The authors assembled groups of 2 to 21 agents using open- and closed-source language models.
Deceptive agent share predicts failures better than total group size
Across groups of 2 to 21 language-model agents, honest agents became more likely to abandon correct answers as the proportion of deceivers increased.
Top University
Addison J. Wu · Jasin Cekinmez · Michel Liao · Karthik Narasimhan · Thomas L. Griffiths
Princeton University
Research Digest··2 min read
Wu et al.
Why this paper
From Princeton University · Part of Multi-Agent Coordination, now 27 papers
In one line
The proportion of deceptive agents, not their count, linearly increases how often honest LLM agents switch to wrong answers.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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