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Author

Bryan Wang

Senior Manager, Applied Science

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Computer vision (8)
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14 results found

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CORRECT: Condensed error recognition via knowledge transfer in multi-agent systems

Yifan Yu, Moyan Li, Shaoyuan Xu, Jinmiao Fu, Renly Hou, Fan Lai, Bryan Wang

ICML 2026

2026

Multi-agent systems (MAS) are increasingly capable of tackling complex real-world tasks, yet their reliance on inter-agent coordination, tool use, and long-horizon reasoning makes error recognition particularly challenging. Minor errors can propagate across agents, escalating into task failures while producing long, intertwined execution trajectories that impose significant costs for both human developers

Machine learning
Text-to-distribution prediction with quantile tokens and neighbor context

Yilun Zhu, Yuan Zhuang, Nikhita Vedula, Dushyanta Dhyani, Shaoyuan Xu, Moyan Li, Mohsen Bayati, Bryan Wang, Shervin Malmasi

ACL 2026

2026

Many applications of LLM-based text regression require predicting a full conditional distribution rather than a single point value. We study distributional regression under empirical-quantile supervision, where each input is paired with multiple observed quantile outcomes, and the target distribution is represented by a dense grid of quantiles. We address two key limitations of current approaches: the lack

Machine learning
AlignFlow: Improving flow-based generative models with semi-discrete optimal transport

Lingkai Kong, Molei Tao, Yang Liu, Bryan Wang, Jinmiao Fu, Chien-Chih Wang, Huidong Liu

ICLR 2026

2026

Flow-based Generative Models (FGMs) effectively transform noise into complex data distributions. Incorporating Optimal Transport (OT) to couple noise and data during FGM training has been shown to improve the straightness of flow trajectories, enabling more effective inference. However, existing OT-based methods estimate the OT plan using (mini-)batches of sampled noise and data points, which limits their

Computer vision
CodeV: Code with images for faithful visual reasoning via tool-aware policy optimization

Renly Hou, Shaoyuan Xu, Manan Biyani, Moyan Li, Jia (Kevin) Liu, Todd C. Hollon, Bryan Wang

CVPR 2026

2026

Agentic vision–language models are increasingly trained to 'think with images' by calling image operations. However, we show that high final-answer accuracy often hides unfaithful visual reasoning: models may invoke tools on irrelevant regions or ignore tool outputs entirely, yet still guess the correct answer. In this work, we first propose a faithfulness evaluation protocol that measures whether intermediate

Computer vision
Temporal-consistent video restoration with pre-trained diffusion models

Hengkang Wang, Yang Liu, Huidong Liu, Chien-Chih Wang, Yanhui Guo, Hongdong Li, Bryan Wang, Ju Sun

AAAI 2026

2026

Video restoration (VR) aims to recover high-quality videos from degraded ones. Although recent zero-shot VR methods using pre-trained diffusion models (DMs) show good promise, they suffer from approximation errors during reverse diffusion and insufficient temporal consistency. Moreover, dealing with 3D video data, VR is inherently computationally intensive. In this paper, we advocate viewing the reverse

Computer vision
Zero-shot customized video editing with diffusion feature transfer

Wei Chen, Huidong Liu, Yang Liu, Chien-Chih Wang, Moyan Li, Hongdong Li, Bryan Wang

ICCV 2025

2025

Customized video editing aims at substituting the object in a given source video with a target object from reference images (Fig. 1). Existing approaches often rely on fine-tuning pre-trained models by learning the appearance of the objects in the reference images, as well as the temporal information from the source video. These methods are however not scalable as fine-tuning is required for each source

Computer vision
QID: Efficient query-informed ViTs in data-scarce regimes for OCR-free visual document understanding

Binh Le, Shaoyuan Xu, Jinmiao Fu,