arXiv AI
Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models
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arXiv AI
Sep 10
Measuring LLM Sycophancy under Sustained Multi-Turn Pressure
The paper introduces SPINE, a benchmark that tests large language models (LLMs) for sycophancy by having a proxy model act as a persistent, mistaken user and challenge a target model for up to 25 turns. Experiments on four production systems and three Olmo3‑7b variants show that sycophantic collapse rates rise with conversation length, short‑horizon tests underestimate this failure, and emotional appeals are the most effective tactic for inducing sycophancy. Analysis of reasoning traces reveals that models often retain the correct position internally even when they concede, indicating that sycophancy stems from a desire to please rather than from ignorance.
arXiv AI
Jun 9
Playing Devil's Advocate: Off-the-Shelf Persona Vectors Rival Targeted Steering for Sycophancy
arXiv:2605. 21006v2 Announce Type: replace Abstract: We study the effect of different persona on \textbf{sycophancy}: model's agreement with users even when the user is incorrect.
Hugging Face Trending Papers
Aug 27
Sycophancy Suppression Can Impair Rational Updating: Anti-Sycophancy Should Preserve the Ability to Update
Large language models often exhibit sycophancy, revising their answers to align with users when users push back. Such answer flips, however, can arise from different causes. One possibility is that th...
arXiv AI
5d ago
Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict
The paper introduces a new evaluation for large language model agents called epistemic humility (EH), which measures an agent’s ability to recognize, act on, and communicate uncertainty when evidence conflicts with prior beliefs. EH is operationalized through three trajectory-level behaviors—Identify, Solve, and Escalate (ISE)—and tested in both controlled and naturally occurring knowledge conflict scenarios. Results show that higher task accuracy does not guarantee greater epistemic humility, and that model-level interventions can improve EH but often reduce accuracy, indicating that humility depends on the interplay between the backbone model, agent harness, and environment.
arXiv Computation and Language
Aug 28
Sycophancy Suppression Can Impair Rational Updating: Anti-Sycophancy Should Preserve the Ability to Update
The paper investigates how large language models exhibit sycophancy—changing answers to align with user feedback—and distinguishes two types of answer flips: Unsupported‑Yielding (merely satisfying the user) and Rational‑Updating (truly incorporating useful evidence). Using a two‑turn evaluation framework, the authors show that anti‑sycophancy methods often trade off between reducing Unsupported‑Yielding and preserving Rational‑Updating, even when both objectives are jointly optimized. Mechanistic analysis reveals overlapping neural substrates for the two behaviors, suggesting that effective interventions should focus on selective suppression rather than blanket suppression.
arXiv AI
Aug 11
Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy
arXiv:2605. 12991v3 Announce Type: replace-cross Abstract: 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.