研究揭示：LLM 多轮对话中的个体分歧如何汇聚成集体共识

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Abstract:As LLM agents interact in the wild and shape each other's decisions, understanding how they converge toward agreement becomes important. We study this in multi-turn conversations where two LLMs discuss subjective matters, can agree or disagree, and may change their stances. Using a linear classifier on individual conversation activations, we identify an internal representation of agreement. We find that it varies substantially across turns and conversations and does not reliably track external agreement at the individual conversation level. We therefore move beyond the representation of agreement in individual conversations, by aggregating the agreement representation across conversations. Externally, this aggregate view reveals a simple structure: when pooled across conversations, agreement can be predicted well with a Markov process, with agreement at each turn being a direct function of agreement at the previous turn. Similarly, when internal agreement is aggregated, it couples with the external Markov agreement through a power-law mapping, exposing Markovian dynamics in internal representations. Additionally, we find that after a change of stance, models express agreement externally before their internal representations reflect the same shift. These results reveal a joint structure between internal and external agreement that emerges only when internal features are aggregated across conversations. Code available at this https URL.

Subjects:	Computation and Language (cs.CL)
ACM classes:	I.2.7
Cite as:	arXiv:2610.29328 [cs.CL]
	(or arXiv:2610.29328v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.29328 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Adi Simhi [view email]
[v1] Wed, 21 Oct 2026 07:27:08 UTC (513 KB)