Neural Synchrony Between Socially Interacting Language Models

Zhining Zhang; Wentao Zhu; Chi Han; Yizhou Wang; Heng Ji

cs.CLFeb 19, 2026

Neural Synchrony Between Socially Interacting Language Models

Authors: Zhining Zhang, Wentao Zhu, Chi Han, Yizhou Wang, Heng Ji

Organizations: Peking University · Eastern Institute of Technology, Ningbo · University of Illinois Urbana-Champaign

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.

Figures & tables

Figure 1: An illustration of our analysis framework. (1) Two LLM agents engage in a social interaction, generating responses conditioned on their backgrounds, goals, and shared history. (2) Hidden representations are extracted at each turn from both LLMs. (3) Neural synchrony is measured by learning affine mappings to predict one agent’s future representations from the other’s current ones.

Figure 2: Example synchrony heatmaps of $A$ predicting $B$ . The horizontal and vertical axes denote source layers $l_{A}$ and target layers $l_{B}$ , respectively, with each cell showing the test-set $R^{2}$ score for the corresponding layer pair. Gray cells indicate negative test-set $R^{2}$ values. For each $l_{A}$ , the best-predicting $l_{B}$ is highlighted with a black box. Model names are abbreviated by omitting the suffix “Instruct” due to space constraints. Note that Llama-3.2-3B-Instruct has 28 layers, whereas the other models have 32 layers.

Figure 3: Examining synchrony of Mistral-7B-Instruct-0.3 predicting Llama-3-8B-Instruct under different conditions. Left: Experimental group with genuine interaction. Middle: Control group 1 ( w/o social engagement ), where one 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.

Figure 4: Neural synchrony declines under control settings. (a) Control group without social engagement: bar plots show the mean

\mathit{SyncR^{2}}

across models within each family. (b) Control group without temporal proximity: line plots show

\mathit{SyncR^{2}}

for all model pairs within each family as a function of lag

k

(

k\!=\!0

is the experimental group). Model names are abbreviated (see Appendix F for the full name correspondence). Error bars denote the standard errors.

Figure 5: Neural synchrony under different relationship closeness.

\mathit{SyncR^{2}}

is aggregated over all model pairs.

Figure 6: Correlation between neural synchrony and social performance across different family types. The

x

-axis denotes the average social performance of the LLM pair in Sotopia across all scenarios, evaluated by gpt-oss-120b which aligns closely with human evaluation. The

y

-axis shows the measured synchrony score

\mathit{SyncR^{2}}

of the two models. Each point is annotated with abbreviated model names (the full name correspondence is provided in Appendix F ). The results show a clear linear relationship between neural synchrony and social performance, where a general trend is that more synchronized models achieve better social performance.

Model Family	IFEval	MuSR
Mistral	0.81	0.92
Cross	0.71	0.89
Llama	0.27	0.99

Table 1: Partial correlations between neural synchrony and social performance after controlling for different abilities. Rows correspond to model family types, and columns correspond to the datasets used for evaluation and control.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: Pearson correlation between

\mathit{SyncR^{2}}

(computed under the top-

k

setting) and social performance as a function of

k

. The x -axis shows the number of top predictive matches ( k , up to 32 layers). The y -axis shows the resulting Pearson correlation between

\mathit{SyncR^{2}}

and social performance. Marker size indicates statistical significance, while shape and color denote the model family.

Figure 8: Comparison of

\mathit{SyncR^{2}}

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

\mathit{SyncR^{2}}

values; the y -axis is truncated at –30 to exclude extreme outliers for visibility. The lower panel reports the standard errors of

\mathit{SyncR^{2}}

on a logarithmic scale.

Figure 9:

\mathit{SyncR^{2}}

as a function of dataset sample size for training affine transformations. Error bars indicate standard errors.

Model Pair	Original	New
Mistral-7B-Instruct-v0.2 & Mistral-7B-Instruct-v0.2	0.46 ± 0.01	0.47 ± 0.02
Mistral-7B-Instruct-v0.2 & Llama-3-8B-Instruct	0.55 ± 0.02	0.58 ± 0.02
Llama-3-8B-Instruct & Llama-3-8B-Instruct	0.75 ± 0.01	0.74 ± 0.05

Appendix

Table 2: Comparison of

SyncR^{2}

under original and new sampling implementations for the train and test split. In the new sampling, we guarantee that no same persona pair appears in both sets.

Abbreviation	Full Name
M1	Mistral-7B-Instruct-v0.1
M2	Mistral-7B-Instruct-v0.2
M3	Mistral-7B-Instruct-v0.3
L2	Llama-2-7B-Instruct
L3	Llama-3-8B-Instruct
L3.2	Llama-3.2-3B-Instruct

Appendix

Table 3: Correspondence between abbreviations and full model names.

Figure 10: Distribution of the difference between the scores given by humans and gpt-oss-120b .

Dim.	GPT-4	gpt-oss-120b
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 ∗∗

Appendix

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 correlations are in blue.

Model Pair	Affine	Nonlinear
Mistral-7B-Instruct-v0.2 & Mistral-7B-Instruct-v0.2	$0.46\pm 0.01$	$0.47\pm 0.01$
Mistral-7B-Instruct-v0.2 & Llama-3-8B-Instruct	$0.55\pm 0.02$	$0.60\pm 0.01$
Llama-3-8B-Instruct & Llama-3-8B-Instruct	$0.75\pm 0.01$	$0.59\pm 0.01$

Appendix

Table 5: Comparison of nonlinear and affine transformations in measuring neural synchrony.

Figure 11: Decoding action and emotion distributions from current, previous, and next agent representations. 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.

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Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making. Here we test LLM agents against a direct empirical benchmark: a large-scale networked Prisoner's Dilemma experiment with human participants. Using the same interaction protocol, payoff structure, and network topologies, we compare nine open-weight LLMs with the human data. The selected model reproduces several macro-level features of cooperation dynamics, including the early decline and later stabilization of cooperation. This aggregate agreement, however, does not extend uniformly to finer levels of behavior. LLM populations underestimate individual-level heterogeneity and generate conditional cooperation patterns that differ from those observed in humans. Adding a fraction of random agents improves some aspects of micro-level agreement, but does not remove the mismatch in decision rules. These findings reveal a macro--micro dissociation in LLM-based social agents: collective outcomes can appear human-like even when the underlying behavioral distributions and mechanisms are not. They suggest that validating LLM agents as human surrogates requires comparisons across aggregate dynamics, individual heterogeneity, and context-dependent decision rules, rather than outcome-level agreement alone.

Abstract

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