3 citations · 8 across the 14 of their papers we have counts for
15 papers
Search2Skill: Skill Distillation Beyond Knowledge Boundaries Via Rubric-Based Reinforcement Learning
Muyang Ye, Tian Lan, Feihu Jiang +10
Reusable skills, which encapsulate the procedural knowledge required to solve real-world professional tasks, offer LLM-based agents a path toward self-evolution in expert domains.…
CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks
Peiqin Lin, Chenyang Lyu, Wenjiang Luo +22
Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities. However, these benchmarks prio…
MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation
Haoyuan Shi, Yunxin Li, Nanhao Deng +5
The evolution of video generation toward complex, multi-shot narratives has exposed a critical deficit in current evaluation methods. Existing benchmarks remain anchored to single-…
Structured Episodic Event Memory
Zhengxuan Lu, Dongfang Li, Yukun Shi +3
Current approaches to memory in Large Language Models (LLMs) predominantly rely on static Retrieval-Augmented Generation (RAG), which often results in scattered retrieval and fails…
ComfyUI-R1: Exploring Reasoning Models for Workflow Generation
Zhenran Xu, Yiyu Wang, Xue Yang +5
AI-generated content has evolved from monolithic models to modular workflows, particularly on platforms like ComfyUI, enabling customization in creative pipelines. However, craftin…
ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development
Zhenran Xu, Xue Yang, Yiyu Wang +7
We introduce ComfyUI-Copilot, a large language model-powered plugin designed to enhance the usability and efficiency of ComfyUI, an open-source platform for AI-driven art creation.…
AniMaker: Multi-Agent Animated Storytelling with MCTS-Driven Clip Generation
Haoyuan Shi, Yunxin Li, Xinyu Chen +3
Despite rapid advancements in video generation models, generating coherent storytelling videos that span multiple scenes and characters remains challenging. Current methods often r…
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization
Yunxin Li, Xinyu Chen, Zitao Li +5
Applying Reinforcement Learning (RL) to Video Large Language Models (Video-LLMs) shows significant promise for complex video reasoning. However, popular Reinforcement Fine-Tuning (…
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…
A Unified Agentic Framework for Evaluating Conditional Image Generation
Jifang Wang, Xue Yang, Longyue Wang +7
Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and…
FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces
Zhenran Xu, Longyue Wang, Jifang Wang +7
Virtual film production requires intricate decision-making processes, including scriptwriting, virtual cinematography, and precise actor positioning and actions. Motivated by recen…
Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video Generation
Yunxin Li, Haoyuan Shi, Baotian Hu +5
Traditional animation generation methods depend on training generative models with human-labelled data, entailing a sophisticated multi-stage pipeline that demands substantial huma…
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
Yunxin Li, Xinyu Chen, Baotian Hu +3
Despite significant breakthroughs in video analysis driven by the rapid development of large multimodal models (LMMs), there remains a lack of a versatile evaluation benchmark to c…
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
Yunxin Li, Shenyuan Jiang, Baotian Hu +5
Recent advancements in Multimodal Large Language Models (MLLMs) underscore the significance of scalable models and data to boost performance, yet this often incurs substantial comp…
VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
Yunxin Li, Baotian Hu, Haoyuan Shi +3
Large Multimodal Models (LMMs) have achieved impressive success in visual understanding and reasoning, remarkably improving the performance of mathematical reasoning in a visual co…