Decoding the Pulse of Reasoning VLMs in Multi-Image Understanding Tasks
Multi-image reasoning remains a significant challenge for vision-language models (VLMs). We investigate a previously overlooked phenomenon: during chain-of-thought (CoT) generation, the text-to-image (T2I) attention of reasoning VLMs exhibits diffuse "pulses": sporadic and unfocused attention patterns that fail to concentrate on task-relevant images. We further reveal a systematic positional bias in attention allocation across images. Motivated by these observations, we propose PulseFocus, a training-free, inference-time method that structures CoT reasoning into interleaved plan/focus blocks with soft attention gating. By forcing the model to explicitly plan which image to examine and the...
| Authors | Chenjun Li |
|---|---|
| Primary category | cs.CV |
| Published | 4 March 2026 |
| Last updated at source | 7 May 2026 |
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