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arxiv: 2607.01707 · v1 · pith:R23N5BQ5new · submitted 2026-07-02 · 💻 cs.CV

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

Bowen Yuan , Zijian Wang , Yadan Luo , Shijie Wang , Zi Huang This is my paper

Pith reviewed 2026-07-03 16:51 UTC · model grok-4.3

classification 💻 cs.CV
keywords visual forgettinglarge vision-language modelsattention preservationsink suppressionvisual grounding rewardpost-trainingLVLMsattention trajectory
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The pith

LASER uses two rewards to keep large vision-language models focused on visual evidence instead of drifting away during long reasoning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large vision-language models lose access to visual evidence as decoding proceeds because attention moves away from images early on and piles up on a few unhelpful visual tokens. The paper identifies these two patterns through attention analysis and introduces LASER, a post-training method that adds a Visual Grounding Reward to sustain attention on salient image tokens and a Sink Suppression Reward to reduce focus on irrelevant ones. The approach regulates both the overall attention path and the distribution among visual tokens. Experiments show consistent gains over baselines on eight datasets. A sympathetic reader would see this as a direct way to embed visual grounding into the training signal rather than patching it at inference time.

Core claim

The central claim is that visual forgetting in LVLMs stems mainly from early-stage attention decay that blocks evidence acquisition and from attention concentrating on task-irrelevant visual sink tokens, and that applying a Visual Grounding Reward to maintain attention on salient visual tokens together with a Sink Suppression Reward to penalize excessive concentration on sinks during post-training corrects both issues, preserves the attention trajectory, and raises performance on downstream tasks.

What carries the argument

The pair of Visual Grounding Reward and Sink Suppression Reward that together enforce sustained attention on salient visual tokens and limit collapse onto sink tokens.

If this is right

  • The model keeps attention on semantically salient visual tokens across the full decoding sequence.
  • Attention no longer collapses onto a small set of task-irrelevant visual sink tokens.
  • Performance rises above strong baselines on all eight evaluated benchmark datasets.
  • Attention-aware post-training becomes a practical remedy for visual forgetting without altering model architecture.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same reward structure could be tested on models that process longer image sequences or video to check whether early decay is equally dominant.
  • Embedding these rewards might reduce reliance on inference-time attention interventions in other multimodal settings.
  • The method opens a route to compare attention regularization against data curation as ways to improve grounding.
  • If the sink tokens prove consistent across models, a shared suppression term could be developed once rather than per model.

Load-bearing premise

That early attention decay and concentration on sink tokens are the primary drivers of performance loss when visual forgetting occurs.

What would settle it

Training an LVLM with the two rewards yet finding that early-stage attention still decays away from visual tokens or that performance gains disappear on the same eight benchmarks.

Figures

Figures reproduced from arXiv: 2607.01707 by Bowen Yuan, Shijie Wang, Yadan Luo, Zi Huang, Zijian Wang.

Figure 1
Figure 1. Figure 1: Illustration of visual forgetting during long-horizon reasoning in LVLMs. Left: an input image and the corresponding question. Middle: the Visual Attention Proportion (VAP) over generation steps shows that attention to visual tokens peaks early and progressively decays during reasoning. Right: difference in attention maps reveals that later decoding stages could attend less to task relevant visual regions.… view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy on MMStar under visual KV￾cache scaling factor at various reasoning stages. decays [26, 49, 53]. However, exist￾ing characterizations primarily doc￾ument the overall decay of visual at￾tention, without examining its tem￾poral sensitivity or internal alloca￾tion patterns. To uncover these finer￾grained dynamics, we re-examine attention trajectories throughout the reasoning process. Through case stu… view at source ↗
Figure 3
Figure 3. Figure 3: Steps Visualizations of attention maps over reasoning steps. We identify image regions that receive high attention score despite lacking semantic relevance to the task. grounding errors happen at this stage, errors in evidence acquisition propagate autoregressively throughout the remaining reasoning process. At the same time, we observe that the visual attention that persists is not always directed toward … view at source ↗
Figure 4
Figure 4. Figure 4: Analysis of visual sink token attention. Left. Redistributing attention away from sink tokens yields consistent accuracy gains over uniform scaling. Right. Visual sink attention ratio across generation steps for GRPO vanilla vs. GRPO + Rvis. 4.1 Preliminaries We build on Group Relative Policy Optimization (GRPO) [46], which eliminates the value function and estimates advantages by comparing rewards within … view at source ↗
Figure 5
Figure 5. Figure 5: Overview of LASER. Visual token attention is extracted during generation to compute two complementary rewards: Rvis sustains overall VAP across reasoning steps, while Rsupp identifies and suppresses sink tokens to rectify visual attention from visual sink tokens. process consists of two stages. First, we isolate a subset of massive activation dimensions D∗ ⊆ [D], where [D] denotes the full set of hidden st… view at source ↗
Figure 6
Figure 6. Figure 6: Analysis of VAP during generation on various datasets. As reported in Tab. 2, both Rvis and Rsupp yield consistent performance gains across all benchmarks. Specifically, integrating Rvis improves the average score from 51.1 to 52.2 by reducing visual forgetting and maintaining attention on task￾relevant regions. The subsequent addition of Rsupp further improves performance, particularly in general reasonin… view at source ↗
Figure 7
Figure 7. Figure 7: Left. Visual sink token attention across generation steps for model trained by LASER. Right. Response length during training process. 5.4 Further Analysis LASER preserves visual attention throughout generation [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: A qualitative comparison between Qwen2.5-VL-Instruct and LASER on a visual perception task. Premature visual disengagement in the base model leads to an erroneous inference, while LASER maintains visual attention and answers correctly. example, the base Qwen2.5-VL-Instruct model makes an erroneous summarization in its early-stage reasoning chain, resulting in an incorrect answer after long￾horizon reasonin… view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative results of LASER [PITH_FULL_IMAGE:figures/full_fig_p028_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative results of LASER [PITH_FULL_IMAGE:figures/full_fig_p029_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative results of LASER [PITH_FULL_IMAGE:figures/full_fig_p030_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative results of LASER [PITH_FULL_IMAGE:figures/full_fig_p031_12.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that visual forgetting in LVLMs during long-horizon decoding is largely driven by two factors identified via systematic empirical analysis: early-stage attention decay that disrupts evidence acquisition, and attention concentration on task-irrelevant visual sink tokens. Motivated by this, it introduces LASER, a post-training framework using two rewards—a Visual Grounding Reward to maintain attention on semantically salient visual tokens and a Sink Suppression Reward to penalize excessive concentration on sinks—resulting in consistent outperformance over strong baselines on eight benchmark datasets.

Significance. If the empirical analysis and results hold under scrutiny, the work would demonstrate that attention-aware post-training can effectively mitigate visual forgetting in LVLMs, offering a practical remedy beyond heuristic reminders or post-hoc fixes. The dual-reward design targeting both trajectory and distribution is a targeted contribution if the causal drivers are isolated.

major comments (2)
  1. [Abstract] Abstract: the central motivation rests on the claim that performance degradation 'is largely driven' by early-stage attention decay and sink concentration, identified through 'systematic empirical analysis.' However, the abstract supplies no description of the analysis (e.g., whether it includes controlled interventions that isolate these factors while holding model, prompt, and decoding fixed, versus purely observational attention-map vs. accuracy correlations). This is load-bearing because the two rewards are directly derived from these factors; if the analysis is only correlational, the rewards may target symptoms rather than primary causes.
  2. [Abstract] Abstract: the claim of 'consistent outperformance on eight benchmark datasets' is presented without any quantitative results, baseline specifications, ablation studies, dataset sizes, or error analysis. This prevents assessment of effect sizes or whether the gains are robust, directly undermining evaluation of the central claim that the proposed rewards are an 'effective remedy.'
minor comments (1)
  1. [Abstract] Abstract: 'visual sink tokens' is used without a definition or citation to prior work on attention sinks; this notation should be clarified on first use.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback on our manuscript. We address each major comment point by point below, focusing on the abstract's presentation of our empirical analysis and results. Where revisions to the abstract can improve clarity without exceeding length constraints, we indicate this explicitly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central motivation rests on the claim that performance degradation 'is largely driven' by early-stage attention decay and sink concentration, identified through 'systematic empirical analysis.' However, the abstract supplies no description of the analysis (e.g., whether it includes controlled interventions that isolate these factors while holding model, prompt, and decoding fixed, versus purely observational attention-map vs. accuracy correlations). This is load-bearing because the two rewards are directly derived from these factors; if the analysis is only correlational, the rewards may target symptoms rather than primary causes.

    Authors: The abstract is a high-level summary, but the full manuscript details the systematic empirical analysis in Section 3. There, we conduct controlled interventions that hold the model, prompts, and decoding strategy fixed while isolating the effects of early-stage attention decay (via attention trajectory interventions) and sink token concentration (via targeted masking experiments), directly measuring causal impacts on accuracy. This establishes the factors as primary drivers rather than mere correlations, motivating the dual-reward design. We will revise the abstract to briefly note that the analysis involves such controlled interventions. revision: yes

  2. Referee: [Abstract] Abstract: the claim of 'consistent outperformance on eight benchmark datasets' is presented without any quantitative results, baseline specifications, ablation studies, dataset sizes, or error analysis. This prevents assessment of effect sizes or whether the gains are robust, directly undermining evaluation of the central claim that the proposed rewards are an 'effective remedy.'

    Authors: Abstracts are space-constrained and conventionally omit specific numbers, tables, and detailed ablations, which are instead provided in the main text (Section 4, Table 1, and ablation studies in Section 4.3) along with dataset details and error analysis. These elements demonstrate the consistent gains and robustness. We can partially revise the abstract to include one or two key quantitative highlights (e.g., average improvement margins) if space permits, while preserving its summary nature. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical post-training method with independent motivation

full rationale

The provided abstract and description contain no equations, fitted parameters, or derivations. The two rewards are explicitly introduced as new constructs motivated by (but not defined in terms of) the empirical observations. No self-citation chains, uniqueness theorems, or ansatzes are referenced. The central claim rests on experimental validation rather than any reduction of outputs to inputs by construction. This is the most common honest non-finding for applied post-training papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no free parameters, axioms, or invented entities are described in sufficient detail to populate the ledger.

pith-pipeline@v0.9.1-grok · 5742 in / 1158 out tokens · 19651 ms · 2026-07-03T16:51:33.010940+00:00 · methodology

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