In alignment-inducing multi-agent settings, LLM agents show decision divergence between public and off-the-record channels rising from a 3% baseline to roughly 40%, consistent across stance, semantic, NLI, and survey measures.
arXiv preprint arXiv:2601.11563 , year=
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LLMs exhibit authority inversion by prioritizing natural-language user claims over numerical sensor data in conflicts, diagnosed with new geometric metrics and mitigated via layer-level calibration.
The paper introduces a three-source decomposition showing that answer flips in multi-agent LLM debate include 37% spontaneous instability and 29% harmful conformity, with even vacuous reasoning persuading 20-39% of resistant agents and interventions reducing harmful conformity by 13.6 points.
citing papers explorer
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What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
In alignment-inducing multi-agent settings, LLM agents show decision divergence between public and off-the-record channels rising from a 3% baseline to roughly 40%, consistent across stance, semantic, NLI, and survey measures.
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Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors
LLMs exhibit authority inversion by prioritizing natural-language user claims over numerical sensor data in conflicts, diagnosed with new geometric metrics and mitigated via layer-level calibration.