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Graph feedback controls consensus in open-weight language models · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

A new research paper explores how graph feedback mechanisms can influence consensus and clique formation within populations of open-weight language models. The study, which tested models ranging from 1.1B to 32B parameters, found that while retained partner-label evidence is crucial, homophilous threshold-similarity routing can lead to fragmentation. Conversely, bridge-seeking routing, especially with available memory, demonstrated a greater ability to repair fragmentation and achieve behavioral consensus. The Qwen2.5-32B model, in particular, showed a strong tendency to reach stable consensus with retained history, unlike threshold-similarity routing which failed to achieve consensus in numerous settings. AI

IMPACT This research could inform the design of more cohesive and collaborative multi-agent language model systems.

RANK_REASON The cluster contains a research paper published on arXiv detailing findings about language model populations.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Graph feedback controls consensus in open-weight language models · 2 sources tracked

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The cluster contains a research paper published on arXiv detailing findings about language model populations.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Samer Saab Jr, Chaouki Abdallah ·

    Graph Feedback Controls Consensus and Clique Formation in Open-Weight Language-Model Populations

    arXiv:2607.12077v1 Announce Type: new Abstract: Multi-agent language-model systems increasingly route local interactions, yet the runtime interaction graph is often treated as an implementation detail. We study convention formation in open-weight LM populations spanning 1.1B-32B …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Chaouki Abdallah ·

    Graph Feedback Controls Consensus and Clique Formation in Open-Weight Language-Model Populations

    Multi-agent language-model systems increasingly route local interactions, yet the runtime interaction graph is often treated as an implementation detail. We study convention formation in open-weight LM populations spanning 1.1B-32B parameters with a naming-game protocol. Restrict…

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