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

• URL: http://arxiv.org/abs/2607.01707v1
• Date: Thu, 02 Jul 2026 04:59:56 GMT
• Title: LASER: A Corrective Lens for LVLMs via Visual Attention Preservation and Sink Suppression
• Abstract summary: Large vision-language models (LVLMs) exhibit strong reasoning ability but suffer from visual forgetting during long-horizon decoding.<n>We propose LASER, a post-training framework that regulates both the visual attention trajectory and intra-visual token attention distribution during reasoning.<n>Experiments on eight benchmark datasets demonstrate that LASER consistently outperforms strong baselines.
• Score: 50.454633334898396
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large vision-language models (LVLMs) exhibit strong reasoning ability but suffer from visual forgetting during long-horizon decoding, where attention progressively drifts away from visual evidence. Existing methods largely treat this issue as a late-stage attention decay problem or attempt to mitigate it through heuristic reminders or post-hoc attention lifting. Through systematic empirical analysis, we find that performance degradation under visual forgetting is largely driven by two overlooked factors: early-stage attention decay disrupts evidence acquisition, and attention concentration on a subset of task-irrelevant visual sink tokens. Motivated by these insights, we propose LASER, a post-training framework that regulates both the visual attention trajectory and intra-visual token attention distribution during reasoning. Technically, LASER introduces two complementary rewards: a Visual Grounding Reward, which encourages the model to maintain attention on semantically salient visual tokens throughout decoding, and a Sink Suppression Reward, which penalizes excessive attention concentration on visual sink tokens. Together, these rewards preserve early-stage grounding while preventing attention collapse onto uninformative regions. Extensive experiments on eight benchmark datasets demonstrate that LASER consistently outperforms strong baselines, validating attention-aware training as an effective remedy for visual forgetting.

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