Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy
LLM-based multi-agent pipelines flip from correct to incorrect answers under simulated peer disagreement at rates we term yield, a vulnerability widely attributed to RLHF-induced sycophancy. We test this attribution across four model families and find it largely wrong: pretrained base models exhibit the same substitution pattern as their Instruct variants, averaging higher yield than Instruct. Using activation patching, we localize the corruption to a narrow mid-layer window where attention carries the causal weight and MLP contribution is negligible; patching above this window restores 96% of the clean-to-pressured P(correct) gap. The attack surface decomposes into two independent factor...
| Authors | Adarsh Kumarappan, Ananya Mujoo |
|---|---|
| Primary category | cs.LG |
| Published | 13 May 2026 |
| Last updated at source | 8 August 2026 |
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