Information-Regularized Attention for Visual-Centric Reasoning

• URL: http://arxiv.org/abs/2607.00434v1
• Date: Wed, 01 Jul 2026 04:45:41 GMT
• Title: Information-Regularized Attention for Visual-Centric Reasoning
• Abstract summary: Vision-language models (VLMs) have become a paradigm for multimodal learning, yet remain unstable due to object hallucination, weak visual grounding, and catastrophic forgetting.<n>We introduce Information-Regularized Attention (IRA), a attention mechanism that explicitly regulates the amount of visual information injected into the hidden states of intermediate transformer layers.
• Score: 30.245558374164492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) have become a paradigm for multimodal learning, yet remain unstable due to object hallucination, weak visual grounding, and catastrophic forgetting after full-parameter instruction tuning. We claim these failures result from a lack of explicit control over visual representation learning during the standard next-token prediction objective. As a result, visual embeddings thus become passively optimized and prone to injecting redundant or spurious signals. To counter this, we introduce Information-Regularized Attention (IRA), a stochastic attention mechanism that explicitly regulates the amount of visual information injected into the hidden states of intermediate transformer layers. This local reparameterization translates uncertainty about visual representations into local noise that is independent across data points. Beyond evaluating model performance, we also quantify embedding properties, where IRA produces smoother curvature trajectories and suppresses attention-sink across all layers, indicating a more stable transformation of the visual signal. Our results suggest that stochastic attention is not merely a regularizer but a key contributor to representation learning in a generative architecture, offering a new direction for building more reliable VLMs.

Related papers

• Disentangling Semantic Attention from Structural Bias in the Attention Manifold [61.68044165974505]
  Multimodal Large Language Models (MLLMs) exhibit disproportionate attention toward certain semantically uninformative visual tokens.<n>We introduce Saliency-guided Purification and Adaptive Redistribution (SPAR), a training-free, plug-and-play intervention.
  arXiv  Detail & Related papers  (2026-07-27T05:30:19Z)
• Diagnosing Visual Ignorance in Vision-Language Models [29.2851901986069]
  Vision-Language Models (VLMs) frequently rely on language priors, producing confident answers that are weakly grounded in visual evidence.<n>In this work, we study language-prior reliance from both mechanistic and behavioral perspectives.<n>We find that language-prior reliance is a systematic routing failure affecting both model internals and benchmark validity.
  arXiv  Detail & Related papers  (2026-06-05T04:16:01Z)
• Persistent Visual Memory: Sustaining Perception for Deep Generation in LVLMs [66.15429821530503]
  Persistent Visual Memory is a lightweight learnable module designed to strengthen sustained, on-demand access to visual evidence.<n>Experiments on Qwen3-VL models demonstrate that PVM brings notable improvements with negligible parameter overhead.<n>In-depth analysis reveals that PVM shows improved robustness in longer generations and accelerates internal prediction convergence.
  arXiv  Detail & Related papers  (2026-05-01T17:54:37Z)
• Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation [31.028607494171336]
  Vision-Language Models (VLMs) are frequently undermined by object hallucination.<n>We introduce Positive-and-Negative Decoding (PND), a training-free inference framework.<n>PND intervenes directly in the decoding process to enforce visual fidelity.
  arXiv  Detail & Related papers  (2026-04-27T12:23:00Z)
• From Attenuation to Attention: Variational Information Flow Manipulation for Fine-Grained Visual Perception [12.4823697158657]
  Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in general visual understanding.<n>They frequently falter in fine-grained perception tasks that require identifying tiny objects or discerning subtle visual relationships.<n>We attribute this limitation to Visual Attenuation: a phenomenon where sparse fine-grained visual signals are prematurely suppressed or diluted by dominant textual tokens during network propagation.<n>Existing input-centric solutions fail to fundamentally reverse this intrinsic mechanism of information loss.<n>We propose the Variational Information Flow (VIF) framework to address this challenge.
  arXiv  Detail & Related papers  (2026-04-14T09:32:13Z)
• Focus Matters: Phase-Aware Suppression for Hallucination in Vision-Language Models [8.304027910542446]
  We investigate the internal attention dynamics of vision encoders in Large Vision-Language Models (LVLMs)<n>Our analysis reveals that hallucination behavior is particularly sensitive to tokens receiving low attention during the focus phase.<n>We propose a lightweight inference-time intervention that selectively suppresses such tokens during the focus phase.
  arXiv  Detail & Related papers  (2026-04-04T02:46:58Z)
• Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigation [51.743225614196774]
  Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language reasoning.<n>They remain vulnerable to hallucination, where generated content deviates from visual evidence.<n>Recent vision enhancement methods attempt to address this issue by reinforcing visual tokens during decoding.<n>We propose Adaptive Visual Reinforcement (AIR), a training-free framework for MLLMs.
  arXiv  Detail & Related papers  (2026-02-27T14:18:51Z)
• ReViP: Reducing False Completion in Vision-Language-Action Models with Vision-Proprioception Rebalance [50.05984919728878]
  We present ReViP, a novel VLA framework with Vision-Proprioception Rebalance to enhance visual grounding and robustness under perturbations.<n>Specifically, we use an external VLM as a task-stage observer to extract real-time task-centric visual cues from visual observations.<n>To evaluate false completion, we propose the first False-Completion Benchmark Suite built on LIBERO with controlled settings such as Object-Drop.
  arXiv  Detail & Related papers  (2026-01-23T11:31:07Z)
• Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent [72.1517476116743]
  Recent MLLMs have shown emerging visual understanding and reasoning abilities after being pre-trained on large-scale multimodal datasets.<n>Existing approaches, such as direct fine-tuning and continual learning methods, fail to explicitly address this issue.<n>We introduce a novel perspective leveraging effective rank to quantify the degradation of visual representation forgetting.<n>We propose a modality-decoupled gradient descent (MDGD) method that regulates gradient updates to maintain the effective rank of visual representations.
  arXiv  Detail & Related papers  (2025-02-17T12:26:34Z)
• Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval [51.53892300802014]
  We show that supervised neural information retrieval models are prone to learning sparse attention patterns over passage tokens. Using a novel targeted synthetic data generation method, we teach neural IR to attend more uniformly and robustly to all entities in a given passage.
  arXiv  Detail & Related papers  (2022-04-24T22:36:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.