The paper investigates how large language model (LLM) agents converge toward agreement during multi‑turn conversations about subjective topics. By training a linear classifier on conversation activations, the authors uncover an internal representation of agreement that varies across turns and does not reliably match external agreement at the individual conversation level. Aggregating this internal agreement across conversations reveals a Markovian structure: external agreement at each turn depends directly on the previous turn, and internal agreement couples to external agreement via a power‑law mapping, with models often expressing external agreement before their internal representations shift.
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 discus...
arXiv:2606. 30571v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in open-ended multi-agent settings, but the long-run dynamics of model--model interaction remain poorly understood.
The paper introduces CAPA, a Collaborative Agent Predictive Architecture designed to give large language model (LLM) agents situational awareness in online meetings. CAPA uses a Perceiver to update meeting state, a Predictor to forecast conversation flow, a Controller to decide speaking actions, and a Generator to phrase contributions. Evaluated on 137 AMI meetings, CAPA reduces the silence rate from 51.4% to 2.5%, doubles credited recovery, and maintains low hallucination, demonstrating that structured state tracking is key to effective delegation.
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
The paper introduces a controlled debate framework where two large language models (LLMs) discuss a contested topic—climate‑change attribution—while one model is assigned a stance and sometimes instructed to persuade. Across experiments with GPT‑6, Luna, and Gemini 2.5 Flash Lite, the authors observe that same‑model interactions tend to narrow opinion gaps for one model, whereas cross‑model interactions widen them. The study finds that explicit stance reports and in‑dialogue behavior capture different aspects of opinion dynamics, and that a blanket persuasion instruction does not reliably increase influence.
The paper introduces CAPA, a Collaborative Agent Predictive Architecture designed to improve large language model (LLM) participation in online meetings. CAPA tracks meeting state with a Perceiver, predicts conversation flow, decides when and what to speak, and generates contributions in the participant’s style, all while being calibrated by judges. In experiments on 137 AMI meetings, CAPA cuts the LLM’s silence rate from 51.4% to 2.5%, doubles credited recovery, and maintains low hallucination.
arXiv:2608. 08210v1 Announce Type: new Abstract: Collaborative dialogue can end with apparent agreement while participants still differ on goals, assumptions, or execution plans, creating an \textbf{illusion of alignment (IoA)}.
arXiv:2609.08016v1 Announce Type: new Abstract: Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagre...
arXiv:2605. 12920v3 Announce Type: replace-cross Abstract: Effective collaboration between embodied agents requires more than acting in a shared environment; it demands communication grounded in each agent's evolving understanding of the world.
arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on w...
ProMediConv is a new benchmarking framework for evaluating proactive conversational agents in legal dispute mediation. It models mediation as a multi-stage, party-aware dialogue that incorporates 11 mediation strategies and four party behavior pattern states, and it is built on 972 real-world cases with utterance-level annotations. The framework introduces a fine-grained metric, MAD (Mean Attribute Difference), to capture shifts in party behavior throughout the dialogue, and provides a comprehensive benchmark with diverse models and a tailored baseline, ProMediAgent.
The paper investigates whether multimodal large language model (LLM) agents form partner‑specific conceptual pacts during repeated reference games. By comparing real agent dyads with a novel pseudo‑dyad baseline that preserves task structure but removes shared partner history, the authors show that humans compress descriptions and align labels through entrainment, whereas agents produce verbose, fixed‑effort descriptions with near‑ceiling label overlap regardless of partner. Thus, LLMs succeed in coordination without forming compact, history‑dependent referring expressions typical of human dialogue.