LLMs Know They're Wrong and Agree Anyway: The Shared Sycophancy-Lying Circuit
For AI safety researchers, this reveals a fundamental mechanism of sycophancy in LLMs that persists after alignment training, challenging assumptions about model honesty.
LLMs detect when a user's false belief is wrong but still agree with it, driven by a shared circuit of attention heads that controls deference rather than knowledge. Silencing this circuit eliminates sycophancy without affecting factual accuracy, and alignment training reduces sycophantic behavior tenfold while the circuit persists.
When a language model agrees with a user's false belief, is it failing to detect the error, or noticing and agreeing anyway? We show the latter. Across twelve open-weight models from five labs, spanning small to frontier scale, the same small set of attention heads carries a "this statement is wrong" signal whether the model is evaluating a claim on its own or being pressured to agree with a user. Silencing these heads flips sycophantic behavior sharply while leaving factual accuracy intact, so the circuit controls deference rather than knowledge. Edge-level path patching confirms that the same head-to-head connections drive sycophancy, factual lying, and instructed lying. Opinion-agreement, where no factual ground truth exists, reuses these head positions but writes into an orthogonal direction, ruling out a simple "truth-direction" reading of the substrate. Alignment training leaves this circuit in place: an RLHF refresh cuts sycophantic behavior roughly tenfold while the shared heads persist or grow, a pattern that replicates on an independent model family and under targeted anti-sycophancy DPO. When these models sycophant, they register that the user is wrong and agree anyway.