LASER: A Corrective Lens for LVLMs via Visual Attention Preservation and Sink Suppression

Large vision–language models (LVLMs) exhibit strong reason-ing ability but suffer from visual forgetting during long-horizon decoding,where attention progressively drifts away from visual evidence. Existingmethods largely treat this issue as a late-stage attention decay prob-lem or attempt to mitigate it through heuristic reminders or post-hocattention lifting. Through systematic empirical analysis, we find thatperformance degradation under visual forgetting is largely driven bytwo overlooked factors: early-stage attention decay disrupts evidenceacquisition, and attention concentration on a subset of task-irrelevantvisual sink tokens. Motivated by these insights, we propose LASER, apost-training framework that regulates both the visual attention tra-jectory and intra-visual token attention distribution during reasoning.Technically, LASER introduces two complementary rewards: a VisualGrounding Reward, which encourages the model to maintain attentionon semantically salient visual tokens throughout decoding, and a SinkSuppression Reward, which penalizes excessive attention concentration onvisual sink tokens. Together, these rewards preserve early-stage groundingwhile preventing attention collapse onto uninformative regions. Exten-sive experiments on eight benchmark datasets demonstrate that LASERconsistently outperforms strong baselines, validating attention-awaretraining as an effective remedy for visual forgetting. The code is availableat https://github.com/KeViNYuAn0314/LASER.