Beyond Static Visual Tokens: Structured Sequential Visual Chain-of-Thought Reasoning
This addresses the limitation of static visual encoding in multimodal LLMs for visual reasoning tasks, though it appears incremental as it builds on existing CoT methods with a structured visual component.
The paper tackles the problem of multimodal LLMs lacking goal-driven and adaptive visual access by proposing Structural Sequential Visual CoT (SSV-CoT), which uses saliency maps to organize visual regions and performs reasoning in a curriculum-like order. Experiments on diverse visual reasoning benchmarks show gains, validating the approach.
Current multimodal LLMs encode images as static visual prefixes and rely on text-based reasoning, lacking goal-driven and adaptive visual access. Inspired by human visual perception-where attention is selectively and sequentially shifted from the most informative regions to secondary cues-we propose Structural Sequential Visual CoT SSV-CoT. First, a question-relevant saliency map identifies and organizes key visual regions, explicitly modeling the spatial distribution of visual importance. Second, reasoning is performed following this discriminative order, inducing a curriculum-like semantic progression from primary to secondary cues. This method is trained end-to-end, using text cot and answer supervision, without relying on region-level annotations or specialized external tools. Experiments on diverse visual reasoning benchmarks show gains, validating structured and sequential visual cognition.