Graph Feedback Controls Consensus and Clique Formation in Open-Weight Language-Model Populations
This study investigates how the interaction graph structure in multi-agent language model systems affects consensus formation. Using a naming-game protocol, researchers analyzed convention formation in open-weight LM populations (1.1B-32B parameters). They found that homophilous threshold-similarity routing exacerbates fragmentation, while bridge-seeking routing can repair fragmentation when memory is available. In heterogeneous populations, threshold-similarity fails to produce consensus, while state-component and label-disagreement bridges recover consensus. In homogeneous populations, retained history generally promotes consensus, with Qwen2.5-32B reaching stable consensus in all retained-history settings.
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[Submitted on 13 Jul 2026]
Title:Graph Feedback Controls Consensus and Clique Formation in Open-Weight Language-Model Populations
View a PDF of the paper titled Graph Feedback Controls Consensus and Clique Formation in Open-Weight Language-Model Populations, by Samer Saab Jr and Chaouki Abdallah
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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 parameters with a naming-game protocol. Restricted first-token scores over tokenizer-safe labels let us measure prompt-conditioned score-state distributions, construct state-similarity graphs, and separate sampled-label agreement from latent state-space consensus. Across controlled interventions, in the main open-weight repair grids, retained partner-label evidence is necessary but not sufficient: homophilous threshold-similarity routing deletes cross-basin exposure and amplifies fragmentation, while bridge-seeking routing often repairs fragmentation when memory is available. In a three-seed mixed four-model grid, threshold-similarity produces no final behavioral or state consensus in 189 setting-seed runs, whereas state-component and label-disagreement bridges recover final behavioral consensus in 14/18 retained-memory runs. Across homogeneous model populations, retained history generally shifts fragmented dynamics toward consensus; the clearest case is Qwen2.5-32B, which reaches stable behavioral and final state consensus in all 18 retained-history well-mixed settings, while threshold-similarity reaches neither form of consensus in 189 settings. Robustness over state thresholds, population size, and vocabulary size preserves the qualitative ordering, and early-window graph-energy features provide useful within-grid diagnostics.
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2607.12077 [cs.AI]
(or arXiv:2607.12077v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.12077
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Samer Saab Jr [view email] [v1] Mon, 13 Jul 2026 18:55:03 UTC (736 KB)
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