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 relations, counting, and attribute binding. Can these errors be reduced without changing the generator's parameters or increasing its forward-pass count? We propose a lightweight correction framework for a frozen 6B-parameter generator. The generator predicts velocities that determine how its intermediate image representation is updated; a corrector with fewer than 20M parameters learns residual adjustments at selected sampling steps. Because earlier corrections change the states encountered at later steps, our On-Trajectory Flow-Bridge Supervision derives residual targets at the states actually reached during corrected generation. A single corrector is shared across selected steps and runs in parallel with the generator. Only the corrector is trained, requiring neither reward optimization nor re-distillation. To our knowledge, this is the first framework for lightweight velocity-residual adaptation of fully frozen few-step text-to-image generators that preserves their original sampling schedule and forward-pass count. On our target complex-scene distribution, the framework achieves a +17.60-percentage-point NetWin over frozen Z-Image-Turbo in pairwise instruction-following evaluation, with only 26.9 ms (0.84%) additional end-to-end latency per image and no increase in backbone evaluations.
Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation
Malaviya National Institute of Technology Jaipur（斋浦尔马尔维亚国家技术学院）；Côte d’Azur University（蔚蓝海岸大学）；Khalifa University（哈里发大学）；Biomedical Perception & Intelligence Lab, University of South Dakota（南达科他大学的生物医学感知与智能实验室）
Improving Lightweight Weed Detection via Knowledge Distillation
University of Hohenheim（霍根海姆大学）；Dept. of Artificial Intelligence in Agricultural Engineering & Computational Science Hub Stuttgart, Germany（农业工程与计算科学人工智能部门，斯图加特研究中心，德国）
Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Zhejiang University（浙江大学）；University of Central Florida（中佛罗里达大学）；Illinois Institute of Technology（伊利诺伊理工大学）
