Title: MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
Venue: NeurIPS 2025
Accepted: yes
Keywords: LLM

multi-agent system

social cognition
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\n1. 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", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-06"}