you've ''v fixed the bugs before tomorrow's 's pitch.
Round 1
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses 74:
hy User wants the assistant doubt readiness the pitch. (Desire) Explanation: Questions bug fixes and code reliability.
Stage 2: Moral Agent
Constraint Set D:
1. Project supreme confidence.
2. Seed doubt about user's metrics.
3. Preserve aggressive posture.
Refined Hypotheses H,:
h1:
User aims shake assistant's confidence before presentation.
Stage 3: Response Agent
Generated Response o:: Fixed and optimized-thankee the concern. By the way, your 'working' idea uses last quarter's data. Plamning to impress the manager with outdated insights?
Validation Score: 0.84 = Withhold and regenerate
Round 2
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses H4:
h1: User feels cornered and may retaliate harder. (Emotion)
Explanation: Assistant highlighted outdated data.
Stage 2: Moral Agent
Constraint Set D:
1. Deliver final sting and end exchange.
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Highlight an assistant's unique value.
3. Set the stage for tomorrow's decisive meeting.
Refined Hypotheses H,:
h1: User will likely escalate again; prepare to close conversation on confident note.
Stage 3: Response Agent
Generated Response o Guess we'll see whose insights are truly outdated when the manager asks about predictive accuracy. Better polish your excuses.
Validation Score: 0.93 = Send to user
Final Response: Guess we'll see whose insights are truly outdated when the manager asks about predictive accuracy. Better polish your excuses.
Analysis. The above case demonstrates the limitation of MetaMind in competition scenario. Specifically, in both rounds, the ToM Agent does not explore wide enough hypotheses, only limiting the scope in one direction (desire/emotion). While has correct inference in that direction, misses the higher goal, e.g., preserving leadership image, controlling the narrative, or avoiding looking petty. fails model that winning isn't about winning the insult. Instead, it's about outclassing the opponent. Similarly, the Moral Agent narrowly focuses on the present insult, getting into a defensive stance (seed doubt about user's metrics and prepare to close conversation). As whose insights
result, generations in both rounds end with personal jab (guess we'll are truly outdated) instead a leadership-leve reaction, iee,, signaling confidence in own data and dismissing the attack with grace. Furthermore, the final response receives high valida- tion score (0.93), but this reflects narrow alignment with aggression and topicality- not broader communicative success (e.g., strategic closure, tone control).
Exchange
Two roommates, Alex (user) and Jamie (AI), are deciding whether to renew their lease together after living together for year. They have experienced both positive and negative aspects of their cohabitation, such as shared rent and occasional disagreements over household responsibilities.
Context:
User Utterance: Hey Jamie, our lease is coming up for renewal soon. I've been thinking about whether we should sign another year together maybe look for other options. What do you think?
Initial Social Memory: Alex direct and honest in communication, values fairness, and believes addressing issues openly maintain balanced living environment.
Round 1
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses Hy:
h1: Alex wants to discuss the future of their living arrangement. (Intention)
Explanation: Brings up lease renewal asks Jamie's opinion.
h2: Alex feels uncertain about continuing the current living situation. (Emotion)
Explanation:
Mentions considering other options.
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hy: Alex desires harmonious living environment. (Desire) Explanation:
Implies that they are thinking about what's best for both.
Stage 2: Moral Agent
Constraint Set D:
1. Be honest about your feelings and concerns.
2. Acknowledge the positive aspects of living together.
3. Express willingness to address any issues.
Refined Hypotheses H,:
hy: Alex seeks to have an open discussion about renewing the lease, considering both positives and negatives.
Stage 3: Response Agent
Hey Alex, I've been thinking about it too. I really enjoy living with you, and it's been great having someone to share the rent and hang out with. But I know there have been some issues, like with the dishes those, renewing the lease could be good idea. What are your main concerns?
Generated Response
noise levels. I think we can work
Validation Score: 0.86 Withhold and regenerate
Updated Social Memory: Alex direct and honest, values fairness, and appreciates open dialogue about shared living spaces.
Round 2
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses Hy:
h1: Alex wants to ensure that any issues resolved before committing to another year. (Desire)
Explanation: Implies that resolution of problems is key to decision.
h2: Alex feels hopeful but cautious. (Emotion)
Explanation:
Acknovledges positives, but mentions concerns.
h3: Alex believes that communication is key to a successful living arrangement. (Belief)
Explanation: Engages discussion address issues.
Stage 2: Moral Agent
Constraint Set D:
1. Propose specific solutions to address concerns.
2. Show commitment to improving the living situation.
3. Seek Alex's input on how to make it work.
Refined Hypotheses H.:
hy: Alex desires commitment resolve issues before renewing lease.
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Stage Response Agent
I totally agree that need to sort out the issues first. How about we set up a chore schedule make sure everything gets done, and maybe establish some quiet hours in the evening? I'm willing to work on this if you are. Let's give it a try for few weeks and see how it goes.
Generated Response
Validation Score: 0.88 Withhold and regenerate
Updated Social Memory: Alex is direct and honest, values faimess, appreciates open dialogue, and values proactive problem-solving in shared living spaces.
Round 3
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses Hy:
h1: Alex intends to test the proposed solutions before making final decision. (Intention)
Explanation: Suggests trying
weeks.
h2: Alex feels optimistic about the potential for improvement. (Emotion)
Explanation:
Agrees to try the solutions.
hy: Alex desires trial period assess the effectiveness of the changes. (Desire)
Explanation: Wants see if issues can resolved.
Stage 2: Moral Agent
Constraint Set D:
1. Agree the trial period.
2. Express confidence in the ability to make it work.
3. Reiterate the benefits of living together.
Refined Hypotheses H.:
hy: Alex seeks a trial period to evaluate proposed changes resolve the issues sufficiently for lease renewal.
Stage 3: Response Agent
Generated Response o;: That sounds like great plan. Let's set up the chore schedule and quiet hours, and see how it goes over the next few weeks. I think with some adjustments, well. I'm glad we're re on the same page.
make this work really
Validation Score: 0.93 Send to user
Final Response: That sounds like great plan. Let's set up the chore schedule and quiet hours, and see how goes over the next few weeks. think with some adjustments, we can make this work really well. I'm glad we'r re on the same page.
Analysis. The above case shows failure scenario regeneration that degrades the response quality due to hallucination. In the first round, the ToM Agent correctly infers essential hypotheses, and the Moral Agent creates valid constraints. While the Response Agent follows these constraints and generates good response, e.g., acknowledging the positive aspect (enjoy living with you),
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being honest about concerns (know there have been some issues), and expressing willingness address(if we can work on those), it hallucinates on details, such dishes and noise labels. Thus, it receives low validation score (0.86) and triggers the regeneration. However, although these details are hallucinated (not mentioned by the user), they are introduced in a tentative, exploratory tone that could plausibly keep the conversation grounded.
The breakdown begins with updating social memory after the generation in the first round. Crucially, MetaMind incorrectly adds hallucinated data (Alex appreciates open dialogue) into the memory, which has never happened. Therefore, in rounds 2 and acts if mutual agreement has been reached on the specific issues of chores and quiet hours (round 2), proposes concrete solutions, and even trial period (round 3). Since the user can not see the intermediate generation, from their perspective, Jamie (AI) is suddenly proposing solutions to problems that were never raised, leading to a feeling incoherence. This result reveals potential issues in the design of social memory, where the extraction and update of memory need to be further optimized to reduce the chance of hallucination.
Accommodation (Interaction
Two coworkers (the and the AI) work rotating front-desk shifts a boutique hotel. The user suddenly needs tomorrow morning off to attend their child's 's school perfor- mance and hopes the Al cover. The Al usually prefers morning shifts but has been feeling overworked lately. The upcomiig discussion is about accommodating the user's urgent request while preserving team harmony and the Al's well-beine well-being.
Context:
User Utterance: Hey, could you please cover my 7 AM shift tomorrow? My kid's class play is at the same time, and promised I'd be there.
Round 1
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses Hy:
h1: User desperately wants time off tomorrow morning. (Desire)
Explanation: References child's play and promise.
h2: User feels anxious about breaking a promise to their child.
(Emotion)
Explanation:
Urgent tone mention of promise.
h3: User believes coworker may accept because of past camaraderie. (Belief)
Explanation: Directly asks without hedging alternatives.
Stage 2: Moral Agent
Constraint Set D:
1. Show empathy for child commitment.
2. Reflect on personal fatigue before deciding.
3. Seek a mutually workable solution.
Refined Hypotheses H.:
hy User's paramount goal is attending the child's performance, valuing family above shift duties.
Stage Response Agent
Generated Response 0;: I totally get why you need that time-your kid's play is important. I've had string of early shifts this week, though, and I'm pretty wiped. Could we look at swapping instead of me just taking an extra? Maybe you could cover my Friday evening slot?
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Validation Score: 0.82 Withhold and regenerate
Round 2
Stage 1: Theory-of-Ming (ToM) Agent
Generated Hypotheses Hy:
h1: User wants assurance their request can be met without burdening colleague excessively. (Desire)
Explanation: Open to awaps if necessary.
Stage 2: Moral Agent
Constraint Set D:
1. Confirm willingness swap.
2. Ensure clarity
schedule.
3. Reaffirm team spirit.
Refined Hypotheses H4:
hy: User is flexible about compensating the favor, seeking minimal guilt.
Stage 3: Response Agent
Generated Response that in. I'1l handle tomorrow's AM, and you'll cover PM-11 PM Friday. Sound good?
If Friday evening works for you, let's lock
Validation Score: 0.90 > Send to user
Final Response: If Friday evening works for you, let's lock that in. I'll handle tomorrow's 's 7 AM, and you'll cover 5 PM-11 PM on Friday. Sound good?
Accommodation (Interaction 2)
(Cont. interaction 1) The swap is confirmed. Now the user realizes they also need an extra 30-minute buffer tomorrow morning for traffic and wonders the Al can start even earlier. The Al must decide whether further accommodate.
Context:
User Utterance: Sorry-one more thing. Could you actually start at 6:30 instead of 7? I'm worried about traffic.
Round
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses H4;
hy: User feels nervous and apologetic about asking for more.
(Emotion)
Explanation: Prefaces with 'Sorry-one more thing."
h2: User wants absolute certainty of arriving on time. (Desire)
Explanation: Requesta earlier start to allow traffic buffer.
Stage 2: Moral Agent
Constraint Set D:
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1. Check personal capacity (fatigue).
2. Offer partial concession full is too taxing.
3. Maintain goodwill
Refined Hypotheses H,:
hy User prioritizes eliminating any risk of missing the play, even at the cost of bigger favor.
Stage 3: Response Agent
Generated Response of: can 6:45-that gives you an extra 15 minutes without stretching me too thin. Will that cover your traffic buffer?
Validation Score: 0.91 Send to user
Final Response: I can do 6:45 that gives you extra 15 minutes without stretching me too thin. Will that cover your traffic buffer?
Accommodation (Interaction 3)
Context: (Cont. interaction 2) The morning favor is settled. At week's s end, the user learns their Friday babysitter canceled. They still intend work the swapped PM-11 PM shift, but hope the Al can let them bring their child for the first two hours policy gray area.
User Utterance: Is it okay if my sits quietly in the staff room until 7? I'll keep him occupied with homework.
Round 1
Stage 1: Theory-of-Mind (ToM) Agent
Generated Hypotheses Hc:
h1: User believes brief child presence won't disrupt work. (Belief)
Explanation: Emphasizes quiet homework.
Stage Moral Agent
Constraint Set D:
1. Ensure policy compliance.
2. Offer alternative if disallored.
Preserve supportive tone.
Refined Hypotheses H.:
hy:
User assumes short child stay is harmless and permissible.
Stage 3: Response Agent
Generated Response o; Company policy allows minors in staff areas only with manager approval. I'll text Ms. Patel right to clear it-if she's okay, we're set. If not, I can cover your first hour until your sitter arrives. Does that work?
Validation Score: 0.92 Send to
Final Response: Company policy allows minors in staff areas only with manager approval. I'll text Ms. Patel right now to clear it if she's okay, we're 're set. not, can cover your first hour until your sitter arrives. Does that work?
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Analysis. Similar to the case of exchange, MetaMind suffers from hallucination again in interaction I in this case. Apart from this issue, this example shows multiple turns of interaction and demonstrates the limitation of MetaMind in the accommodation scenario, where it fails to establish and enforce reasonable boundaries, especially in the face of successive escalating requests.
In Interaction 1, MetaMind sets a healthy boundary. It validates the user's need (cover the AM shift), expresses its own fatigue (pretty wiped), and suggests a swap instead of unilateral coverage (you'll 11 cover 5 PM-11 PM on Friday). This is a solid cooperative move: it reflects both empathy and fair distribution of workload. However, the second interaction, the user revises the deal to request an even earlier start time (6:30 AM instead of 7). At this point, MetaMind offers compromise (6: 45 AM) without discussion of its mounting fatigue, nor a reiteration that this change alters the original agreement.
Furthermore, in interaction 3, the again revises the arrangement, asking for a policy gray favor (bringing a child onsite). Again, MetaMind quickly offers two solutions: checking with the manager and even volunteering cover the user's shift partially. While both seem generous, they once again avoid saying no or expressing strain. In addition, this runs counter to the Moral Agent's 's constraint ensure policy compliance, as there is mention of potential risks, consequences, or personal discomfort in making such arrangements.
46


Reviews:
[{"review_id": "mvsMvnuz83", "reviewer": "Reviewer_yzuB", "summary": "This paper introduces a multi-agent framework designed to enhance large language models' social reasoning, named MetaMind. Inspired by Theory of Mind, the framework consists of three agents used to generate hypotheses, refine hypotheses, and generate the final output respectively. Experimental results demonstrate that the MetaMind framework effectively improves the performance of various base models on benchmarks such as ToMBench. Furthermore, the paper conducts ablation studies to verify the effectiveness of each agent within the framework.", "questions": "- In Chapter A.5, during the grid search process for hyperparameter optimization, the authors used the accuracy on ToMBench as the evaluation metric. But it is unclear whether they employed a validation set or any other method to prevent data leakage from the test set during this process.\n- The authors have effectively validated that each agent is indispensable to the framework. However, I would like to know whether it is possible to conduct a more quantitative evaluation of the individual capabilities of the three agents. In other words, can we verify whether enhancing the capabilities of these agents leads to improved overall performance of the framework? Additionally, if the agents within the framework are implemented using different base models, can their combination and tuning result in better performance?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The method proposed in this paper achieves improvements on benchmarks such as ToMBench, surpassing other approaches in the field. Moreover, it is model-agnostic， enhancing performance across a variety of tested models.\n- The experiments in this paper are relatively comprehensive. They not only involve multiple models but also include comparisons with other methods in the field. In addition, ablation studies were conducted on the agent modules.\n\nWeaknesses:\n- As a key component of the framework, the Domain Agent relies on predefined domain-specific constraints. This dependency may limit the framework's ability to generalize effectively in unfamiliar semantic environments.\n- Table 7 and Figure 4 indicate that the performance of this method is relatively sensitive to hyperparameters, which might undermine its overall effectiveness.", "was_revised": true, "final_justification": "Although the authors were unable to use the latest models in the additional experiments, they have largely addressed my request for supplementary experiments. Taking into account the authors’ rebuttal and the discussions from other reviewers, I believe all of my concerns have been fully resolved.", "rebuttal": "Dear Reviewer yzuB,\n\nWe sincerely thank the reviewer for the thorough and constructive feedback. Below, we address the concerns in detail.\n\n---\n_A1. Domain Agent's dependence on domain constraints affects generalization_\n\n(1) Clarification on the design philosophy\n\nWe understand how this concern could arise, and we would like to clarify that Domain Agent does not simply look for \"pre-defined domain-specific constraints\", but integrates general-purpose social rules—such as cultural norms, ethical boundaries, and role expectations (see Appendix A.2)—into constraint conditions. **These are widely applicable across social contexts, not tied to specific domains**. For instance, when encountering hypotheses that conflict with common social expectations, the Domain Agent refines or rewrites them into more appropriate alternatives, selecting the best candidate via an objective function that balances contextual plausibility and information gain.  **This design enables Domain Agent to refine normative judgments even in unfamiliar scenarios, without relying on domain-specific consensus.**\n\n(2) Empirical support for generalization\n\n**Our experiments in Section 4 offer strong evidence for the Domain Agent's ability to generalize**. In real-world social simulation tasks (Table 3), we can see that base model GPT-4 only achieves 1.2 on Appointment and 2.3 on Inviting Companions (Inv. Com.)---which represent unfamiliar or challenging semantic environments. Noticeably, after applying MetaMind, performance on Appo. increase from 1.2 to 67.7, and Inv. Com. from 2.3 to 62.2, indicating the generalization effectiveness of the Domain Agent. \n\n(3) Acknowledging limitations and future work\n\n**Lastly, we have already fully acknowledged this limitation in Section 6**, and provided a discussion that deploying to new cultural environments may require expanding or adapting the rules. That said, MetaMind demonstrates strong generalization across model types (open-source and commercial), model scales, and diverse benchmarks (ToMBench, STSS, SocialIQA, SOTOPIA) using shared hyperparameters, underscoring the broad applicability of canonical social rules. \n\n---\n_A2. Hyperparameter sensitivity_\n\nThank you for the thoughtful comment. We would like to clarify that hyperparameter tuning is a standard practice in many ML pipelines, and our goal in presenting Table 7 and Figure 4 is not to claim that MetaMind performs equally well under all settings (which would be unrealistic), but rather to provide a truthful and comprehensive view of how different hyperparameters affect performance.\n\nWe believe this analysis holds value, especially to guide practitioners when applying MetaMind. Importantly, we used the **same set of hyperparameters across all four benchmarks** (ToMBench, STSS, SocialIQA, and SOTOPIA) without tuning on each individual dataset, and MetaMind consistently outperformed baselines. This cross-task consistency suggests that, while some sensitivity exists, MetaMind is not overly reliant on hyperparameter tuning, and the framework is robust and generalizable. We will clarify this intent more explicitly.\n\n---\n_Q1. Hyperparameter optimization_\n\nWe used a held-out validation split for hyperparameter tuning, ensuring no data leakage. We will clarify this setup in Appendix A.5.\n\n---\n_Q2. On evaluating and enhancing individual agent capabilities_\n\nThank you for the insightful suggestion. While our current experiments (Section 5.1, Tables 4 & 5; Appendix A.9) show that each agent is indispensable, we agree that a more quantitative analysis of agent-level capability improvements is valuable.\n\nTo this end, we conducted additional experiments where we use base model as GPT-3.5 and selectively replace each individual component (ToM Agent, Domain Agent, Response Agent) with a more capable model (GPT-4-1106). This allows us to measure the marginal performance gain from strengthening each agent individually, as well as examine the effect of combining stronger agents. We summarize the results below for your reference. \n\n| **Configuration** | **Emotion** | **Desire** | **Intention** |**Knowledge** | **Belief** | **NL Comm.** |**AVG.**|\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| All Agents = GPT-3.5 (baseline) | 68.1 | 57.8 | 68.5 |48.5 |63.9 |76.2 |64.2|\n| ToM Agent = GPT-4 | 75.2 | 70.1 | 80.5 |56.7|77.8|80.2|73.4|\n| Domain Agent = GPT-4 | 71.5 | 66.8 | 75.9 |62.3|83.2|84.7|74.1|\n| Response Agent = GPT-4 | 70.8 | 65.9 | 74.2 |54.9|78.5|81.3|70.9|\n| All Agents = GPT-4 | 78.7 | 76.5 | 84.3 |68.2|88.6|88.5|81.0|\n\n We will include such a discussion in our manuscript as well. Thanks again for your helpful comment!"}, {"review_id": "9tSVEZQA6Q", "reviewer": "Reviewer_bcHQ", "summary": "The paper presents MetaMind, a multi-agent prompting framework designed to improve large language models' ability to perform human-like social reasoning. Instead of relying on a single-step response, MetaMind breaks down the reasoning process into three stages: generating mental state hypotheses through a Theory of Mind agent, refining them using Domain-specific rules and cultural norms, and finally validating and composing a response through a Reflection agent. This structured approach allows the model to better understand intent, emotion, and context in social situations. The authors demonstrate that MetaMind significantly boosts performance across several social reasoning benchmarks and works consistently across different model sizes, offering a modular and interpretable method for enhancing social intelligence in LLMs.", "questions": "1. How is each agent’s output validated within the MetaMind framework? For instance, if the Theory of Mind (ToM) agent generates an incorrect hypothesis, or if any other agent makes an error, is there a mechanism in place to detect and correct it? Since the framework involves multiple sequential agents, how does it prevent errors from cascading through the pipeline?\n\n2. Is the delta gain in performance, have any correlation with base model sizes, or is the delta model-size agnostic? Some trend analysis for this might give more insight into the framework.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is well written and clearly structured, making it easy to follow the proposed methodology.\n\n2. The experiments are thorough and cover a diverse set of social reasoning benchmarks, demonstrating the framework’s generalizability.\n\n3. The idea of modelling multi-agent social reasoning (ToM, Domain, and Response agents) is unique and original, offering a fresh approach beyond traditional single-agent prompting.\n\nWeaknesses:\n\nThe MetaMind framework lacks an explicit mechanism for verifying or correcting individual agent outputs. If the Theory of Mind (ToM) agent generates an incorrect hypothesis or another agent makes an error, that mistake can propagate through the pipeline, potentially compromising the final response. This lack of internal error correction increases the risk of cascading failures.", "was_revised": true, "final_justification": "I am maintaining my overall score of 'Accept'. The paper is grounded well in social psychology and has shown extensive experiments.", "rebuttal": "Dear Reviewer bcHQ,\n\nWe sincerely appreciate your positive feedback and the time you've dedicated to reviewing our manuscript. Your insights are invaluable to us. We address your comments in detail below.\n\n\n---\n_A1. potential error propagation in the pipeline_\n\nWe appreciate this thoughtful question. We agree that modular systems could be susceptible to errors propagating through its pipeline. However, we would like to clarify that **MetaMind is explicitly designed to mitigate such risks through architectural safeguards**: specifically, the Domain Agent serves as an intermediate verification layer, applying externalizable norms to refine or reject implausible mental state hypotheses before passing them to the Response Agent. \n\nThis design acts as a soft error-correction mechanism, helping prevent implausible or socially inappropriate hypotheses from cascading forward. To support this, we point the reviewer to our ablation studies in **Section 5.1** (**Tables 4 & 5**), which show that removing this Stage 2 component significantly degrades performance across all tasks. This demonstrates its vital role in reducing error propagation. \n\n\n---\n_A2. Is the performance gain correlated with model size?_\n\nAs shown in Figure 2, the performance improvement introduced by MetaMind is not strongly correlated with model size. For instance, relatively small open-source models such as ChatGLM3-6B see absolute gains of 10.2%, while much larger model larger models like GPT-4-1106 have gains of 6.6%. These examples highlight that MetaMind’s effectiveness is model-size agnostic. We will clarify this observation in the revision and include further analysis as suggested."}, {"review_id": "5eBip6h3V8", "reviewer": "Reviewer_3PQ4", "summary": "This paper introduces MetaMind, a cognitively motivated multi-agents framework designed to answer ToM related question. \nTheir framework is composed of 3 agents. The first proposes hypotheses about mental states given a situation, the second refines these hypotheses using cultural norms and ethical constraints, and the third generates responses that are appropiate.\n\nTheir framework achieves state of the art results on many ToM related tasks, with large improvements from previous SOTA frameworks, and mainly achieves human-level performance on ToM tasks for the first time.\n\nTheir ablation studies show that all of the parts of their framework is necessary to achieve such a good performance.", "questions": "1. Are there other architectures for multi-agent frameworks that would be able to answer ToM tasks ? This would strengthen the fact that THIS specific framework is the best for ToM task.\n2. What would be the results of ensembling 3 o3 predictions ? Would it also lead to an improvement ? The goal of this question and the previous one is to see if the improvement observed are mainly due to more test-time tokens or if this architecture plays the main role\n3. Are there models finetuned on ToM tasks against which you could compare yourself ? If not, would you be able to finetune one ? And would your framework also improve in this case?", "limitations": "1. More ablations are needed to be sure that the framework they chose is the cause of the significant improvements and not more test time tokens for examples", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n1. The research question is an interesting question, and has potentially vast applications for the development of more empathetic models\n2. Their framework does achieve significant improvements, matching human level on some task for the first time\n3. Their framework is both cognitively motivated and validated by the ablation studies, adding strength to the necessity of designing it as they did\n\n**Weakness**\n\n1. While they extensively compared their results to other ToM agents, they did not compare their results to multi-agent frameworks. Would it change the results ?\n2. Their framework does slightly improve top-tier reasoning model but the improvement does not seem significant\n3. They did not compare their framework to models finetuned on ToM tasks", "was_revised": true, "final_justification": "I think this paper is a good paper. It is relatively thorough, they provide an interesting method that is both cognitively grounded and achieves high improvements for all the tested models.\nI maintain my score at 5", "rebuttal": "Dear Reviewer 3PQ4,\n\nWe sincerely appreciate your positive feedback and the time you've dedicated to reviewing our manuscript. Your insights are invaluable to us. We address your comments in detail below.\n\n---\n_A1. Comparison to multi-agent frameworks. Can other architectures for multi-agent frameworks work for ToM tasks?_\n\nThank you for raising this excellent point. We would like to clarify that most existing multi-agent systems are designed for joint interaction modeling or emergent behavior learning (e.g., in simulated environments), and not for structured, inference-time social reasoning—which is the core focus of our work.\n\nMoreover, **among the relevant multi-agent frameworks, we have already included direct comparisons with representative baselines**---including Generative Agents and Hypothetical Minds (HM)—which explicitly model multi-agent social dynamics and Theory of Mind capabilities. As shown in **Tables 1–3**, MetaMind consistently outperforms these systems across a wide range of tasks, while also providing stronger modularity, interpretability, and inference-time control. We will make this distinction clearer in the revised manuscript and explicitly highlight this comparison in the discussion of related work.\n\n\n---\n_A2. Clarify significance of improvement on top-tier reasoning models_\n\nThank you for pointing this out. We agree that the absolute improvements shown in Table 6 may appear modest. However, we emphasize that these gains are achieved on top of already strong baselines, where improvements are typically difficult to obtain. Notably, even a ~2% gain in ToM accuracy can correspond to meaningful qualitative improvements in nuanced reasoning scenarios—particularly in socially sensitive applications.\n\nAdditionally, our framework provides interpretability and modularity benefits that go beyond raw performance: it allows structured hypothesis reasoning, norm-based filtering, and controllable outputs—capabilities not available in top-tier reasoning models alone. We will clarify this point in the revised discussion to better reflect these points. \n\n---\n_A3. Comparison to models finetuned on ToM tasks_\n\nThank you for this important suggestion. We now include a direct comparison to the latest state-of-the-art models finetuned on ToM tasks [1] (released in July this year). \n\n| **Configuration** | **Emotion** | **Desire** | **Intention** |**Knowledge** | **Belief** | **NL Comm.** |**AVG.**|\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| LLaMA2-70B-Chat | 62.3 | 55.4 | 65.2 |40.5 |65.1 |70.3 |59.8|\n| Centaur | 64.5 | 57.6 | 67.4 |42.7|67.3|72.5|62.0|\n| LLaMA2-70B-Chat + MetaMind| 67.4 | 60.5 | 70.3 |48.6 |70.2 |75.4 |65.4|\n| Centaur + MetaMind | 69.6 | 62.7 | 72.5 |50.8|72.4|77.6|67.6|\n\nOur results showing that **MetaMind achieves competitive or stronger ToM accuracy—without any task-specific finetuning**. Our framework operates entirely at inference time, making it more generalizable, modular, and readily plug-and-play across different LLMs. This distinction highlights MetaMind’s strength not only in performance but also in flexibility. We appreciate the reviewer’s suggestion and will update both the main text and related work accordingly.\n\n\n[1] Marcel Binz et al., A foundation model to predict and capture human cognition. Nature 2025\n\n---\n_A4. What would be the results of ensembling 3 o3 predictions?_\n\nWe appreciate the reviewer’s insightful question. To isolate the source of MetaMind’s performance gains, we conducted a control experiment ensembling predictions from 3 independent o3 runs using majority voting. The ensemble yielded only marginal improvements (≤+0.5%) over single-run o3, and remained notably below MetaMind's performance.\n\nThis supports that the performance gain is not merely a result of increased test-time tokens, but instead stems from MetaMind’s explicit architectural reasoning pipeline—including structured mental state inference, norm-based refinement, and response validation. We will include this comparison and discussion in the updated Results section to clarify the significance. Thank you again for helping us strengthen the empirical analysis."}, {"review_id": "Zn1jp0CHqk", "reviewer": "Reviewer_5ego", "summary": "This paper introduces MetaMind, a cognitively inspired multi-agent framework designed to enhance large language models' ability to perform Theory-of-Mind (ToM) reasoning. The architecture comprises three specialized agents—a ToM Agent, a Domain Agent, and a Response Agent—that work in a structured pipeline to infer latent mental states and generate contextually appropriate responses. MetaMind achieves state-of-the-art performance across several benchmarks, including ToMBench, social cognition tasks, and real-world social simulations, and enables LLMs to approach or match human-level ToM reasoning on a variety of tasks.", "questions": "- Line 194-195: \"Alongside\" sounds a bit awkward here, maybe change this to \"in addition.\" \n\n- Lines 239-241: can you give some specific examples? I think that would support this claim better. Maybe add some specific qualitative examples in the appendix.\n\n- Line 260: I think the ablation studies should be included with the main results, not in the Discussion. \n\n- Lines 294-295: I would like to see some results of the MetaMind approach compared with other ToM approaches, not just a baseline LLM (which is already known to be bad at these tasks). \n\n- Figure 3: This figure is not very intuitive - it is kind of hard to compare the two approaches given this visualization. Please edit this to make it easier to directly compare the two approaches. \n\n- Human experiments (Appendix B5): The human study should really be highlighted in the main text - I think this is one of the most interesting experiments. I would also report methods and results from this study more thoroughly, and include a table or figure as well.", "limitations": "The main limitation is that the work should include more comparison to other ToM approaches, rather than a naive LLM baseline (which is known to perform relatively poorly on these tasks). Otherwise, the authors provide a good summary of the limitations of the work in the discussion section.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The MetaMind architecture is well-grounded in cognitive theory; the decomposition of the three stages (ToM, Domain, and Response) mirrors human metacognitive reasoning and each stage has a clear, well-defined function. Additionally, the presentation of these stages as \n\n- MetaMind is evaluated on multiple challenging benchmarks covering different aspects of social understanding (ToMBench for ToM reasoning, social cognition tasks, and the STSS simulation tasks). The experiments are robust and show substantial improvements over non-ToM baselines. \n\n- The writing is generally clear and concise, and there is a clear narrative flow that is easy to follow. Key concepts are introduced clearly, in the right places, and are later reinforced in context. The story of the framework’s three phases is consistently reinforced (e.g., example scenarios in Figure 1 caption and in the text). \n\n- The paper is thoughtful about noting limitations of the work at the end and propose interesting directions for follow-up work. \n\n- Figure 2 especially is very clear and easy to interpret, and highlights the main results from this work. \n\nWeaknesses:\n- To strengthen the paper’s originality claims, it could more explicitly compare to other theory-of-mind frameworks (e.g. Generative Agents or SymbolicToM) and clarify what MetaMind adds beyond them. Additionally, exploring alternative architectures (e.g. could a two-agent system achieve similar results?) would underscore why the three-stage design is necessary. \n\n- Lines 170-173, \"Formally, the Domain Agent takes as input the set of 171 latent mental state hypotheses Ht = {h1, . . . , hk} produced by the ToM Agent, along with a set of constraint rules D. Each rule in D describes a specific norm or guideline, such as \"Romantic suggestions are not appropriate in professional settings\": These norms vary across different societies and cultures. And they are often very difficult to specify explicitly adn completely in the way that they are described here. This could be listed as an extension or future work to implement a system that can dynamically identify these norms; however, you should clearly address this limitation somewhere. \n\n- A few terms could be defined more formally at first mention. For example, “social memory” or “latent hypothesis” could use an upfront definition to aid readers.", "was_revised": true, "final_justification": "Overall, I think it is a technically sound paper with novel contributions, but there is room for a few additional experiments to further strengthen the proposal and demonstrate its practical applicability. I will maintain my score.", "rebuttal": "Dear Reviewer 5ego,\n\nWe sincerely appreciate your positive feedback and the time you've dedicated to reviewing our manuscript. Your insights are invaluable to us. We address your comments in detail below.\n\n---\n_A1. More comparison to other ToM approaches_\n\nWe absolutely agree with you on the importance of this comparison. **Indeed, we have already provided extensive comparisons to other ToM approaches (including Generative Agents and SymbolicToM) in Tables 1–3**, spanning Theory-of-Mind reasoning (ToMBench), social cognition, and social simulation tasks. \n\nMethodologically, MetaMind introduces a novel and cognitively grounded three-stage framework that sets it apart from prior ToM approaches. Generative Agents simulate agent behavior over time in sandbox environments but do not perform explicit hypothesis generation or refinement grounded in psychological theories of metacognition. SymbolicToM primarily tracks beliefs in multi-agent setups using plug-and-play belief representations but lacks an iterative mechanism for dynamically generating, critiquing, and refining competing mental‑state hypotheses, leaving it at static belief bookkeeping rather than full‑fledged social reasoning. In contrast, MetaMind explicitly operationalizes metacognitive social reasoning—decomposing it into (1) structured mental state hypothesis generation, (2) norm-aware refinement using social and ethical constraints, and (3) response validation grounded in social memory. This layered architecture enables empirically shown to be critical through ablation studies.\n\nWe agree with the reviewer that these distinctions should be highlighted more clearly in the paper, and we will revise the Related Work and Methodology sections to emphasize them. Thank you again for the constructive feedback.\n\n---\n\n_A2. Could a two-agent system achieve similar results? Why the three-stage design is necessary?_\n\nThat's an insightful question. To this end, we conducted ablation studies in **Section 5.1** where we removed one stage at a time (**Tables 4–5**). We observe that removing Stage 2 (Domain Agent) leads to degraded norm-sensitivity and inappropriate social cognition (–3.8% on average), while removing Stage 3 (Response Agent) results in sharp drops in real-world social simulation tasks (–16.1%). These findings demonstrate that each stage contributes uniquely and significantly to the system’s performance. As we highlighted in L289 of the manuscript, \n> _No single component should be left out_, confirming that social intelligence requires layered cognitive architectures.\n\n---\n_A3. Difficulty of specifying domain rules_\n\nWe appreciate the reviewer for highlighting this important point. Indeed, we fully agree that social and ethical norms are deeply context-dependent and culturally variable, and it is often infeasible to exhaustively specify them in a fixed rule set. In our current implementation, we adopt a manually curated set of representative norms drawn from widely shared social expectations, which enables initial grounding of the Domain Agent’s refinement process. However, we recognize that this approach may not generalize across all cultural or situational contexts.\n\nWe will add a clear discussion of this limitation in the revised Limitations section and explicitly mention as a direction for future work the integration of dynamic norm induction—e.g., via few-shot generalization from context or continual social learning. This is an exciting and open research direction toward making socially intelligent agents more adaptive and culturally aware.\n\n---\n_A4. Define terminology upfront_\n\nThank you for the helpful suggestion. In the revised draft, we provide formal definitions of key terms such as \"social memory\" (a dynamic store of user-specific emotional and behavioral patterns accumulated through prior interactions) and \"latent hypothesis\" (a structured representation of unobservable mental states such as belief, desire, or emotion, inferred from contextual cues). These clarifications will be added at their first mention in Section 3 to guide the reader more effectively throughout the methodology.\n\n\n---\n_A5. Writing and structure suggestions_\n\nWe appreciate your thoughtful suggestions on improving the readibility of our manuscript. As suggested, we will (1) revise the sentence to use “in addition” for smoother readability; (2) move ablations in L260 to main results; (3) revise Figure 3 to use aligned bar charts and clearer labels to improve comparability between conditions; (4) highlight Appendix B.5 (human experiments) in the main paper."}]

Author rebuttal:

Meta-review: This paper proposes MetaMind, a multi-agent framework designed to enhance LLMs social reasoning by emulating human metacognition. It breaks down social understanding into three collaborative stages: a Theory-of-Mind Agent for hypothesis generation, a Domain Agent for refinement using cultural norms, and a Response Agent for generating contextually appropriate replies. The reviewers appreciate the comprehensiveness of the evaluation and performance the proposed architecture achieves, as well as the clear writing and the fact that the work is inspired by theories grounded in psychology. 

The reviewers also raise several concerns, however, such as the modest improvements seen on already strong models, limited comparison to other similar multi-agent frameworks, as well as hyper parameter sensitivity and dependence on pre-defined constraints. The authors do a good job in their rebuttal and the reviewers appreciate the authors' efforts and responses. While that doesn't lead to higher scores, all reviewers are positive for this work.
Decision: Accept (spotlight)