Searching for Task-Specific Vision Paths: Evolutionary Block Pruning Across Vision-Language Models
Vision-language models normally execute the same complete vision encoder for every question, even when OCR, counting, object, attribute, and spatial queries may not require identical computation. We study whether fixed-budget combinations of vision blocks can be skipped without fine-tuning. A shared K-block route skips one searched set of exactly K blocks for every question, while a capability-specific K-block policy selects one same-size route using a known capability label. We introduce a source-balanced evolutionary search and compare it with independent ranking, contiguous removal, and random routes at matched budgets. Experiments use Qwen2.5-VL-3B-Instruct, SmolVLM2-2.2B-Instruct, an...
| Authors | Tarun Tomar |
|---|---|
| Primary category | cs.CV |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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