Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models
Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied primarily in single-turn settings. This paper investigates a critical question: does subjecting LLMs to greater interaction scaffolding make sycophancy better or worse? Across 4,800 veracity judgments (200 statements $\times$ 6 models $\times$ 4 conditions), we find that the interaction scaffolding characteristic of agentic systems (feedback loops, reconsideration checkpoints, and iterative refinement) systematically amplifies sycophantic behavior. Multi-turn interaction, user pressure, and iterative self-refinement each provide additional opp...
| Authors | Thantham Jittham |
|---|---|
| Primary category | cs.CL |
| Published | 6 July 2026 |
| Last updated at source | 1 September 2026 |
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