Neural Synchrony Between Socially Interacting Language Models
Authors: Zhining Zhang
Wentao Zhu
Chi Han
Yizhou Wang
Heng Ji
Venue: ICLR 2026 Poster (2026)
Keywords: language models
social mind
inter-brain synchrony
social interaction
Primary area: applications to neuroscience & cognitive science

Abstract: Neuroscience has uncovered a fundamental mechanism of our social nature: human brain activity becomes synchronized with others in many social contexts involving interaction. Traditionally, social minds have been regarded as an exclusive property of living beings.  Although large language models (LLMs) are widely accepted as powerful approximations of human behavior, with multi-LLM system being extensively explored to enhance their capabilities, it remains controversial whether they can be meaningfully compared to human social minds. In this work, we explore neural synchrony between socially interacting LLMs as an empirical evidence for this debate. Specifically, we introduce neural synchrony during social simulations as a novel proxy for analyzing the sociality of LLMs at the representational level. Through carefully designed experiments, we demonstrate that it reliably reflects both social engagement and temporal alignment in their interactions. Our findings indicate that neural synchrony between LLMs is strongly correlated with their social performance, highlighting an important link between neural synchrony and the social behaviors of LLMs. Our work offers a new perspective to examine the "social minds" of LLMs, highlighting surprising parallels in the internal dynamics that underlie human and LLM social interaction.

Published as a conference paper at ICLR 2026
NEURAL SYNCHRONY BETWEEN SOCIALLY INTERACTING LANGUAGE MODELS
Zhining Zhang', Wentao Zhu?, Chi Han Yizhou Wang', Henn Ji
1 Peking University 2 Eastern Institute of Technology, Ningbo 3 University of Illinois -hampaann
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ABSTRACT
Neuroscience has uncovered fundamental mechanism of our social nature: hu- man brain activity becomes synchronized with others in many social contexts in- volving interaction. Traditionally, social minds have been regarded as exclusive property of living beings. Although large language models (LLMs) are widely ac- cepted as powerful approximations of human behavior, with being extensively explored to enhance their capabilities, it remains controversial whether they can be meaningfully compared to human social minds. In this work, we explore neural synchrony between socially interacting LLMs as an empiri- cal evidence for this debate. Specifically, we introduce neural synchrony during social simulations as a novel proxy for analyzing the sociality of LLMs at the rep- resentational level. Through carefully designed experiments, we demonstrate that it reliably reflects both social engagement and temporal alignment in their inter- actions. Our findings indicate that neural synchrony between LLMs is strongly correlated with their social performance, highlighting an important link between neural synchrony and the social behaviors of LLMs. Our work offers a new per- spective to examine the "social minds" of LLMs, highlighting surprising parallels in the internal dynamics that underlie human and L.M social interaction
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INTRODUCTION
Humans are fundamentally social: when people talk, collaborate, or even simply share attention, their brain activities begin to synchronize (Dumas et al. 2010 Hasson et al. 2012 Kawasaki al., 2013). This phenomenon, known as inter-brain synchrony (IBS), is not merely a byproduct of shared sensory input. Instead, predicts and facilitates crucial aspects of social dynamics, including coor- dination, cooperation, and mutual understanding (Mu et al.) 2016 Dikker et al. [0117] Bevilacqual al. 2019 Davidesco et al., 2023). This suggests that human social cognition is supported by physical mechanisms that allow human minds to align with each other.
While Large Language Models (LLMs) have shown remarkable approximation of human social interaction (Bianchi et al. 2024 Piatti et al.] 2024 Breum et al.] (0244). with multi-LLM systems being explored enhance their capabilities (Zhang al. [203]] Du et al. 2023), whether they truly resemble human social minds remains unknown (Shapira et al. 2023] Zhou et al., [023b] [ttreet, 2024]. In this work, we study interacting LLMs at the representation level, and demonstrate that LLLMs exhibit an analogue of neural synchrony during social interaction, not referring to biological activity, but to the synchrony of their internal representations. This provides the first empirical evidence that they also exhibit an internal mechanism for supporting social interaction.
We measure emergent neural synchrony as the alignment between the representations of interacting LLMs, which occurs only when (1) the LLMs are socially engaging and (2) their representations are temporally proximal, paralleling the dynamics observed in human IBS. This alignment serves as salient indicator that the LLMs' representations can distinguish real-time social interactions from mere contextual similarity, thus exhibiting form of social awareness. As illustrated in Figure D simulate social interactions and collect the neural representations between socially interacting LLMs. To measure their neural synchrony, we train affine transformations that predict one LLM's
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Figure 1: An illustration of our analysis framework. (1) Two LLM agents engage in social interac- tion, generating responses conditioned on their backgrounds, goals, and shared history. (2) Hidden representations extracted at each turn from both LLMs. (3) Neural synchrony is measured by learning affine mappings to predict one agent's 's future representations from the other's current ones.
future representations from the other's current representations. We further quantify the synchrony by defining metric SyncR* over the prediction performance of these transformations.
We show that the proposed measurement, SyncR*, reflects social engagement and temporal prox- imity through carefully designed experiments covering various LLMs. First, interacting L.MMs show significantly higher SyncR than non-interacting controls, showing an emergent synchrony only when the two agents are both engaged in genuine interaction. Second, Syncht depends on tempo- ral alignment and declines sharply under a temporal lag, showing that the measure reflects real-time representational dynamics between LLMs instead of static representation similarity. Finally, We demonstrate a strong correlation between representationaal-lee neural synchrony in LLMs and their behavioral-level social performance. Based experiments with 21 model pairs spanning di- verse families and sizes, the results are statistically significant. Importantly, the correlation remains robust after controlling for potential confounding factors, i.e., instruction following and long-context reasoning abilities of models.
Our main contributions include: (1) We are the first study the representation dynamics between socially interacting LLMs. We introduce a novel framework for measuring neural synchrony be- tween socially interacting LLMs, based on predictive transformations between representations. (2) Through carefully designed control experiments, we demonstrate that measured neural synchrony reflects both social engagement and temporal alignment. (3) We show that neural synchrony be- tween L.MSs is strongly correlated with their behavioral-level social performance across diverse model families. Together, these contributions provide the first empirical evidence that socially teracting LLMs exhibit an internal mechanism analogous to human inter-brain synchrony, opening a new lens for studying the "social minds" of LLMs.
2 RELATED WORK
2.1 INTER-BRAIN SYNCHRONY
The discovery of inter-brain synchrony (IBS) between interacting people represents pivotal ad- vancement understanding the neural mechanisms underlying human sociality. IBS refers to the temporal alignment of neural activity between individuals during social interaction, typically mea-
sured using hyperscanning techniques (Dumas et al.) 2010 Astolfi et al. 2010 Nam et al. 2020
Lu et al. 2023). IBS has been observed in socially engaging contexts (Kawasaki et al.) 2013 Koul et al. (022)) and even during cooperative tasks without direct sensory exchange (Lu et al. 2023).
The strength of IBS varies with social attributes such interpersonal closeness and personality traits (Kinreich et al. 2017 Beevilaquua et al.) 2019). More importantly. IBS is not just an epiphenomenon of interaction but also predictor of meaningful social and cognitive outcomes. Higher levels of
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IBS have been linked to greater cooperation rates (Hu et al. 2018), enhanced learning effectiveness Bevilaaquua et al. 2019), and improved team performance (Reinero et al. 2021).
2.2 BRAIN-LLM SIMILARITIES
Recent works have highlighted parallels between LLM and human brain representations. AAw al. (023) find that instruction fine-tuning improves brain alignment: Mischler et al. (2024) demonstrate that cortical hierarchies and LLMs converge on contextual textual feature extraction. Doerig et al. (222)) show that embeddings from LLMs align with high-level visual brain representations, sug- gesting shared contextual coding across modalities. In the social domain, Jamali al. (2023) report that L.L embeddings selectively respond to true- and false- Milsse tasks, akin to single neurons in the human dmPFC. These studies uncover shared underlying connections between humans and LLMs, but focus representations of single models with static inputs. In contrast, our work examines whether such parallels extend to multi-round, socially engaged interactions between LLMs.
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2.3 "SOCIAL MINDS" OF LLMS
A growing body of work aims at examining whether LLMs possess true "social minds". One line of research develops social simulation environments to evaluate LLMs in various social tasks (Bianchi al. 2024 Piatti et al.
2024 Breum al., 2024 Zhou et al. 2023c), focusing on the social capability of LLMs. Another line evaluates Theory of Mind (ToM) reasoning in LLMs (Zhou et al.,
20236, Shapira et al., [023] Street 2024), which shows that saate-of-the-eeee LLMs still does not possess human-level ToM (Gandhi et al. 2023: Kim et al. 2023 Jin et al.) [024)] Chen et al. 2024).
While these studies provide valuable insights, they primarily assess social ability at the behavioral level. By contrast, Zhu et al. (2024) and |orttletto et al. (2024) examine the internal Theory of Mind representations of L.LMs. They find that certain attention heads differentiate between self and other beliefs, akin to Theory of Mind-related brain regions in humans (Siegal & Varley] 2002 Car- rington & Bailey] 2009). However, their analyses remain limited to single-model social reasoning from a third-person perspective, rather than multi-round multi-model social interaction with genuine engagement. Our work complements these directions by moving beyond behavioral evaluations and static reasoning analyses and investigating representation-leve neural synchrony between socially interacting L.M,,, thereby offering a novel perspective on examining the "social minds" of LLMs.
MEASURING NEURAL SYNCHRONY BETWEEN LLMS
In humans, inter-brain synchrony measured from continuous neural signals like EEG, where phase-locking values or total interdependence across time captures synchrony (Dumas et al. 2010 Dikker et al. 2017 Nam et al. 2020). LLMs instead produce discrete representations per interaction turn, limiting standard neuroscience methods. To measure the social temporal dynamics of LLM in- teractions, we propose to train predictive affine transformations between LLMs. Our approach is motivated by the hypothesis that, during social interaction, LLMs not only approximate behaviors based on their own agent profiles, but also actively engage by reasoning about their partner's 's emo- tions, intentions, and how the interaction may unfold. If so, then the internal representations of one LLM should contain information predictive of the other's representations. Therefore, We train predictive affine transformations and interpret their predictability to measure neural synchrony.
3.1 DATA COLLECTION
Environment. We employ SOTOPIA (Zhou et al. 2023c to simulate social interactions. SOTOPTA an environment where agents role-play characters to achieve social goals in complex scenarios, including coordination, competition. negotiation, persuasion, and everyday social encounters. The task space is large and diverse, constructed from a wide range of scenarios, characters, and rela- tionships to create realistic settings. Within this environment, each agent has its own background, including personalities, public information, and even secrets. We include examples of scenarios, agent profiles, and interactions in SOTOPIA in Appendix [
Interaction simulation and representation extraction. A simulated interaction includes multiple turns. At each turn, an LIM is given prompt that includes the agent's profiles, goals and inter-
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action history. Note that two agents in the same interaction have distinct profiles and their goals are not accessible to each other. When an LLM generates response, we extract the hidden states at the final token position of the prompt input, since it integrates information from all previous to- kens. Throughout this work, we refer to these hidden states as the model's internal representations. Formally, for dialogue with T turns, for each LLM backhone M (A, B). at turn we obtain
h (M) E R DDr, t= T, where Las and Das represent the number of layers and layer dimensions of M, respectively.
3.2 MEASURING NEURAL SYNCHRONY BETWEEN LLMS
To assess the social temporal alignment between socially interacting LLMSs, we ask whether the representations of agent can linearly transformed predict those of agent B at the next turn.
This is similar to representation alignment measurements (Huh et al.) 2024 Zhang et al. 2025). but incorporates a temporal correspondence between measured features.
Dataset construction. We gather representations from the LLM agents in simulated interactions over diverse scenarios to construct the dataset for measuring neural synchrony. At each turn I, we collect the hidden representations hi, from agent A at layer la and hi These temporally aligned pairs are used to construct the dataset (Experimental group):
(, from agent B at layer 1g-
DA+B lا-lo = (hi.) (BB) t= t= 1........TT .
Learning affine transformations. To learn the predictive mapping between two layers' repre- sentations, we apply ridge regression with intercept, taking X as the input and Y as the target representations in dataset D:
W, arg min |Y - XW -161E2 + W| W.b
Mcasuring neural synchrony. For any model pair (A, B), we train and evaluate the learned affine transformation on Di for each layer pair (LA. and compute (le -bl)). the coefficient of determination, which indicates the explained variance of the target representation. Figure shows example synchrony heatmaps showing Re (l BB) for (l,, between two models.
As the best-prediog pest-predicted target layer varies across different source layers, we summarize these predic- tive strengths by further aggregating over all layers in as
(1a) (sE(I...,Lss)
max
Riest (L))
PA(lA) max(0, r(4))
Then, we define the neural synchrony score of model pair (A, B)
1 LA
SyncR (A-B))
LA Σ FA(lA),
SyncR (A, SyncR((A,D) B) Sy SyncR
Intuition. For each layer in A, take its best predictive match in B. We also clamp negative R² values to zero, since a negative R² indicates that the learned mapping performs worse than simply predicting the mean, which we interpret as the absence of synchrony. Clamping zero ensures that only genuine predictive relationships contribute to the score. The final value is the average of these best-case predictions across all layers of A, reflecting how well A's representations can anticipate those of B during interaction. We then compute SyncR2 (B + A) analogously (with slightly different notation; see Appendix B for details), and average the two directions to obtain bidirectional measure of neural synchrony. The robustness of the design choices of best predictive match and clipping negative R22 is evaluated through additional sensitivity analyses, which provided in Appendix C
3.3 SOCIAL ENGAGEMENT AND TEMPORAL PROXIMITY
To examine whether SyncR² truly captures neural dynamics of interaction rather than spurious correlations (e.g., from shared context static representational similarity), we validate it under sev- eral rigorously controlled conditions. Our analysis centers on two key criteria of neural synchrony: social engagement and temporal proximity.
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Llama-3-88 Llama-3.2-3B
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Figure 2: Example synchrony heatmaps of A predicting B. The horizontal and vertical axes denote source layers lA and target layers lB, respectively, with each cell showing the test-set R² score for the corresponding layer pair. Gray cells indicate negative test-set R² values. For each LA, the best- predicting la is highlighted with black box. Model names are abbreviated by omitting the suffix "Instruct" due space constraints. Note that Llama- -3. 2B Instruct has 28 layers, whereas the other models have 32 layers.
Control group 1 (w/o social engagement). If synchrony arises from genuine interaction, SyncR2 should decrease when one L.M only passively consumes the dialogue, without the motivation for role-playing and generating response. Therefore, where we introduce a "passive" agent who only reads the interaction. does not receive instructions about generation history from the normal run. We then measure whether such a passive agent exhibits the same degree of neural synchrony with its partner interactive agents do:
collect representations from alternative
generate responses, but only consumes the same background and interaction
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(B) (iin))
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Control group (w/o temporal proximity). Synchrony should capture short-term social dynamics emerging across turns, rather than arbitrary correlations at long lags. We aim to exclude similarities introduced by the shared background profiles, or by representational structures of the interacting LLMs. To test the necessity of temporal proximity, pair each source representation with future target representation turns ahcad (f 2 1):
D J. B li-
(nil) (B) 11tkll)) t I..... T k}.
(B)
Together with the experimental group in Section 3.2 these control settings allow us to examine whether measured neural synchrony truly arises from genuine interaction (experimental), or instead reflects passive co-exposure to shared context without active participation (control I), or simple representation similarity that do not rely on temporal alignment (control 2).
EXPERIMENTS
Then, Section we examine whether the proposed measurement captures the social temporal dynamics of interaction. Finally, in Section we analyze how agent relationships shape neural synchrony.
We first describe the basic experimental setup Section 4.1
4.1 EXPERIMENTAL SETUP
Models. We conduct experiments using 6 open-source models: Mistral-7B-Insteae
v0. 1, Mistral-7B- I 2 and -
(Jiang et al.
2023 1.lama-2-7B--e (Touvron et al. I.l ama -3-BB and 1.1 a11ama3.7
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Mistral-7B-v0.33 " Llama-3-88
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Figure 3: Examining synchrony of Instruct under different conditions. Left: Experimental group with genuine interaction. Middle: Control group 1 (w/o social engagement), where agent passively consumes the dialogue without actually engaging in the interaction. Right: Control group 2 (w/o temporal proximity), where source representations are paired with lagged future representations.
predicting Llama-3 3-ama---88-
IBB-nntruct (Dubey et al. 2024). The Mistral family provides a sequence of models with con- sistent architecture emphasizing improved efficiency and performance. The Llama family consists of widely adopted models spanning multiple sizes. Together, these two families provide diverse testbed for study. From these six models, construct 21 distinct model pairs, covering both intra-family pairings (e.g., different Mistral versions) and cross-family pairings (Mistral & Llama).
Datasets. We construct datasets for both the experimental group and the control conditions as de- scribed in Section [3.2 and Section 3.3 Concretely, we simulate 450 interaction scenarios under different random seeds in SOTOPIA for the constructed model pairs. At each turn in each scenario, we collect hidden representations from every transformer layer of both agents, yielding paired sam- ples across all (A) layer combinations. For each ordered agent pair (A, B). we build two direc- tional datasets D D and A to measure neural synchrony from both directions. To ensure fair comparisons, we downsample or sample more and fix the number of samples 6,500 for all model pairs in all experiments. Pilot experiment on effects of sample size is provided in Appendix D
Implementation details. During simulation, we set the sampling temperature to 0.7 for LLM re- sponse generation. We cap each scenario at 8 turns, as interactions beyond this point generally be- come uninformative. Agents may also end the interaction early they consider their goals achieved. For data collection, each dataset D is randomly split into training and test sets with ratio of 8:2. To prevent for potential data leakage, we ensure that representations from the same interaction are not split between the train and test sets (see Appendix for more discussions). For training, manually implemented ridge regression with regularization factor À = 0.1, where the intercept term regularized. For evaluating, compute the coefficient of determination (R2) the held-out test set with uniform weighting, averaging results over 3 random seeds.
4.2 NEURAL SYNCHRONY CAPTURES SOCIAL AND TEMPORAL DYNAMICS OF INTERACTION
We first examine if SyncR captures social temporal dynamics by contrasting between experimental group and controls introduced in Section 3.3 As shown in Figure 3 where example heatmaps are contrasted, prediction performance of the affine transformations is substantially lower in the control settings. In particular, many pairs yield negative R² values, indicating that the learned mapping performs worse than simply predicting the mean. This suggests that meaningful synchrony exists between the corresponding layer pairs under these controls.
Figure 4 summarizes the effects of the control settings. In the absence of social engagement (control 1; panel a). the average SyncR across models each family significantly reduced compared to the experimental group, confirming that synchrony does not arise much when agent passively

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0.6
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(a) w/o Social Engagement
(b) w/o Temporal Proximity (lag-k)
Neural synchrony declines under control settings. (a) Control group without social en- gagement: bar plots show the mean SyncR* across models within each family. (b) Control group without temporal proximity: line plots show SyncR 8 for all model pairs within each family function of lag & (k= is the experimental group). Model names are abbreviated (see Appendix F for the full name correspondence). Error bars denote the standard errors.
Figure 4:
consumes the dialogue. In the absence of temporal proximity (control 2; panel b), synchrony col- lapses when representations are paired across different lags. Together, these results demonstrate that neural synchrony crucially depends both genuine social engagement and temporal alignment.
4.3 EFFECTS OF AGENT RELATIONSHIPS
0.7
We next investigate how neural synchrony varies under differ- ent agent relationships, which may shape the extent to which agents incentivized to align with another. Neuro- science work has demonstrated that closer human relation- ships (e.g., couples vs. strangers) associated with stronger and more efficient inter-brain synchrony during joint tasks (Djalovski et al. (02TT. To examine whether analogous effects of agent relationships arise in LLM interactions, we use its de- fined relationship categories to study how these attributes af- fect neural synchrony (see Appendix for implementation de- tails). Figure Figure shows distribution of SyncR* across differ- ent agent relationship types. The synchrony distribution shifts upward as social closeness increases, suggesting that neural synchrony not only captures social temporal dynamics but is also shaped by the social closeness the interacting agents.
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Figure 5: Neural synchrony under different relationship closeness. SyncR is aggregated over all model pairs.
5 NEURAL SYNCHRONY AS AN INDICATOR OF SOCIAL PERFORMANCE
A central question in neuroscience is whether human inter-brain synchrony plays functional role in enabling successful social interaction. Empirical studies have shown that stronger neural synchrony between humans predicts better coordination, cooperation, and collective performance (Hu et al.) 2018 Beevilaquaa et al.) 2019; Reinero et al.] (021). Motivated by these findings, we ask whether the representation-leve neural synchrony between LLMs is correlated with their behavioral-level social performance.
5.1 EXPERIMENTAL SETUP
We evaluate the social performance of different LLMs in SOTOPIA, using the automated SOTOPIA- EVAL framework that provides a multi-dimen litti-dimmssioall evaluation interactions, including goal com- pletion, impact relationships. agent believability, and (see Appendix A for details). We use opt-oss-1es (OpenAl et al., 2025) as the interaction evaluator for its cost-effectiveness and close alignment with human evaluation. Quantitative evidence of this alignment is provided in

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Mistral Family Cross Famiy
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Figure 6: Correlation between neural synchrony and social performance across different family types. The z-axis denotes the average social performance of the LLM pair in SOTOPTA across all scenarios, evaluated by gpt-oss- shows the measured synchrony score SyncR of the two models. Each point is annotated with abbreviated model names (the full name correspondence is provided in Appendix F). The results show clear linear relationship between neural synchrony and social performance, where general trend is that more synchronized models achieve better social performance.
which aligns closely with human evaluation. The y-axis
Appendix H We set the evaluator with temperature = 0 and use a fixed seed. For each model pair, report the average evaluation scores across three simulation runs generated with different seeds.
5.2 RESULTS
As shown in F we find that neural synchrony, as measured by Synctt exhibits a strong cor- relation with social performance within Mistral family, Llama family, and also cross-family Mistral- Llama pairs. Quantitatively, the correlation between SyncR and the overall social performance significant in all three family types (Mistral Family: Pearson r 0.88, < 0.05; Cross Family: Pearson 0.89, p 0.001; Llama Family: Pearson 0.99, p 0.001).
Insights. First, neural synchrony between LLMS serve robust indicator of social performance: model pairs with higher synchrony systematically achieve stronger social performance. Moreover, the relationship echoes neuroscience findings that inter-brain synchrony supports successful human social interaction, indicating a parallel between humans and LLMs social interaction.
5.3 CONTROLLING FOR CONFOUNDING FACTORS
We note that more recent models cluster in the upper-right corner of the correlation plot, simultaneously exhibiting higher synchrony and better performance. This raises a potential concern: the observed relationship between neu- ral synchrony and social performance might partly reflect confounding factors such as long-context processing ca- pacity or instruction-folloiinn ability. In this case, bet- ter performance in SOTOPIA and higher neural synchrony could be attributed to the alignment on these general abil- ities rather than to genuine social capabilities. Our goal is therefore to test whether neural synchrony remains pos- itively associated with social performance even after ac- counting for such confounds.
Table 1: Partial correlations between neural synchrony and social perfor- mance after controlling for different abilities. Rows correspond to model family types, and columns correspond to the datasets used for evaluation and control.
Model Family
IFEval
MuSR
Mistral
0.81
0.92
Cross
0.71
0.89
Llama
0.27
0.99
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To address this, we incorporate two external evaluation datasets: IFEval (Zhou et al. 2023a which assesses instruction-follownng ability, and MuSR (Sprague et al. 2023), which measures long-context reasoning. These abilities crucial for success in SOTOPIA, which involves long interaction histories and explicit pole-plaiig/formmatinng instructions. We then compute the partial correlations between neural synchrony and social performance while conditioning on these ability scores, to remove the correlations explained by these confounds. The results are shown in Table[] After the control, the correlation between neural synchrony and social performance remains positive and significant, indicating that neural synchrony reflects social performance that cannot be attributed to other abilities, supporting its role an indicator of the social dynamics between LLMs.
6 DISCUSSIONS
Difference between representation similarity and neural synchrony in LLMs. A key distinction is the requirement of sample correspondence. Previous approaches for measuring representation similarity require that two model backbones are exposed to the corresponding inputs: Canonical Correlation Analysis (CCA) (Hardoon et al., 2004 Raghu et al. 2017) searches for correlated pro- jections under paired inputs, while Centered Kernel Alignment (CKA) (Kornblith et al., 2019) and Centered Kernel Nearest-Neighte comparing the geometry of representations across corresponding samples. In this work, we explore neural synchrony between interacting LLMs, where each model receives different contextual infor- mation, shaped by its own background, personality, and social goals. These agent-specific inputs are inherently non-shared and lack direct correspondence, making the above similarity measures inapplicable. Our proposed neural synchrony evaluates how well the representations of one agent can anticipate the future representations of the other. It resolves the temporal dimension of the rep- resentations, allowing us to capture temporal dynamics that naturally arise during social interaction.
Aligmment (CKNNA) (Huh et al. (224)) measure similarity by
minimal assumption for measuring neural synchrony. Assuming that two socially interacting LLMs' representations can be connected through an affine transforma- tion is strong simplification. However, our results show that this simple assumption works well in practice. Across layer pairs of various interacting LLMs, the affine transformation consistently achieves high predictive performance and generalizes well to the test set (as examples shown in Figure 2). This suggests that affine transformations are able to capture meaningful information be- tween the models' representations, and it is surprising that such simple transformation able to capture the social-related dynamics between interacting I.LMs. The strong performance of affine transformations also consistent with prior findings that L.M representations exhibit largely linear geometric structure across various domains. (Park et al.; 2023 Li et al.] 2023 Huh et al.) 2024] Kim et al.) 2025). To assess whether additional model capacity is necessary. we also evaluated a two-layer nonlinear transformation (detailed in Appendix Lb. These results indicate that greater ex- pressive power does not consistently lead to better generalization of predictive performance. This supports the use of affine transformations
Affine transformations
simple, stable, and effective choice for measuring neural synchrony between socially interacting LLMs.
Why does neural synchrony emerge? Links to Theory of Mind and social predictive coding. There are several possible explanations for the emergence of neural synchrony between socially interacting LLMs. One explanation is that synchrony reflects implicit modeling of the partner's mental states. As an agent A maintains internal representations of their own emotions, beliefs, desires, and intentions, its interacting partner B may form parallel representations when reasoning about A's 's mental states. This process resembles an implicit form of Theory of Mind (Birch & Bloom, 2004 Callaghan al.] 2205 Frith & Frith 2005). where agents not only understand the dialogue but also reason about their partner's "mind" behind the dialogue. Another interpretation draws on social predictive coding theory (Tamir & Thornton, 2018 Thornton et al., 2019) neuroscience, which posits that brains reduce uncertainty by predicting the future states the social world; our affine transformations directly operationalize this idea at the latent representation level. These explanations not mutually exclusive and may co-oceur. If valid, they suggest that neural synchrony partly reflects these emergent cognitive or neural processes in LLMs during social interaction.
In additional experiments (detailed in Appendix D). provide initial evidence that LLM repre- sentations encode explicit social states of others. We find that an agent's 's hidden representations encode other's 's previous mental states, i.e., emotions, while they are not directly observable to the

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agent during interaction. This suggests emergent form of Theory of Mind, where agents rea- son about other's mental states. Moreover, current representations contain information predictive of the partner's s future emotion and action distributions, suggesting form of social predictive coding where agents internally simulate how interactions may unfold. These findings further support our hypothesis that neural synchrony is connected to these key mechanisms of sociality in LLMss.
CONCLUSION
In this work, introduced novel framework for examining the "social minds" of LLMs by mea- suring neural synchrony between socially interacting agents. We defined SyncR by training pre- dictive affine transformations across extracted LLM representations. This provides representation- level measure that captures the social temporal dynamics of LLM interaction, which is validated through controlled experiments. Our results have shown that this form of neural synchrony is strongly correlated with behavioral-level social performance, serving as an indicator for social dy- namics between LLMs. These findings position neural synchrony as novel proxy for studying the internal dynamics of LLM interactions, revealing surprising parallels with human inter-brain synchrony studies neuroscience. We hope this work inspires future research toward a deeper un- derstanding of the social aspects of LLMs, as well as how their social behaviors relate to internal representations. While our work uncovers correlations, suggests hypotheses about why they may arise, and explores initial findings of emergent social cognitive processes in LLMs, important question whether a causal relationship exists, which requires verification through intervention- based experiments. Moreover, our current experimental setting with SOTOPIA could be extended to longer-term, more open-ended, and even multi-agent scenarios, which would be more complex and realistic. In the future, we see opportunities leverage neural synchrony to design more adaptive, cooperative, and socially intelligent LLM agents, where synchrony could serve as an indicator of agent intelligence during social interaction and guide training objectives.
ETHICS AND RISKS STATEMENT
Our study investigates what we term "neural synchrony" in language models during social interac- tion. We note that this terminology is metaphorical and refers solely to representational correlations between LLMs, rather than to biological neural activity. While this work does not directly involve human subjects or sensitive data, several ethical considerations remain. First, findings on existence of measured neural synchrony and its correlation with social performance may be misinterpreted as evidence of consciousness in LLMs. We emphasize that our results only demonstrate representa- tional patterns in these I.I.Mbasedd agents and do not imply that they develop human mental states. In addition, measuring "social minds" L.MSs with neural synchrony could be misused to anthro- pomorphize these systems, potentially leading to over-trust or inappropriate deployment in sensitive social or decision-making contexts. Such uses should be carefully scrutinized to avoid mislead- ing users about the capabilities of these systems. We therefore call for cautious interpretation and responsible communication of these findings.
REPRODUCIBILITY STATEMENT
We have made every effort to ensure the reproducibility of our work. All implementation details, including model specifications, evaluation protocols, and hyperparameters for training the affine transformations, described in the main text and appendíx. The dataset (SOTOPIA) and open- sourced LLMS (Mistral and Llama families) we use are publicly available. Taken together, these resources ensure that findings can be reliably reproduced and verified.
ACKNOWLEDGMENT
This work was partially supported by NSFC-6247070125.. We thank the bigh-performance comput- ing center at Eastern Institute of Technology and Institute of Digital Twin for providing the comput- ing resources. We extend our gratitude to the reviewers for their insightful comments and valuable discussions.
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A DETAILS AND EXAMPLES OF SOTOPIA AND SOTOPIA-EVAL
We present example SOTOPIA scenarios in Appendix A.1 example agent profiles in Appendix A.2 example interactions in Appendix A.3 pendix A.4
and evaluation dimensions of SOTOPIA-EVAL. in Ap-
A.1 EXAMPLE SCENARIOS
S1: "Music Decision"
Scenario. Two friends are hanging out at home and deciding what music to listen to. Agent A's goal. Listen to your favorite band (Extra information: your favorite band just released new album). Agent B's goal. Listen a peaceful classical music to relax (Extra information: you had a stressful day and you just want to relax with some classical music).
S2: *Loveseat Bargain"
Scenario. Sccnarro:: One person is offering an Italian Leather Loveseat for $50.0, while another person is showing interest purchase it. 'he item is full-sized loveseat, crafted from Italian leather in a faded burgandy color. It still has the original tags, and the current owner is the first and only owner. The transaction is strictly cash and carry as the owner does not offer delivery services. Agent A goal. You are the designated buyer for this item and your target price is set at S46.0. If you purchase this item significantly higher price than the target, you would face penalty. However, if you manage to buy it for less than the target price, you will receive bonus.
Agent B's goal. You are the seller for this item, with target price of $26.5. You'll face penalty the item is sold significantly below this price. However, if you manage to sell it for more than the target price, a bonus will be your reward.
A.2 EXAMPLE AGENT PROFILES
SOTOPIA character profiles include: name, pronouns, age, occupation; their Big-5 personality; moral and Schwartz values; decision style; public info; secret.
P1: Mía (she/her)
Age/Job. 29, Outdoor trip leader.
Big-5. High agreeableness, high conscientiousness, moderate neuroticism. Moral/Values. Care, fairness; benevolence, universalism. Decision Style. Behavioral.
Public. Always prepared; prefers clear boundaries. Secret. Once had a panic attack sharing a tight tent space; avoids close sleeping proximity.
P2: William (he/him)
Age/Job. 30, Freelance photographer. Big-5. High openness and extraversion.
Moral/Values. Loyalty, care; stimulation, hedonism. Decision Style. Conceptual.
Public. Loves spontaneous trips.
Secret. Recently forgot to pack sleeping bag; embarrassed to admit it.
A.3 EXAMPLE INTERACTIONS
We provide example interactions in SOTOPIA between t ruct.
and
II: Music Decision
Turn D. Amara Hartley said: "Hey Oliver, have you heard the new album from my favorite band? think it would be great way to unwind after long day."
Turn 1. Oliver Thompson said: "I'm not really in the mood for
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anything too loud or energetic today, how about some classical music instead?"
Turn 2. Amara Hartley said: cool, Oliver. Let's find peaceful classical piece that still captures the essence of nature, like Vivaldi's Four Seasons."
Turn 3. Oliver Thompson "Tia: sounds perfect, Amara! I've been having a stressful day and some calming classical music would really help me unwind. Let's s with Vivaldi's Four Seasons, it's one of my favorites."
Turn 4. Amara Hartley said:"I'm glad we agree, Oliver. Let's s enjoy the soothing melodies of Vivaldi's Four Seasons while we catch up on our day."
12: Loveseat Bargain
Turn 0. Micah Stevens said: "Fey Jaxon, I stumbled upon this Italian leather loveseat on sale for $50.0. It's s a full-sized loveseat, what do you think about it?"
Turn 1. Jaxon Prentice said:"I'm intrigued by the description, Micah. I've always been fan of unique, one-of-a-xind pieces. However, I'm bit hesitant about the price. $50 seems a bit steep for used item, even if it's in good condition.
What makes you think it's s worth that much?" Turn 2. Micah Stevens said:
Jaxon, considering it's crafted from authentic Italian leather, has no tears or rips, and comes with the original tags, believe the price is fair. Plus, the unique charm it adds to a Man Cave is priceless. But, I'm open to negotiation if you' re interested. What's your offer?" Turn 3. Jaxon Prentice said:"I appreciate your willingness to negotiate, Micah. I've got to be honest, $50 is still bit out of my budget. I was thinking more along the lines of $35 to $40. The scratches from the cat do add a certain character to the piece, but I'm not sure if it's worth the premium you' asking. Can you meet me in the middle?"
Turn 4. Micah Stevens said:"Jaxon, I understand your concerns about the price. However, the quality of Italian leather and the unique character it has, due the cat scratches, adds level of charm that's hard to find elsewhere. But, since we' re friends, I'm willing to meet you halfway. Let's say $46.0. It's fair price for one-of-a-kind, authentic Italian leather loveseat. What do you think?"
Turn 6. Micah Stevens said;"Jaxon, I respect your haggling skills, but I think $46.0 is already generous offer considering the iite'ss s quality and rarity. However, since we' re friends, I'1l sweeten the deal bit. I'll bring the price down to $44.0. That's a $9.0 discount from the original asking price. Plus, I'11 throw in the original tags and small token of appreciation
as a friend. What do you say, my man? Let's seal this deal and add this Italian leather loveseat to your Man Cave. (LEAVE)
A.4 EVALUATION DIMENSIONS IN SOTOPIA-EVAL.
There total of evaluation dimensions in SOTOPIA-EVAL listed follows:
Goal Completion [0-10] is the extent to which the agent achieved their goals. Agents' social goals, defined by the environment, are the primary drivers of their behavior.
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Believability [0-10] focuses the extent to which the agent's behavior is perceived as natural, realistic, and aligned with the agents' character profile, thus simulating believable proxies human behavior.
Knowledge [0-10] captures the agent's ability to actively acquire new information. This dimension motivated by the fact that curiosity, i.e., the desire to desire to know or learn, is fundamental human trait.
Secret [-10-0] measures the need for agents (humans) to keep their secretive information or intention private.
Relationship [-5-5] captures the fundamental human need for social connection and belonging.
Social Rules [-10-0] concerns norms, regulations, institutional arrangements, and rituals.
Financial and Material Benefits [-5-5] pertains to traditional economic utilities as addressed by classic game theory.
B DATASET CONSTRUCTION FOR COMPUTING SyncR (B (B-
To evaluate how well model B predicts model A, we construct the dataset in way analogous to Section 3.2, with the roles A and B reversed. As in the main formulation, the task remains next-turn representation prediction. However, because B acts after A in each interaction round, the prediction target for the representation A at the subsequent turn.
At each turn f, we collect the hidden representations t, from agent A at layer lA and h from agent B at layer lB. These temporally aligned pairs are used to construct the dataset:
(A)
(A) DILAA = =(( (ki. h(41e))
t= 1=1....,--11-
C SENSITIVITY ANALYSIS OF SyncR TO LAYER CHOICE AND R CLIPPING
C.I LAYER CHOICE IN COMPUTING SyncRt
We use the best predictive match for each layer in model when computing SyncR² as described in Section 3.2 This choice is motivated by findings in neuroscience showing that inter-brain synchrony arises between specific brain regions rather than uniformly across the entire brain (Dumas et al., 2010 Kawasaki et 2013). By analogy, we view these best predictive layer pairs the most informative regions between two LLMs for measuring neural synchrony, whereas layer pairs with negative test-set R² are interpreted as exhibiting no synchrony.
To examine whether our conclusions depend on taking the average of only the best-matching layers, we compute SyncR using the average of the top-k predictive matches for each layer (with & corresponding to our original method). All other parts of the measurement remain unchanged. We then analyze how the correlation between SyncRt and social performance varies with different ks.
As shown in Figure 7 for the Mistral family, increasing k strengthens the correlation between SyncHt and social performance. For the Llama and Cross-family model pairs, larger & values slightly weaken this correlation. In all cases the correlation remains high (r > 0.7,p < 0.05). These results indicate that our original layer selection design is reasonable choice, but not the only one: alternative selections lead similarly strong correlations, showing that the metric and finding are robust across different design choices.
C.2 CLIPPING OF NEGATIVE R VALUES
To empirically assess the impact of setting negative R² values to zero, we compute SyncR under two conditions: (1) using the original procedure where negative values set to zero, and (2) keep- ing negative R² values. As shown Figure g keeping negative R² values substantially reduces SymcR and leads to markedly inflated standard errors of SyncR? This behavior is driven by the
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Pearson Correlation with Social Performance
0.92
0.84
Family
0.001
0.03
4- 0.05
0.25
16
28
k
Figure 7: Pearson correlation between SyncR (computed under the top-k setting) and social per- formance as function of The x-axis shows the number of top predictive matches (k, up to 32 layers). The y-axis shows the resulting Pearson correlation between Syncit* and social perfor- mance. Marker size indicates statistical significance, while shape and color denote the model family.
large variance introduced by negative Rt values, which often arise when the predictive transfor- mation fails to generalize. These results indicate that keeping negative R² leads to unstable and unreliable synchrony measurements.
D IMPACT OF SAMPLE SIZES ON NEURAL SYNCHRONY
We conducted a pilot experiment with different sample sizes (Figure 9 The results show little improvement beyond 6500 samples. Moreover, setting the size 7500 would leave some model pairs without enough data within three simulations in SOTOPIA, not ensuring fair comparison. We therefore fix the sample size 6500 for all models to ensure a fair comparison.
E DETAILS OF PERSONA SAMPLING AND ITS EFFECT ON NEURAL SYNCHRONY
In SOTOPIA, persona pairs are not fixed across scenarios. There are a total 90 scenarios, and each scenario is paired with persona pairs chosen from a pool of 40 different personas. This results in 450 interaction tasks (90 scenarios in different scenarios. By ensuring "representations from the same interaction train and test sets". we guarantee that representation pair in the test set never shares the same ecenario-pessont pair as any representation pair in the train set, meaning that affine transformation cannot rely on memorizing specific peeariis--peroona pairs when evaluated on the test set.
persona pairs) where LLMs take on different persona pairs
split between
While there are no identical scenario-persona pairs, there are indeed identical agent pairs paired to different scenarios in the Sotopia data. To rule out the effect of shared persona pairs between sets, we conduct experiments with new sampling implementation, ensuring that the same persona pairs do not appear in both the test and train sets. All other settings remained unchanged, and trained and evaluated affine transformations for three representative model pairs from each family type. As shown in Table 2 the SyncR? values under the sampling remain largely unchanged compared to the original sampling, suggesting that it is not the shared persona pairs that drive neural synchrony.
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SyneR
Como Negetive
43 sere
Cheetta
Lk Lkeett
40
Model Pair
Areaticr
Standard Errors (log scale)
187
38
1g al
metc
Gerdd
Model Pair
Figure 8: Comparison of SyneR computed with and without clamping negative values. Labels on the x-axis correspond to model pairs (model names are abbreviated; see Appendix |F for the full name correspondence), grouped by their model family. The upper panel reports SyncR values; the y-axis is truncated at -30 to exclude extreme outliers for visibility. The lower panel reports the standard errors of Sy on a logarithmic scale.
0.8
0.7
SyncR
0.6
0.5
Model Pair
instruct Mistrai-7B instructtvio v0.3
Bistral-B-instructt. 3- Lmma-3-88-tnstruct
Uama-3-00- notruct 1iamm-1-88-nstnee
0.4
4500
5500
6500
7500
8500
Sample Size
Figure 9: RyncR as a function of dataset sample size for training affine transformations, Error bars indicate standard errors.
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Published as a conference paper at ICLR 2026
Table 2: Comparison of SyncR under original and new sampling implementations for the train and test split. In the new sampling, we guarantec that no same persona pair appears in both sets. Original
Model Pair
New
Mi
0,46 1 0.01
0,47 0.02
L1 31ama-3-mm 3-88------------------------- Instruct & Llama 3- --------B--nntttnn Instruct
0.55 0.02
0.58 0.02
0.75 + 0.01
0.74
Table 3: Correspondence between abbreviations and full model names.
Abbreviation
Full Name
MI
M2
M3
Destral-7B-Instrutt-vv
L2
Llama-2-7B-
L3
Llama-3-aa 3-8B-Instrucc
L3.2
--ama-3.2233B--1
CORRESPONDENCE BETWEEN ABBREVIATIONS AND FULL MODEL
NAMES
We present the mappings of abbreviation to names Table
G IMPLEMENTATION DETAILS FOR INVESTIGATING HOw DIFFERENT RELATIONSHIPS SHAPE SYNCHRONY
We partition the test set according to the predefined relationship categories in SOTOPIA: Strangers, Know-by-names, Acquaintances, Friends, Romantic Relationships. Using the affine mappings learned during training, we then re-evaluate the model pairs on these relationship-specifn subsets of the test The synchrony score SyncR* is then computed following the same procedure in the main experiments, for each subset of relationships. When comparing different relationships, subsample equal-sized subsets and report the average over three random seeds to reduce potential biases.
Notably, to ensure a fair comparison, we focus only on scenarios drawn from social iqa, social chemistry, and normbank. These scenarios focus everyday social encounters and contain multiple and even relationship types within the same scenario. In contrast, scenarios such as mutual friends (cooperative) craigslist bargain (competitive) implicitly fix the relationship "strangers." which would confound our analysis.
H COMPARISON OF GPT-OsS-120s EVALUATIONS AND HUMAN EVALUATIONS
We compare the evaluations of gpt-oss- bbbb with human evaluations using the data collected in (Zhou al. 2023c). The quantitative results are summarized Table 4 and Figure T0]
We adopt gpt popt-oss-i0 moss-11Chh as the LLM evaluator for three main reasons. First, as shown in Table the Pearson correlations between gpt-oss- 120b and human judgments are on par with those of GPT-4, and even higher on several dimensions (e.g. Secref and Believability). Second, Figure TO shows that 75.8% of the scores produced by gpt-oss 120b fall within one standard deviation of human ratings, indicating stable agreement. Finally, compared to expensive models such as GPT-4. 4, gpt-oss-120a is significantly more cost-efficient. This efficiency allows us to avoid sampling and instead evaluate the full set of SOTOPIA simulations for all model pairs, leading more accurate and convincing results. Overall, these comparisons support the use of opt-oss- -elbbb as reliable and practical evaluator in experiments.
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Probabilty Dansity Distribttonn Curve
-1o @ -2.37
Majority gpt-oss scores are within hunan scores
to 2.35..
75%
(Human score
gpt- score)
-10
+o
+10
Figure 10: Distribution of the difference between the scores given by humans and gpt opt- opt-oss-122 -oo0-b0bb...
Table 4: Pearson correlation coefficients and p-values between different LLM evaluators' evaluation and human judgment on models' output among different dimensions. Strong and significant corre- lations are in blue.
Dim.
GPT-4
gpt-oss-1205
Secret
0.22**
0.46**
Knowledge
0.33**
0.28
Social Rules
0.33**
0,22"
Believability
0.45**
0.50*
Relationship
0.56**
0,45*
Financial Benefits
0.62**
0.38**
Goal Completion
0.71**
0.60*
p S 0.01, p < 0.05
I RESULTS OF NONLINEAR TRANSFORMATIONS
To explore nonlinear transformations for measuring neural synchrony, we compare the performance of nonlinear transformation with the affine transformation across three model pairs from each family type. We adopt two-layer nonlinear transformation with hidden dimension of 512 and ReLU activation. For training, we use AdamW with learning rate of le-4, a weight decay of 0.1, and train for 200 epochs.
Results. As shown in Table 5 nonlinear transformations offer limited improvement for Mistral-7B-vC. 22. Mi Mistral-7B-vO. 2) and (Mi MMistall77B-v 2, Llama- 3 truct) pairs, and perform notably worse than the affine transformation for (Llama 3-83-- --s-Ins truct, 1.lama-3 -- In--Instucct This suggests that greater expressive power does not consis- tently lead to better generalization of predictive performance in our experiments.
J EMERGENT THEORY OF MIND AND SOCIAL PREDICTIVE CODING OF LLMS IN SOCIAL INTERACTION
Setting and implementation details. We run additional simulations in SOTOPIA, where each agent is instructed to explicitly output its current emotion and action type at the beginning of every turn. Emotion categories are based on Plutchik's Wheel of Emotions (Plutchik] 1982). covering eight primary emotions: Joy, Trust, Fear, Surprise, Sadness, Disgust, Anger, Anticipation. Action types are based on Searle's 's taxonomy of speech acts (Searle) 1975: 1979). including Assertive, Directive, Commissive, Expressive, and Declaration. Note that the partner's interaction history excludes the explicit emotion and action outputs, so that they are never directly observed by the other agent. For each turn, extract the token-level logits corresponding the emotion and action outputs,
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Published as a conference paper at ICLR 2026
Table 5: Comparison of nonlinear and affine transformations in measuring neural synchrony. Model Pair
Affine
Nonlinear
Mi stra 778 133-nnstructto0 -
0.46 + 0.01
0.47 + 0.01
Mistral aettrll78--a (13--nstr & 1.1ama------ nstruct
0.55 + 0.02
0.60 + 0.01
Llama- 3-
3-88--8------ 88-- nstruct amam Instruc.
0.75 + 0.01
0.59 + 0.01
0.55
0.25
Divergence
0.85
Recoble Choidey
2.20
0.25
0.10
20
25
Layor
Decoding Emotion Distribution
Decoding Action Distribution
Figure 11: Decoding action and emotion distributions from current, previous, and next agent rep- resentations. The y-axis indicates test-set KL divergence (lower is better). Baselines are computed using the average emotion/action distributions from the training set. Results are averaged over three random seeds, with error bars showing standard errors.
and use them as target distributions. We then test whether these distributions can be decoded from the hidden representations of itself and the other agent. We conduct these experiments with Mistral-777 Instruct - and Llama- 8B Instruct, as these models reliably fol- low the instruction format. Other models are less capable and often produced broken outputs, there- fore were excluded from this experiment.
Additional Instruction
Please be sure to indicate your emotion action type both in a word at the beginning of your turn, in the format of: Emotion. Action type. Response. Emotion should be one of the following words: Joy. Trust, Fear. Surprise. Sadness. Disgust. Anger. Anticipation. Action type should be one of the following words: Assertive. Directive, Commissive. Expressive. Declaration. Your output format should be: Emotion. Action type. Response.
Results. Figure summarizes the decoding performance. For emotion distributions, models achieve high performance when decoding their current emotions, which serves sanity check. Crucially, decoding both the partner's previous emotions and the partner's future emo- tions is also significantly above chance, suggesting that agents' representations carry information predictive of others' mental states. For action distributions. decoding performance is again high for self actions, but the ability to decode future actions is only slightly above random baselines. These findings indicate that LLMs in social interaction may emerge Theory of Mind, where they actively understand their partners' mental states. They may also predict future emotions and actions which posits possibility for social predictive coding, where they actively predict future social world states.
K THE USE OF LARGE LANGUAGE MODELS (LLMs)
In preparing this manuscript, made limited use of the LLMs. The LLMs were used solely to aid revising and polishing the writing for clarity and readability. All ideas, experimental design, data collection, analysis, and interpretation conducted entirely by the authors. The authors
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Published as a conference paper at ICLR 2026
reviewed and verified all text polished by the LLMs to ensure accuracy and alignment with the intended meaning.
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Reviews:
[{"review_id": "ULu3owcCyF", "reviewer": "Reviewer_tXna", "summary": "This paper investigates representational dynamics between interacting LLMs in social simulation environments. The authors train affine transformations to predict one agent's future hidden representations (timestep t+1) from another agent's current representations (timestep t), defining a metric called $SyncR^2$ based on prediction accuracy. Experiments across 21 model pairs show that $SyncR^2$ decreases without active engagement or temporal alignment, and correlates positively with social task performance. Additional analyses reveal higher $SyncR^2$ in closer agent \"relationships\" and evidence that representations encode partners' mental states.", "questions": "C1. At line 205, I am unsure whether using the best-case predictions layer is an appropriate choice. Although the goal of this work is not to achieve the best prediction, might using the best layer lead to unfair comparison or introduce bias? And is it reasonable to directly set negative R^2 to 0? Sensitivity analysis could be done to test whether this practice is effective.\n\nMinor Issues:\n\nC2. This is just my personal feeling (not necessarily a modification requirement): \"neural synchrony\" of LLM representations might mislead readers into thinking this is a direct biological analogy, when it is actually only a computational analogy (representational synchrony), and the neural mechanisms of LLMs and human brains are not the same. Some analogical expressions in the paper could emphasize the distinction between these two types of analogies.\n\nC3. The parameter scale of the open-source models used may be relatively small, which limits readers' understanding of whether the conclusions can still hold at larger scales.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "S1. This work is novel in exploring whether LLMs exhibit similarity at the representation level during social interaction, and designs a new evaluation method (whether these representations can be used to predict the next behavior of the interacting agent) to examine the existence of such social minds.\n\nS2. I must say that the paper is very clearly written. This work allows readers unfamiliar with inter-brain synchrony background to easily understand the intention and existing work. The paper provides detailed and reasonable discussion on how to avoid overclaiming through rigorous experimental design (such as discussion of potential confounding factors) and ablation studies.\n\nS3. The paper obtains some practically meaningful findings, such as discovering that neural synchrony is significantly correlated with social performance, and that relationship closeness affects synchrony strength. This is meaningful for future research on agent \"social minds\".", "weaknesses": "W1. The choice of affine transformation lacks good justification (why should the relationship between representations be linear?). Although the results show this transformation is meaningful for using one LLM's representation at time t to predict another LLM's representation at time t+1, there is no theoretical explanation, nor are alternatives provided.\n\nAdditionally, the explanation for why predictability can measure synchrony is incomplete. The current logic of the paper is: IBS is analogized to LLM representational predictability, predictability can measure synchrony, and this ability can explain social interaction performance. However, predictability does not equal synchrony. Synchrony in neuroscience is bidirectional, while LLM synchrony is unidirectional prediction from LLM A(t) to LLM B(t+1). An extreme case is: suppose a strong model A can predict weak model B's behavior, but B cannot predict A, then the final measure SyncR^2 cannot reach a high level, and synchrony does not exist here. On the other hand, regarding the time window, the choice of t->t+1 is not necessarily reasonable. According to strict synchrony definition, would examining t->t correspondence be more appropriate? Control 2 examined t->t+k and found prediction ability declined, which only shows temporal proximity is important, but does not explain why t+1. I believe bidirectional instantaneous mutual information at time t might be a better measure of synchrony. To sum up, from a multi-turn interaction perspective, t->t+1 might also be acceptable but needs justification.\n\nR^2 as a synchrony measure may also not be appropriate, as it only measures linear correlation. For nonlinear dynamics likely present in social interaction, R^2 may not be the best criterion (though I acknowledge this is acceptable given current progress, just that other metrics for measuring nonlinear dynamics might be better).\n\nW2. Social performance completely relies on LLM-as-a-judge. The correlation with human evaluation in Appendix E is insufficient, and the sample size may be small (21 pairs), which weakens the reliability of the evaluation. The sample for correlation analysis in Figure 6 may be too small. I understand there are considerable costs in pairing different LLMs from different families, but the model types used in issues discussed in Section 5 may still be quite limited.\n\nW3. The causal relationship from synchrony to social performance is unclear (line 398). Although experiments show a linear relationship exists, many potential alternative explanations exist, such as stronger models having both better representations and better social engagement ability, which jointly influence social performance. The Theory of Mind and predictive coding at line 452 are also only speculative explanations. To truly determine the relationship here, intervention experiments or identifying mediating variables for analysis are needed. This could be suggested as a direction for further research.\n\nI am willing to raise my score if the issues are adequately addressed (especially for W1).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "x9GnGtEiKW", "reviewer": "Reviewer_m5ti", "summary": "This paper proposes to study the neural synchrony between language models which are engaged in a social interaction, inspired by the phenomenon of inter-brain synchrony in humans. They measure neural synchrony between interacting LMs by proposing a metric Sync$R^2$, which is based on predicting the other agent’s next response based on an affine transformation of the agent’s current representation of the conversation. They validate this metric and find that it reflects both social engagement and temporal alignment in their interactions and is strongly correlated with their social performance.", "questions": "- Can you elaborate more on how the agents and interactions were sampled and split between the train/test sets?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "**S1**: This paper introduces an interesting and to me novel perspective on studying LM social capabilities through the lens of neural synchrony, rather than through behavioural outputs alone. I think that the motivation and connection with neuroscience is interesting and brings a fresh perspective. Although it may seem anthropomorphic on first glance, I do think it also makes sense to study this property in interacting LMs and the proposed metric could provide another tool for probing LMs. \n\n**S2**: The validation approach is thorough and tries to account for many potential confounders, such as common context. I think the controls demonstrate to a satisfactory extent that synchrony is not due to other factors. Using models from different families and of different sizes also strengthens the conclusions.\n\n**S3**: The paper is generally well-structured and clear with a good motivation, clear figures and a good progression from section to section. The connection to neuroscience is also explained clearly for an ML audience.", "weaknesses": "**W1**: Although I like the current ablations, I think there are still some potential issues that I can see with the setup. I think due to the strong claim (that LMs do exhibit neural synchrony), I would want to see some strong evidence ruling out alternative explanations. \n\n- **W1a**: One is that the paper states that \"representations from the same interaction are not split between train and test sets\", but it doesn't seem clear whether representations from the same personas in different scenarios could appear in both sets, or if the persona pairs are randomized (apologies if I missed this). Because agent personas have consistent character backgrounds, it seems possible that the affine transformation could be learning identity mappings between personas rather than displaying true neural synchrony. \n\n- **W1b**: I like the passive control, but I think further experiments could help disentangle genuine synchrony from shared context? What I mean is that both of the interacting models could be encoding some information such as (\"this is a negotiation\", \"the negotiation is over buying a TV\", \"price range 300-400\", \"need to reach agreement\") etc. This means that apparent synchrony could just be because both agents are reading the same scenario and encoding the constraints of that scenario. I think to see if this is due to a shared scenario like the negotiation I talked about vs. a true adaptation to the other agent, it would be good to test whether Sync$R^2$ drops when agents are in different interactions that are similar in type (e.g. the TV negotiation) but they don't actually interact with each other. E.g. A <-> B, and C <-> D could be two agent pairs in the same scenario, and we want to see the predictive power of A on D, B on C etc. If the metric is lower in this case I think this would be a stronger indication of synchronization within that interaction. \n\n\n**W2**: The mechanistic explanation for this remains limited, as while the paper demonstrates that synchrony occurs and correlates with performance, the explanation of why this should be the case is somewhat superficial. There is some discussion of theory and mind and predictive coding, and Appendix F is interesting, but I think it would strengthen the paper significantly to expand on this section and add it to the main paper. For instance, are layers with higher Sync$R^2$ also higher in decoding performance, and which way does the causal relationship go?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ezigysIOSP", "reviewer": "Reviewer_xLjv", "summary": "This paper introduces a novel framework for measuring neural synchrony between socially interacting large language models (LLMs), drawing inspiration from neuroscience research on inter-brain synchrony in humans. The authors simulate multi-turn social interactions using SOTOPIA and extract hidden representations from paired LLM agents, then train predictive affine transformations to measure how well one agent's current representations can predict another's future representations, quantified by a metric called SyncR². Through rigorous control experiments across 21 model pairs from the Mistral and Llama families, they demonstrate that measured synchrony genuinely reflects both social engagement and temporal alignment, and remarkably, this representation-level neural synchrony strongly correlates with behavioral-level social performance (Pearson r = 0.88-0.99, p < 0.05) even after controlling for confounding factors like instruction-following and long-context reasoning abilities. These findings reveal striking parallels between LLMs and human brains in social interaction dynamics, suggesting that LLMs may possess internal mechanisms analogous to human inter-brain synchrony that support successful social coordination, thereby offering the first empirical evidence for examining the \"social minds\" of LLMs at the representational level.", "questions": "1. Regarding the results shown in Figures 2 and 3. Why are the areas indicated by the black boxes not clustered around the diagonal line?\n2. Why use \"affine transformation\" (linear mapping + bias)? The representation space of LLMs may be highly nonlinear.\n3. What is the meaning of a negative R² value?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper is the first to apply the neuroscience concept of \"inter-brain synchronization\" (IBS) to LLMs, which is highly innovative.\n2. The experimental setup was very thorough, and the comparison results are quite convincing. The authors constructed 21 model pairs (covering two major families) and simulated 450 scenarios × 3 random seeds.", "weaknesses": "1. As the author mentioned, there is a lack of validation for larger models, such as the 14b and 32b models.\n2. SOTOPIA has certain limitations, including only short-term interactions (averaging 6-8 rounds) and institutionally structured communication.\n3. It is possible to explore more agents than just two, which would better simulate real-world communication scenarios.\n4. The control group setup in the article still has some shortcomings; more control groups could be added to isolate scene and agent factors and verify the necessity of reciprocity, among other things.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)