Shows that reply length decides whether user pushback helps or harms LLM answers, probes hidden states to predict caving, and argues sycophancy evals should allow full replies and report self-correction.
Traces sycophancy across LLM training stages and finds SFT establishes most of it, not preference alignment, using interpretability probes on training data to support the analysis.
Introduces mechanistic shaping, which trains models through their internal representations to be right for the right reasons, reducing sycophancy, reward hacking and confounder use, and studies effects on monitorability.