Principled Agent Debate: Adversarial Arbitration for Sycophancy Reduction in Large Language Models

arXiv:2606.07532v1 Announce Type: cross Abstract: RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process. We present Principled Agent Debate (PAD), a multi-agent architecture that mitigates identity-framed sycophancy by arbitrating between two models tuned to opposing philosophical dispositions, with a pragmatist synthesizer evaluating both arguments blind to their origins. This paper evaluates a prompt-based instantiation of PAD. The key mechanisms are static dispositional tuning, identity stripping before synthesis, single-

Source: arXiv cs.AI — read the full report at the original publisher.

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