Diagnosing the Reference Gap: What Visual Scaffolds Can and Cannot Fix in Multimodal Reasoning
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper argues that multimodal LLM failures on multi-step visual reasoning, such as losing track of counted objects, stem from a reference gap, meaning difficulty binding language to relevant visual entities, rather than perception. Motivated by visual routines theory, the authors test whether external references like numbering narrow this gap. They introduce ReGap-Bench, a paired benchmark of five visual reasoning tasks where only the reference varies. Task-matched references improve accuracy by up to 25%, while mismatched references give little benefit or hurt performance.
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The paper argues that multimodal LLM failures on multi-step visual reasoning, such as losing track of counted objects, stem from a reference gap, meaning difficulty binding language to relevant visual entities, rather than perception. Motivated by visual routines theory, the authors test whether external references like numbering narrow this gap. They introduce ReGap-Bench, a paired benchmark of five visual reasoning tasks where only the reference varies. Task-matched references improve accuracy by up to 25%, while mismatched references give little benefit or hurt performance.
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Key findings from the stored brief
- Multimodal models can recognize visual content yet struggle with reliable multi-step reasoning over it.
- ReGap-Bench pairs five visual reasoning tasks where scene, question, and answer are fixed and only the reference varies.
- Task-matched external references such as numbering improve accuracy by up to 25%.
- Mismatched references provide little benefit or can even hurt performance.
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Why this may matter
The results suggest some multimodal reasoning errors may be addressed by constructing task-appropriate visual references without retraining, rather than only by improving perception. This points to a training-free intervention for visual reasoning.
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