Beyond Fixed Targets: Context-Adaptive Subspace Navigation for Mitigating Object Hallucinations in LVLMs

Large vision-language models (LVLMs) generate fluent responses but often hallucinate objects that are absent from the input image. Existing subspace interventions reduce hallucinations by moving subspace components toward a target that remains fixed across inputs, but this uniform correction can also suppress mentions of real objects, creating a trade-off between hallucination reduction and object recall. To address this challenge, we propose **S**ubspace-**C**onstrained **A**nchor **N**avigation, termed SCAN, which retains a shared subspace but replaces the fixed target with an anchor adapted to each context. Specifically, SCAN separates the hidden state into a component within the hallucination-associated subspace and a residual orthogonal to it. Using only this residual as context, a lightweight Navigator predicts an anchor and per-direction strengths that control how far the subspace component moves toward the anchor, leaving the residual unchanged. With the LVLM backbone and subspace basis frozen, the Navigator is trained on preference pairs for captioning and object existence judgments to discourage hallucinated mentions while preserving object recall. Extensive experiments across multiple tasks and LVLM backbones demonstrate that SCAN consistently reduces object hallucinations and achieves a favorable hallucination–recall trade-off while preserving decoding efficiency.