mlsanityData to 14 Oct 2026

Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats

arXiv 2511.17254 ↗First posted 21 Nov 2025cs.CV cs.AIcited by 6

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

Abstract (arXiv)

Despite their impressive performance across a wide range of tasks, Large Vision-Language Models (LVLMs) remain prone to hallucination. In this study, we propose a comprehensive intervention framework aligned with the transformer's causal architecture in LVLMs, integrating the effects of different intervention paths on hallucination. We find that hallucinations in LVLMs do not arise from a single causal path, but rather from the interplay among image-to-input-text, image-to-output-text, and text-to-text pathways. For the first time, we also find that LVLMs rely on different pathways depending on the question-answer alignment format. Building on these insights, we propose simple yet effective methods to identify and intervene on critical hallucination heads within each pathway, tailored to discriminative and generative formats. Experiments across multiple benchmarks demonstrate that our approach consistently reduces hallucinations across diverse alignment types.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 69 of its 85 references that are in the map. First posted on arXiv 21 Nov 2025.

Builds on

69 of 85 references.

Closest ancestors · 19

Same direction, newest first

  1. 2025Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context23 Oct
  2. 2024Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens23 Nov
  3. 2024ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models22 Nov
  4. 2024Mitigating Object Hallucination via Concentric Causal Attention21 Oct
  5. 2024Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality7 Oct
  6. 2024Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs31 Jul
  7. 2024Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models16 Jul
  8. 2024Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models28 May
  9. 2024Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding27 Mar
  10. 2024Multi-Modal Hallucination Control by Visual Information Grounding20 Mar
  11. 2024PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset17 Mar
  12. 2024Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models18 Feb
7 more
  1. 2023Hallucination Augmented Contrastive Learning for Multimodal Large Language Model12 Dec
  2. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding28 Nov
  3. 2023Woodpecker: Hallucination Correction for Multimodal Large Language Models24 Oct
  4. 2023Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models9 Oct
  5. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models1 Oct
  6. 2023Detecting and Preventing Hallucinations in Large Vision Language Models11 Aug
  7. 2023Evaluating Object Hallucination in Large Vision-Language Models17 May

Other references · 50

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
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  12. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,565
38 more
  1. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  3. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Modelscited by 1,652
  4. 2023Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understandingcited by 1,471
  5. 2023Shikra: Unleashing Multimodal LLM's Referential Dialogue Magiccited by 906
  6. 2023Aligning Large Multimodal Models with Factually Augmented RLHFcited by 712
  7. 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledgecited by 710
  8. 2018Object Hallucination in Image Captioningcited by 669
  9. 2023MiniGPT-v2: large language model as a unified interface for vision-language multi-task learningcited by 625
  10. 2022Contrastive Decoding: Open-ended Text Generation as Optimizationcited by 609
  11. 2023How Language Model Hallucinations Can Snowballcited by 311
  12. 2023DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Modelscited by 128
  13. 2019Dynamic Graph Attention for Referring Expression Comprehensioncited by 111
  14. 2023Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animatorcited by 74
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  17. 2020Graph-Structured Referring Expression Reasoning in The Wildcited by 58
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  19. 2023Contrastive Grouping with Transformer for Referring Image Segmentationcited by 54
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  21. 2023EdaDet: Open-Vocabulary Object Detection Using Early Dense Alignmentcited by 44
  22. 2023Neuron-Level Knowledge Attribution in Large Language Modelscited by 37
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  24. 2019Cubic LSTMs for Video Predictioncited by 32
  25. 2023LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Modelscited by 29
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  27. 2019Relationship-Embedded Representation Learning for Grounding Referring Expressionscited by 25
  28. 2024Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentationcited by 23
  29. 2025MVTokenFlow: High-quality 4D Content Generation using Multiview Token Flowcited by 17
  30. 2025Rethinking Query-based Transformer for Continual Image Segmentationcited by 14
  31. 2025Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancementcited by 13
  32. 2024Part2Object: Hierarchical Unsupervised 3D Instance Segmentationcited by 12
  33. 2023Spatial and Visual Perspective-Taking via View Rotation and Relation Reasoning for Embodied Reference Understandingcited by 11
  34. 2024Understanding Multimodal LLMs: the Mechanistic Interpretability of Llava in Visual Question Answeringcited by 11
  35. 2024The devil is in the object boundary: towards annotation-free instance segmentation using Foundation Modelscited by 10
  36. 2024Plain-Det: A Plain Multi-Dataset Object Detectorcited by 9
  37. 2023Grounded Image Text Matching with Mismatched Relation Reasoningcited by 8
  38. 2025Free on the Fly: Enhancing Flexibility in Test-Time Adaptation with Online EMcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Modelscited by 3
  2. 2026RefAny3D: 3D Asset-Referenced Diffusion Models for Image Generationcited by 2
  3. 2026Mitigating Hallucinations in Large Vision-Language Models via Causal Route Gatingcited by 2
  4. 2026Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucinationcited by 1
  5. 2026HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Modelscited by 0
  6. 2026Beyond Attention Imbalance: Mitigating Hallucinations via Spectral Surgerycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation
  2. 2024Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence
  3. 2024Reducing Hallucinations in Vision-Language Models via Latent Space Steering
  4. 2026What Makes LVLMs Hallucinate Less? Unveiling the Architectural Factors Behind Hallucination Robustness
  5. 2026Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations
  6. 2026Dynamic Alignment Compensation for Hallucination Mitigation in Large Vision-Language Models
  7. 2026Reducing Object Hallucination in LVLMs via Emphasizing Image-negative Tokens
  8. 2024A Survey on Hallucination in Large Vision-Language Models