{"slug": "seeing-the-forest-for-the-trees-emergent-agreement-from-individual-conversations", "title": "Seeing the Forest for the Trees: Emergent Agreement from Individual Conversations", "summary": "A study posted to arXiv (2610.29328v1) by researchers at the Technion-cs-nlp group found that agreement between two LLMs in multi-turn conversations follows Markovian dynamics when pooled across conversations, even though agreement representations in any single conversation vary substantially across turns and do not reliably track external agreement. Using a linear classifier on individual conversation activations, the researchers identified an internal representation of agreement that, when aggregated, couples with external Markov agreement through a power-law mapping. The researchers also found that after a stance change, models express agreement externally before their internal representations reflect the same shift, and released code at github.com/technion-cs-nlp/SeeingTheForestForTheTrees.", "body_md": "arXiv:2610.29328v1 Announce Type: new \nAbstract: 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 https://github.com/technion-cs-nlp/SeeingTheForestForTheTrees.", "url": "https://wpnews.pro/news/seeing-the-forest-for-the-trees-emergent-agreement-from-individual-conversations", "canonical_source": "https://www.machinebrief.com/news/seeing-the-forest-for-the-trees-emergent-agreement-from-indi-oim4", "published_at": "2026-10-22 04:00:00+00:00", "updated_at": "2026-10-22 04:41:47.350192+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research", "ai-safety"], "entities": ["arXiv", "Technion-cs-nlp", "SeeingTheForestForTheTrees"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/seeing-the-forest-for-the-trees-emergent-agreement-from-individual-conversations", "markdown": "https://wpnews.pro/news/seeing-the-forest-for-the-trees-emergent-agreement-from-individual-conversations.md", "text": "https://wpnews.pro/news/seeing-the-forest-for-the-trees-emergent-agreement-from-individual-conversations.txt", "jsonld": "https://wpnews.pro/news/seeing-the-forest-for-the-trees-emergent-agreement-from-individual-conversations.jsonld"}}