MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Natural Language Processing · Depth: Expert, extended

Summary

MultiToP is a novel multimodal-context-aware visual token patching framework designed to mitigate hallucinations in Video Large Multimodal Models (VideoLMMs). This framework refines unreliable visual tokens prior to language generation using a lightweight Visual Token Patcher. The patcher predicts token-level replacement distributions and substitutes misleading tokens with a dynamic global patch token. Its training incorporates information-guided rank calibration, leveraging answer-conditioned frame-level cues from the VideoLMM backbone, alongside ground-truth answer supervision and sparsity regularization. MultiToP operates without modifying the original VideoLMM, introducing negligible inference overhead. Experiments show it boosts Qwen3-VL-4B-Instruct's F1 score on Vript-HAL by 50.60% and improves Video-LLaVA-7B's ActivityNet-QA accuracy by 18.58%, while preserving general video understanding and demonstrating robustness across model scales.

Key takeaway

For AI Scientists and Machine Learning Engineers deploying VideoLMMs, MultiToP offers a compelling strategy to mitigate hallucinations without costly model retraining or significant inference overhead. You should consider integrating this token-level visual evidence refinement approach to enhance model reliability and factual consistency. This method improves performance on hallucination benchmarks like Vript-HAL while preserving general video understanding, making it ideal for applications demanding high accuracy and efficiency. Be aware that the current fixed replacement ratio might require tuning for optimal performance across diverse video content.

Key insights

Hallucinations in VideoLMMs can be mitigated by selectively patching unreliable visual tokens before language generation.

Principles

• Repairing misleading visual tokens can mitigate hallucinations.
• VideoLMM-derived frame-level cues guide token replacement.
• Relative ranking objectives are robust to noisy attention.

Method

MultiToP employs a Visual Token Patcher to predict replacement distributions and generate a dynamic global patch token. It's optimized via cross-entropy, information-guided rank calibration, and sparsity regularization.

In practice

• Refine visual tokens at the token-level pre-generation.
• Derive information cues from answer-to-visual attention.
• Use Gumbel-Softmax for differentiable token replacement.

Topics

• Video Large Multimodal Models
• Hallucination Mitigation
• Visual Token Patching
• Qwen3-VL-4B-Instruct
• Computational Efficiency
• Video Understanding

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer

Related on AIssential

• MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models — MultiToP patches unreliable visual tokens in Video-LMMs to reduce hallucinations while preserving general understanding.
• MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs — MHSA mitigates LVLM hallucinations by correcting cross-modal attention patterns via a lightweight MLP generator.
• P\textsuperscript{2}-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization — P²-DPO reduces LVLM hallucination by self-generating on-policy visual preference pairs and using a calibration loss.
• Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models — SSCD mitigates VideoLLM hallucinations by using deliberately disrupted spatiotemporal and semantic negative features in contrastive decoding.
• VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck — VIB-Probe uses Variational Information Bottleneck theory to detect and mitigate VLM hallucinations by analyzing internal attention head outputs.
• PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment — Aligning multimodal reasoning processes with visual evidence, not just final answers, is crucial to prevent hallucinations.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.