2 citations · 2 across the 14 of their papers we have counts for
15 papers
VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness
Zihao Lin, Haoyu Dong, Haobo Xu +10
LLM system capabilities depend not only on model parameters but also on the executable harnesses that organize information acquisition, tool use, reasoning, and memory. Recursive h…
VideoGen-Agent: Reinforcing Video Generation Agents
Binxu Li, Haoyi Duan, Yuhui Zhang +9
Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring speci…
UniTemp: Unlocking Video Generation in Any Temporal Order via Bidirectional Distillation
Lin Zhang, Sicheng Mo, Zefan Cai +6
Autoregressive video diffusion models have emerged as a promising approach for long video generation, achieving strong performance in streaming settings. However, existing methods…
Open-Ended Video Game Glitch Detection with Agentic Reasoning and Temporal Grounding
Muyang Zheng, Tong Zhou, Geyang Wu +3
Open-ended video game glitch detection aims to identify glitches in gameplay videos, describe them in natural language, and localize when they occur. Unlike conventional game glitc…
GLANCE: A Global-Local Coordination Multi-Agent Framework for Music-Grounded Non-Linear Video Editing
Zihao Lin, Haibo Wang, Zhiyang Xu +7
Music-grounded mashup video creation is a challenging form of video non-linear editing, where a system must compose a coherent timeline from large collections of source videos whil…
Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding
Haibo Wang, Zihao Lin, Zhiyang Xu +1
3D Visual Grounding (3D-VG) aims to localize objects in 3D scenes via natural language descriptions. While recent advancements leveraging Vision-Language Models (VLMs) have explore…
MiLDEdit: Reasoning-Based Multi-Layer Design Document Editing
Zihao Lin, Wanrong Zhu, Jiuxiang Gu +8
Real-world design documents (e.g., posters) are inherently multi-layered, combining decoration, text, and images. Editing them from natural-language instructions requires fine-grai…
SuperFlow: Training Flow Matching Models with RL on the Fly
Kaijie Chen, Zhiyang Xu, Ying Shen +3
Recent progress in flow-based generative models and reinforcement learning (RL) has improved text-image alignment and visual quality. However, current RL training for flow models s…
R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation
Kaijie Chen, Zihao Lin, Zhiyang Xu +5
Reasoning is a fundamental capability often required in real-world text-to-image (T2I) generation, e.g., generating ``a bitten apple that has been left in the air for more than a w…
Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents
Zihao Lin, Zichao Wang, Yuanting Pan +5
Suggested questions (SQs) provide an effective initial interface for users to engage with their documents in AI-powered reading applications. In practical reading sessions, users h…
Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models
Mohammad Beigi, Sijia Wang, Ying Shen +9
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating…
InternalInspector : Robust Confidence Estimation in LLMs through Internal States
Mohammad Beigi, Ying Shen, Runing Yang +7
Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinat…
Holistic Evaluation for Interleaved Text-and-Image Generation
Minqian Liu, Zhiyang Xu, Zihao Lin +4
Interleaved text-and-image generation has been an intriguing research direction, where the models are required to generate both images and text pieces in an arbitrary order. Despit…
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models
Zihao Lin, Mohammad Beigi, Hongxuan Li +5
Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new facts into Large Language Models (LLMs). Two mainstream ME methods exist: parameter-m…
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models
Zihao Lin, Yan Sun, Yifan Shi +4
With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern. Altho…