Revis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models

• URL: http://arxiv.org/abs/2602.11824v1
• Date: Thu, 12 Feb 2026 11:07:44 GMT
• Title: Revis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models
• Abstract summary: REVIS is a training-free framework designed to explicitly re-activate suppressed visual information.<n>We show that REVIS reduces object hallucination rates by approximately 19% compared to state-of-the-art baselines.
• Score: 18.624825577944854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the advanced capabilities of Large Vision-Language Models (LVLMs), they frequently suffer from object hallucination. One reason is that visual features and pretrained textual representations often become intertwined in the deeper network layers. To address this, we propose REVIS, a training-free framework designed to explicitly re-activate this suppressed visual information. Rooted in latent space geometry, REVIS extracts the pure visual information vector via orthogonal projection and employs a calibrated strategy to perform sparse intervention only at the precise depth where suppression occurs. This surgical approach effectively restores visual information with minimal computational cost. Empirical evaluations on standard benchmarks demonstrate that REVIS reduces object hallucination rates by approximately 19% compared to state-of-the-art baselines, while preserving general reasoning capabilities.

Related papers

• Look Clearly Before Answering: Mitigating Hallucinations in LVLMs via Saliency-Driven Perceptual Realignment [45.92240827409312]
  Saliency-Driven Perceptual Realignment (SDPR) is a training-free framework that mitigates the degradation of visual awareness throughout inference.<n>Our proposed SDPR is robust against hallucinations due to its holistic alignment of visual awareness across the entire generative trajectory.
  arXiv  Detail & Related papers  (2026-07-18T14:39:16Z)
• Information-Regularized Attention for Visual-Centric Reasoning [30.245558374164492]
  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.
  arXiv  Detail & Related papers  (2026-07-01T04:45:41Z)
• When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs [54.411658510110215]
  We propose HalluScope to better understand the extent to which different factors induce hallucinations.<n>HalluVL-DPO is a framework for fine-tuning off-the-shelf LVLMs towards more visually grounded responses.
  arXiv  Detail & Related papers  (2026-04-23T17:54:36Z)
• VCE: A zero-cost hallucination mitigation method of LVLMs via visual contrastive editing [70.82867621856968]
  Large vision-language models (LVLMs) frequently suffer from Object Hallucination (OH)<n>Recent studies suggest that the hallucination problem may stem from language priors.<n>We propose Visual Contrastive Editing (VCE), a novel post-hoc method that identifies and suppresses hallucinatory tendencies.
  arXiv  Detail & Related papers  (2026-04-21T12:40:07Z)
• 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)
• SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision [59.61988843996952]
  Style-Aware Visual Early Revision SAVER is a novel mechanism that dynamically adjusts LVLMs' final outputs based on the token-level visual attention patterns.<n>We show that SAVER achieves state-of-the-art performance in hallucination mitigation across various models, datasets, and tasks.
  arXiv  Detail & Related papers  (2025-08-05T07:41:25Z)
• CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models [60.0300765815417]
  Large Vision-Language Models (LVLMs) frequently produce content that deviates from visual information, leading to object hallucination.<n>We propose Caption-sensitive Attention Intervention (CAI), a training-free, plug-and-play hallucination mitigation method.
  arXiv  Detail & Related papers  (2025-06-30T07:52:36Z)
• ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs [98.27348724529257]
  We introduce ViCrit (Visual Caption Hallucination Critic), an RL proxy task that trains VLMs to localize a subtle, synthetic visual hallucination injected into paragraphs of human-written image captions.<n>Models trained with the ViCrit Task exhibit substantial gains across a variety of vision-language models benchmarks.
  arXiv  Detail & Related papers  (2025-06-11T19:16:54Z)
• Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations [11.474045796965056]
  Large Vision-Language Models (LVLMs) have achieved remarkable success but continue to struggle with object hallucination (OH)<n>We propose VaLSe, a Vision-aware Latent Steering framework that adopts an interpretation-then-mitigation strategy to address OH in LVLMs.
  arXiv  Detail & Related papers  (2025-05-23T12:29:00Z)
• OViP: Online Vision-Language Preference Learning for VLM Hallucination [44.14029765850719]
  Large vision-language models (LVLMs) remain vulnerable to hallucination, often generating content misaligned with visual inputs.<n>We propose an Online Vision-language Preference Learning framework that dynamically constructs contrastive training data based on the model's own hallucinations.
  arXiv  Detail & Related papers  (2025-05-21T19:26:09Z)
• Poison as Cure: Visual Noise for Mitigating Object Hallucinations in LVMs [7.920981206857122]
  Large vision-language models (LVMs) extend large language models (LLMs) with visual perception capabilities.<n>A major challenge compromising their reliability is object hallucination that LVMs may generate plausible but factually inaccurate information.<n>We propose a novel visual adversarial perturbation (VAP) method to mitigate this hallucination issue.
  arXiv  Detail & Related papers  (2025-01-31T14:31:00Z)
• VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decoding [38.23310445372371]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal task reasoning.<n>They often generate responses that appear plausible yet do not accurately reflect the visual content, a phenomenon known as hallucination.<n>Recent approaches have introduced training-free methods to mitigate hallucinations by adjusting the decoding strategy during the inference stage.<n>We propose a novel hallucination-mitigation method from the visual encoding perspective: textbfVisutextbfal textbfLayer Fustextbfion Contrastive textbfD
  arXiv  Detail & Related papers  (2024-11-24T13:42:02Z)
• CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs [74.36850397755572]
  CATCH addresses issues related to visual defects that cause diminished fine-grained feature perception and cumulative hallucinations in open-ended scenarios. It is applicable to various visual question-answering tasks without requiring any specific data or prior knowledge, and generalizes robustly to new tasks without additional training.
  arXiv  Detail & Related papers  (2024-11-19T18:27:31Z)
• Reducing Hallucinations in Vision-Language Models via Latent Space Steering [34.1755878632361]
  Hallucination poses a challenge to the deployment of large vision-language models (LVLMs) in applications. We introduce Visual and Textual Intervention (VTI), a novel technique designed to reduce hallucinations by steering latent space representations during inference to enhance the stability of vision features.
  arXiv  Detail & Related papers  (2024-10-21T08:42:30Z)
• From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models [15.401221354325672]
  Hallucinations in large vision models (LVLMs) are a significant challenge, i.e., generating objects that are not presented in the visual input.<n>Recent studies often attribute hallucinations to a lack of understanding of visual input, yet ignore a more fundamental issue: the model's inability to extract or decouple visual features.<n>In this paper, we revisit the hallucinations in LVLMs from an architectural perspective, investigating whether the primary cause lies in the visual encoder (feature extraction) or the modal alignment module (feature decoupling)
  arXiv  Detail & Related papers  (2024-10-09T11:46:32Z)
• Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs [52.497823009176074]
  Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations.<n>We introduce Visual Description Grounded Decoding (VDGD), a training-free method designed to enhance visual perception and improve reasoning capabilities in LVLMs.
  arXiv  Detail & Related papers  (2024-05-24T16:21:59Z)

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.