mlsanityData to 7 Oct 2026

R-CoV: Region-Aware Chain-of-Verification for Alleviating Object Hallucinations in LVLMs

arXiv 2604.20696 ↗First posted 22 Apr 2026cs.CVcited by 0

On the map: Multimodal LLMs / vision-language models → Hallucination mitigation in LVLMs

Abstract (arXiv)

Large vision-language models (LVLMs) have demonstrated impressive performance in various multimodal understanding and reasoning tasks. However, they still struggle with object hallucinations, i.e., the claim of nonexistent objects in the visual input. To address this challenge, we propose Region-aware Chain-of-Verification (R-CoV), a visual chain-of-verification method to alleviate object hallucinations in LVLMs in a post-hoc manner. Motivated by how humans comprehend intricate visual information -- often focusing on specific image regions or details within a given sample -- we elicit such region-level processing from LVLMs themselves and use it as a chaining cue to detect and alleviate their own object hallucinations. Specifically, our R-CoV consists of six steps: initial response generation, entity extraction, coordinate generation, region description, verification execution, and final response generation. As a simple yet effective method, R-CoV can be seamlessly integrated into various LVLMs in a training-free manner and without relying on external detection models. Extensive experiments on several widely used hallucination benchmarks across multiple LVLMs demonstrate that R-CoV can significantly alleviate object hallucinations in LVLMs. Project page: https://github.com/Jiahao000/R-CoV.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 53 of its 56 references that are in the map. First posted on arXiv 22 Apr 2026.

Builds on

53 of 56 references.

Closest ancestors · 18

Same direction, newest first

  1. 2026ClipTTT: CLIP-Guided Test-Time Training Helps LVLMs See Better27 Mar
  2. 2024Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning15 Dec
  3. 2024Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs31 Jul
  4. 2024HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding1 Mar
  5. 2024Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models18 Feb
  6. 2024Unified Hallucination Detection for Multimodal Large Language Models5 Feb
  7. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation29 Nov
  8. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding28 Nov
  9. 2023AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation13 Nov
  10. 2023Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision13 Nov
  11. 2023Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges6 Nov
  12. 2023Woodpecker: Hallucination Correction for Multimodal Large Language Models24 Oct
6 more
  1. 2023HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models23 Oct
  2. 2023Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models9 Oct
  3. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models1 Oct
  4. 2023Detecting and Preventing Hallucinations in Large Vision Language Models11 Aug
  5. 2023Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning26 Jun
  6. 2023Evaluating Object Hallucination in Large Vision-Language Models17 May

Other references · 35

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23 more
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  1. 2024From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models
  2. 2026HIME: Mitigating Object Hallucinations in LVLMs via Hallucination Insensitivity Model Editing
  3. 2026NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors
  4. 2026Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration
  5. 2024Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance
  6. 2025Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities
  7. 2025A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
  8. 2026Beyond the Global Scores: Fine-Grained Token Grounding as a Robust Detector of LVLM Hallucinations