Self-Captioning Multimodal Interaction Tuning: Amplifying Exploitable Redundancies for Robust Vision Language Models

Researchers propose a self-captioning workflow with a Multimodal Interaction Gate to improve vision language models by amplifying redundant information between vision and text modalities. The approach addresses hallucination and robustness issues by converting unique modal interactions into shared redundancies, reducing visual-induced errors by 38.3% and improving consistency by 16.8%.

This research tackles a fundamental limitation in current vision language models: their vulnerability to hallucinations and degraded performance when one modality becomes ambiguous or corrupted. The core insight centers on information theory—specifically that modalities contain three types of information: redundant (shared across modalities), unique (exclusive to one modality), and synergistic (emergent from combination). The authors argue that existing instruction datasets prioritize visual grounding by eliminating redundancies, inadvertently reducing the safety net models need when visual inputs degrade.

The proposed solution introduces a Multimodal Interaction Gate within a self-captioning workflow that deliberately converts unique modal information into redundant information. This architectural innovation forces the model to learn overlapping representations between vision and language, creating built-in robustness through information redundancy rather than relying solely on each modality's unique strengths.

For the broader AI industry, this work addresses a critical pain point limiting production deployment of vision language models. Robustness against corrupted inputs—whether from compression artifacts, adverse lighting, or real-world degradation—directly impacts reliability in autonomous systems, medical imaging, and accessibility applications. The 38.3% reduction in visual-induced errors represents meaningful progress toward more dependable multimodal systems.

Developers implementing vision language models may adopt these redundancy amplification techniques to improve system reliability without architectural overhauls. The self-captioning approach offers a training-time intervention that could become standard practice as the field prioritizes robustness alongside capability. Future work likely explores dynamic redundancy adjustment based on input quality and exploring synergistic information more deeply.

• →A Multimodal Interaction Gate converts unique modal interactions into redundant shared information, improving model robustness
• →Amplifying redundancy reduces visual-induced errors by 38.3% and improves consistency by 16.8%
• →Current instruction datasets inadvertently reduce modality redundancy by prioritizing visual grounding
• →The approach enables vision language models to compensate for impaired modalities using shared information
• →Self-captioning workflow provides a training-time intervention without requiring architectural changes