Title: Thought Communication in Multiagent Collaboration
Venue: NeurIPS 2025
Accepted: yes
Keywords: multiagent system

inter-agent communication

latent variable models

foundation models
Abstract: Natural language has long enabled human cooperation, but its lossy, ambiguous, and indirect nature limits the potential of collective intelligence. While machines are not subject to these constraints, most LLM-based multi-agent systems still rely solely on natural language, exchanging tokens or their embeddings. To go beyond language, we introduce a new paradigm, *thought communication*, which enables agents to interact directly mind-to-mind, akin to telepathy. To uncover these latent thoughts in a principled way, we formalize the process as a general latent variable model, where agent states are generated by an unknown function of underlying thoughts. We prove that, in a nonparametric setting without auxiliary information, both shared and private latent thoughts between any pair of agents can be identified. Moreover, the global structure of thought sharing, including which agents share which thoughts and how these relationships are structured, can also be recovered with theoretical guarantees. Guided by the established theory, we develop a framework that extracts latent thoughts from all agents prior to communication and assigns each agent the relevant thoughts, along with their sharing patterns. This paradigm naturally extends beyond LLMs to all modalities, as most observational data arise from hidden generative processes. Experiments on both synthetic and real-world benchmarks validate the theory and demonstrate the collaborative advantages of thought communication. We hope this work illuminates the potential of leveraging the hidden world, as many challenges remain unsolvable through surface-level observation alone, regardless of compute or data scale.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes enhancing the performance of LLM-based multi-agent collaborative discussions by introducing shared and private hidden thoughts. Specifically, the authors design an autoencoder with an assignment matrix to encode the latent thoughts from the last token embeddings. These latent thoughts are then split into shared and private components based on the assignment matrix and delivered to each agent in the next discussion round via prefix-tuning. The effectiveness of the assignment matrix design is first validated through experiments on a synthetic dataset, and the proposed method is further evaluated on two mathematical reasoning benchmarks, demonstrating its effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The paper explores an interesting and meaningful direction: multi-agent communication beyond natural language.\n2. It adopts an efficient training strategy that avoids heavy computation overhead.\n3. The design is intuitively appealing—communication as a composition of private and shared perspectives aligns well with how collaborative reasoning may work.\n4. Experiments are conducted on diverse and up-to-date model backends, showing the method's applicability across different LLMs.\n\nWeaknesses\n\n1. The experimental scale is limited, involving only three agents. It remains unclear how the method generalizes to more complex multi-agent settings.\n2. The experimental analysis lacks depth. For instance, the paper focuses on tuning parameters like prefix length and debate rounds, which are arguably not central to the proposed THOUGHTCOMM framework. More critical variables such as the scale of latent thoughts and the number of agents (which directly relate to the structure of thoughts) deserve closer examination.\n3. The implementation details are overly brief. The main paper and appendix provide only three sentences on implementation. Key details regarding the autoencoder, prefix-finetuning hyperparameters, and training setup are missing.\n\nQUESTIONS:\n1. Why is it necessary to group latent thoughts by agreement level?  \n\n2. Is $w_{\\alpha_j}$ manually set or learned during training?  \n\n3. How can we validate that the learned structure of thoughts is meaningful in math reasoning tasks?  \n\n4. Have you considered a baseline without any structure of thoughts? Specifically, what happens if all last-token embeddings from previous agents are directly used as prefixes for the next round, without grouping or assigning structure? \n\n5. This looks very similar to Latent Dirichelet Allocation. Have you tried to use a latent generative model directly on natural language in a similar way to LDA? The latent generative model is not bound to hidden representation. In my opinion, the design principle of THOUGHTCOMM is based on latent thoughts, not latent embeddings. Latent thoughts can also be tokens.  \n\n6. Can you provide a concrete case study of THOUGHTCOMM in action? Is there an example showing intermediate natural language outputs for each round of discussion?\n\n7. Do latent thoughts offer interpretable dimensions in real-world tasks like math reasoning? The toy example in Figure 1 includes interpretable concepts like \"carrying luggage\" and \"punctuality\". Can similar concepts be extracted in real-world tasks?\n\n8. Does scaling up the structure of thoughts lead to continued performance gains? \n\n9. From the example in Figure 1, the THOUGHTCOMM design aligns well with scenarios where the problem is approached from different perspectives. However, in cases where multiple agents hold opposing views and need to debate and persuade each other, can THOUGHTCOMM still operate effectively?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel communication paradigm for multi-agent LLM systems called thought communication, which replaces traditional natural language exchanges with direct interaction via shared latent variables. The authors formalize the notion of thoughts as latent variables and present a nonparametric latent variable model that allows agents to share and reason over these latent constructs. The core theoretical contribution is a set of identifiability results proving that shared and private thoughts can be recovered under mild conditions. Building on this, the authors develop THOUGHTCOMM, a framework leveraging a sparsity-regularized autoencoder and prefix adaptation for direct latent communication among agents. Extensive experiments on synthetic data and real-world reasoning benchmarks MATH, GSM8K demonstrate substantial performance gains over standard message-passing approaches.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n1.\tThe paper presents an interesting and well-explained idea: letting agents share their internal thoughts directly instead of talking through natural language, which can be vague or unclear. This tackles a well-known problem in how AI agents work together and offers a new way for them to communicate more effectively.\n\n2.\tThe paper establishes rigorous identifiability results for both shared and private latent thoughts ensures that the recovered latent representations reflect the true internal structure of agent reasoning. \n\n3.\tThe experiments results show clear improvements in both accuracy and agent consensus over existing baselines and are consistent across model scales.\n\n4.\tThe method avoids full LLM finetuning and works with frozen models, which makes it applicable in real settings.\n\nWeakness:\n1.\tThe proposed framework assumes access to agent model states, which may not be available for closed-source models. While acknowledged as a limitation, the practical impact is non-trivial, especially given the paper’s ambitious real-world aspirations.\n\n2.\tAlthough the theory ensures identifiability, there’s limited discussion on whether the recovered latent thoughts are interpretable or actionable by humans or other systems. Some empirical visualization or case study would have strengthened the narrative.\n\n3.\tThe paper adopts a sparsity-regularized autoencoder as its core estimation engine. However, there’s little exploration of its architecture, training sensitivity, or how design choices impact identifiability or downstream performance.\n\n4.\tThe evaluation is focused on math reasoning, which, while difficult, represents a narrow task distribution. It’s unclear how well THOUGHTCOMM performs in domains like planning, negotiation, or open-domain dialogue.\n\nQUESTIONS:\nPlease refer to the weakness.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes to leverage extracted thoughts (i.e. representation vectors) of one model that can be injected into another model in order to improve results.\n\nSTRENGTHS AND WEAKNESSES:\nOverall, this paper pursues a very interesting idea in an up-and-comming hot topic, i.e., multi-agent systems. It is mostly well written and nicely motivated. However, the presentation has still some issues, which leaves a couple of questions open to me.\n\nWhile reading, I struggled with the connection between the data generating process in the problem formulation and how to connect it to LLMs. The authors should consider making this more explicit.\n\nThe reported results are actually impressively good. So good that I actually have trouble to understand how this is achieved. The collaboration of 3 agents of the same type should not be able to solve a problem that much better than a single one.\n\nRelated work to the novel approach is not properly discussed. For example, how does the extraction of thoughts relate to research on interpretability, specifically to probing or sparse autoencoders?\nFurthermore, the proof in the appendix should be explained in more detail; to me if was incomprehensible.\nUnfortunately, the full code is not availbale for review.\n\nQUESTIONS:\nI am struggling with why the results on Table 1 are actually that good. How can the collaboration of just three 0.6B models substantially outperform an 8B models? What is the intuition behind this? Is the knowledge actually in the trained adapter, so is most of the improvement not about the multi-agent but about the finetuning?\n\nFigure 4: \" The red line marks the red line threshold typically considered identifiable when exceeded.\" => what does \"typically considered identifiable\" mean?\n\nLIMITATIONS:\nLimitations should be discussed in more depth. Also a proper discussion of and positioning with respect to related work is missing.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces thought communication, a paradigm where large-language-model agents exchange latent representations nstead of natural-language messages to reduce ambiguity in multi-agent coordination. It formalizes agent states as outputs of an unknown injective function applied to latent thoughts and proves that shared thoughts, private thoughts, and their agent-specific structure are identifiable under mild assumptions. Building on this theory, the authors propose THOUGHTCOMM: a sparsity-regularized autoencoder extracts latent thoughts from agents’ hidden states, and lightweight prefix adapters inject the relevant thoughts back into each agent during generation. Experiments on synthetic data validate the identifiability guarantees, while tests on MATH and GSM8K show accuracy and consensus gains over single-agent baselines and multi-agent finetuning, with minimal additional training overhead.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper demonstrates strong empirical improvements over existing methods in well-established benchmarks.\n- Introduces a novel “thought communication” paradigm and backs it with formal identifiability proofs. These results are theoretically interesting and provide a strong basis for the method’s empirical performance.\n- The paper is clearly written and well organized.\n\nWeaknesses:\n- The experimental comparison isn’t fair in terms of test-time compute and I think the baselines could be stronger. See questions.\n- There is a bit of a disconnect between the theory an empirical experiments. My understanding is that the real-world experiments use a dependency structure such that all messages are shared to all agents. It would be nice to have some real-world demonstration of why selective sharing of messages is important.\n- I think the paper could really benefit from a more detailed analysis of what the thought vectors are learning to exchange between agents. It’s not clear to me whether the empirical improvement just comes from an increase in test-time compute, rather than the models actually engaging in a meaningful form of collaboration. I think an analysis of the contents of the thought vectors could provide a lot of insight in this direction. Some experiments in this direction would likely lead me to improve my score.\n- Some of the claims are a bit bold and I think distract from the main points of the paper: “We hope this work marks a potential step toward superhuman collaboration, which is essential for superhuman intelligence”\n\nQUESTIONS:\n1) This form of multiagent approach is a form of test-time scaling since it involves both several rounds of debate and it involves querying several language models. Because of this, it seems unfair to compare against \"single answer\" that involves no test-time scaling. Do you think the experiments improvements could just results from the increase in test-time scaling? I would really like to see an attempt at giving each of the approaches similar amounts of test-time compute for a fair comparison. For examples, approaches like using thinking models or top-k sampling.\n\n2) It's claimed multiple times that the approach is model-agnostic. As it stands the claim is unsupported – it would be great to see some experimental results backing this up, or rephrasing that this is speculation.\n\n3) It's quite surprising that varying the prefix length doesn't affect the models' performance at all. I would think too small of a prefix length would bottleneck how much the models could communicate but this doesn't seem to be the case. What do you think is going on here?\n\nLIMITATIONS:\nThe limitations of the approach could be more thoroughly discussed. For example, multi-agent appraoces introduce additional overhead in terms of test-time scaling. Also, having models communicated through thought vectors rather than natural language introduces risks in making the models much less interpretable and black-box. Communication through natural language at least allows human oversight into the models. I would really like to see a discussion of this point in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}