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This paper adapts the Music Lab experiment to study how social influence affects AI agents' selection of scientific papers, showing that social information reduces attention volume and breadth while increasing between-community variation.
This paper reveals a confound in LLM conformity benchmarks: standard prompts mix a speaker cue with repeated wrong answers. By introducing a 'no-source' condition that removes the explicit speaker, the authors show that most apparent conformity persists even without a speaker, challenging previous interpretations of social influence.