Silent Dissent: LLM Agents That Yield to the Majority Still Represent Their Original Premise
Read the original on arXiv Computation and Language →The paper investigates whether large language model (LLM) agents that yield to a unanimous majority in multi‑agent debate truly change their underlying premise or merely adjust their statement. Using two‑hop factual questions with an unstated intermediate entity, the authors show that agents that concede still encode the original bridge in their internal representations, as revealed by a Jacobian‑lens analysis, even when the majority answer is wrong. Experiments across several open‑weight models demonstrate that hiding or removing the agent’s earlier answer increases conformity and that the original premise can be recovered from the question alone.
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