Message capacity and claim wording set the transition points of collective truth-finding in language-model networks
Whether human or large language model (LLM), an agent in a discussion reads only a few of the others' contributions, bounded by cognition, context, or cost. LLM collectives can settle on a wrong consensus even when a majority starts out correct; we ask how far that reading bound alone decides the outcome. We model the bound with one number, the message capacity, which sets how many of the others' messages an agent reads, and generate the communication network from it. Over 31,824 randomized queries, we found that an 8-billion-parameter model's judgment of a claim effectively reduces to a logistic function of a weighted sum of its inbox, the update rule of a stochastic binary neuron with d...
| Authors | Makoto Fukushima |
|---|---|
| Primary category | cs.MA |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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