# Study finds LLM group agreement can ignore network structure

> A study of eight language models found that their shared biases and message framing mattered more than network connections in simulated group discussions.

Oossa · 2026-10-30 · https://oossa.com/en/study-finds-llm-group-agreement-can-ignore-network-structure

An arXiv study reports that the structure of a network did little to predict where simulated groups of language models would end up agreeing. Researchers measured 73,080 controlled exchanges across eight instruction-tuned models, then used the results in simulations of 10,000 agents.

The models sometimes shifted opinions beyond the range of views they had seen, and some groups converged on objectively false claims, the researchers say. In their simulations, each model’s internal bias and how messages were framed mattered more than a network measure called algebraic connectivity.

## The facts

- The study measured 73,080 exchanges across eight models.
- Its simulations modeled populations of 10,000 agents.

## Why it matters

Teams using multiple language models to discuss claims should not assume that changing how the models are connected will prevent shared errors; the study suggests model biases and message framing may still drive agreement.

## Sources & references

1. [Network Topology Does Not Matter: The Breakdown of Classical Consensus in LLM Populations](https://arxiv.org/abs/2610.37699) – arXiv cs.MA, 2026-10-30

Last updated: 2026-10-30