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20242026
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cs.CV2026

InterSketch: An Interleaved Reasoning Model with Self-correcting Visual Sketch and Stepwise Reward

Zhiwei Ning, Wenwen Tong, Xiangli Kong +12

While vision-language models (VLMs) have exhibited multi-turn visual reasoning capabilities, their reasoning trajectories remain relatively shallow and are dominated by a text-cent…

cs.CV2026

Stage-adaptive Token Selection for Efficient Omni-modal LLMs

Zijie Xin, Jie Yang, Ruixiang Zhao +4

Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequences interleaved at the window…

cs.CV2026

OmniPro: A Comprehensive Benchmark for Omni-Proactive Streaming Video Understanding

Ruixiang Zhao, Jie Yang, Zijie Xin +4

Omni-proactive streaming video understanding, i.e., autonomously deciding when to speak and what to say from continuous audio-visual streams, is an emerging capability of omni-moda…

cs.CV2025

KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model

Jie Yang, Wang Zeng, Sheng Jin +5

The emergence of Multimodal Large Language Models (MLLMs) has revolutionized image understanding by bridging textual and visual modalities. However, these models often struggle wit…

cs.CV2025

WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement Learning

Jie Yang, Feipeng Ma, Zitian Wang +4

Building on the success of text-based reasoning models like DeepSeek-R1, extending these capabilities to multimodal reasoning holds great promise. While recent works have attempted…

cs.CV2025

Unlock the Power of Unlabeled Data in Language Driving Model

Chaoqun Wang, Jie Yang, Xiaobin Hong +1

Recent Vision-based Large Language Models~(VisionLLMs) for autonomous driving have seen rapid advancements. However, such promotion is extremely dependent on large-scale high-quali…