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Headlines · 1

1. Nov 3
   Networked LLM Agents Form Polarized Opinion Groups

   A study by researchers from Tsinghua University and partner institutions, published in Nature Communications, built a network of 1,000 LLM agents and tested GPT-3.5, GPT-4o, ChatGLM, Llama-3 and DeepSeek-V3. Without prescribed behavioral rules, agents formed opposing opinion groups, increasingly connected with similar-minded agents and showed self-inconsistency between their stated views, generated reasons and shared information. The researchers released code and quantified the effects of five interventions. In the polarized system, network-level changes had limited effects, while eliminating confirmation bias and neutral elite signals reduced polarization by 11.8% and 8.8%, respectively. A 36Kr article was an authorized republication of the New Zhiyuan report, not independent confirmation. The researchers cautioned that the simulation does not predict real-world populations and may be affected by the selected models, prompts and simplified opinion representation.

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