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arXiv Computation and Language By Niclas Lietzow, Danielle Bitterman, Carsten Eickhoff, William Rudman, Michal Golovanevsky

Vision-Default, Prior-Override: Causal Mechanisms of Perception-Knowledge Conflict in Vision-Language Models

Read the original on arXiv Computation and Language →

The paper investigates how vision‑language models resolve conflicts between visual evidence and memorized knowledge. Using activation patching and ablation across residual streams, attention heads, and MLP sublayers, the authors find that visual grounding is the default behavior, while prior knowledge relies on a small, causally essential set of attention heads concentrated in the network’s second half. Ablating these heads flips knowledge‑grounded predictions to visually grounded ones in most cases, revealing an asymmetric causal structure and a sparse circuit of routing and writing heads that underlies perception‑knowledge conflict.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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