CAER: Conflict-Aware Evidence Routing with Dual Prefix Experts for Multimodal Large Language Models
Quick Answer
The CAER framework enhances multimodal large language models by detecting visual-language conflicts and enabling conflict-aware generation through a dual-prefix expert routing mechanism.
Quick Take
It effectively improves reliability on benchmarks like MMMC and the new AgriConflict dataset without modifying backbone parameters.
Key Points
- CAER introduces a span-grounded evidence router for fine-grained conflict estimation.
- The dual-prefix expert routing mechanism learns separate experts for supported and contradicted inputs.
- CAER improves multimodal without updating backbone parameters.
- Experiments show effectiveness on public benchmarks and a new dataset.
- The framework addresses hallucinations in multimodal understanding and generation.
DeepSignal Analysis
What happened
The CAER framework introduces a method for detecting conflicts between visual and textual inputs in multimodal large language models (MLLMs). It utilizes a dual-prefix expert routing mechanism to enhance conflict-aware generation, improving reliability on benchmarks without altering backbone parameters.
Key evidence
- CAER is a backbone-agnostic framework designed for visual-language conflict detection and conflict-aware generation.
- The framework employs a span-grounded evidence router to transform claim representations into soft textual queries, retrieving evidence from frozen visual tokens.
- Experiments on the MMMC benchmark and the new AgriConflict dataset show that CAER improves the reliability of open-source MLLMs without updating their backbone parameters.
Why it matters
The ability to detect and manage conflicts between visual and textual data is crucial for the reliability of MLLMs. Traditional methods often fall short in fine-grained conflict localization. CAER's approach could enhance the performance of various applications relying on multimodal understanding, such as content generation and information retrieval.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in multimodal understanding and generation. However, when textual inputs conflict with visual evidence, they still suffer from hallucinations and produce responses inconsistent with visual content. Existing approaches mainly rely on decoding strategies, additional training, verification methods, or prompting techniques, but often lack fine-grained conflict localization and conflict-aware generation. In this work, we propose CAER, a backbone-agnostic framework for visual-language conflict detection and conflict-aware generation. CAER introduces a span-grounded evidence router that transforms claim representations into soft textual queries and retrieves corresponding evidence from frozen visual tokens, enabling fine-grained conflict estimation. Furthermore, we design a dual-prefix expert routing mechanism that learns separate experts for visually supported and contradicted inputs, enabling conflict-aware generation through explicit expert selection. Experiments on the public MMMC benchmark and our newly curated AgriConflict dataset demonstrate that CAER effectively detects visual-language conflicts and improves the reliability of open-source MLLMs without updating their backbone parameters.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.28991 [cs.CV] |
| (or arXiv:2607.28991v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.28991 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zixuan Liu [view email]
[v1]
Fri, 31 Jul 2026 03:38:12 UTC (1,319 KB)
— Originally published at arxiv.org
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