From Drop-off to Recovery: A Mechanistic Analysis of Segmentation in MLLMs
Multimodal Large Language Models (MLLMs) are increasingly applied to pixel-level vision tasks, yet their intrinsic capacity for spatial understanding remains poorly understood. We investigate segmentation capacity through a layerwise linear probing evaluation across the entire MLLM pipeline: vision encoder, adapter, and LLM. We further conduct an intervention based attention knockout analysis to test whether cross-token attention progressively refines visual representations, and an evaluation of bidirectional attention among image tokens on spatial consistency. Our analysis reveals that the adapter introduces a segmentation representation drop-off, but LLM layers progressively recover thr...
| Authors | Boyong Wu, Sanghwan Kim, Zeynep Akata |
|---|---|
| Primary category | cs.CV |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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