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cs.CV2026
A Lightweight Velocity Correction Framework for Improving Instruction Following in Few-Step Text-to-Image Generation
Yating Gao, Zhenlong Zhu, Wen Zihao +6
Few-step text-to-image generators produce high-quality images with few network forward passes, yet struggle with complex prompts involving multiple subjects, interactions, spatial…
cs.CV2026
Trace, Verify, and Correct: A Training-Free Framework for Spatial Reasoning in Multimodal LLMs
Yang Yang, Jiawei Chen, Tairan Chen +1
Although Multimodal Large Language Models (MLLMs) have made substantial progress, their spatial reasoning may still produce intermediate judgments inconsistent with the input image…
cs.CV2026
MedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation
Yang Xing, Jiong Wu, Savas Ozdemir +4
Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report generation and visual question answering (…