1 citations · 1 across the 10 of their papers we have counts for
11 papers
When Non-Adversarial Noise Hurts Learning: Benchmarking Skill Self-Evolution in Agents
Tao Yu, Zhongtian Luo, Yuchen Long +19
Large language model agents increasingly improve by distilling experience into reusable skills, but errors in that experience can persist in learned rules and affect later tasks. E…
SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks
Tao Yu, Yifei Qu, Zhiqing Cui +14
Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA e…
Omni-DeepSearch: A Benchmark for Audio-Driven Omni-Modal Deep Search
Tao Yu, yiming ding, Shenghua Chai +16
Current omni-modal benchmarks mainly evaluate models under settings where multiple modalities are provided simultaneously, while the ability to start from audio alone and actively…
Beyond the All-in-One Agent: Benchmarking Role-Specialized Multi-Agent Collaboration in Enterprise Workflows
Tao Yu, Hao Wang, Changyu Li +15
Large language model (LLM) agents are increasingly expected to operate in enterprise environments, where work is distributed across specialized roles, permission-controlled systems…
RPO:Reinforcement Fine-Tuning with Partial Reasoning Optimization
Hongzhu Yi, Xinming Wang, Zhenghao zhang +12
Within the domain of large language models, reinforcement fine-tuning algorithms necessitate the generation of a complete reasoning trajectory beginning from the input query, which…
Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss
Zhenghao Zhang, Jun Xie, Xingchen Chen +11
The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established…
BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions
Tao Yu, Zhengbo Zhang, Zhiheng Lyu +8
Efficiently solving real-world problems with LLMs increasingly hinges on their ability to interact with dynamic web environments and autonomously acquire external information. Whil…
More Is Better: A MoE-Based Emotion Recognition Framework with Human Preference Alignment
Jun Xie, Yingjian Zhu, Feng Chen +9
In this paper, we present our solution for the semi-supervised learning track (MER-SEMI) in MER2025. We propose a comprehensive framework, grounded in the principle that "more is b…
Multimodal Video Emotion Recognition with Reliable Reasoning Priors
Zhepeng Wang, Yingjian Zhu, Guanghao Dong +4
This study investigates the integration of trustworthy prior reasoning knowledge from MLLMs into multimodal emotion recognition. We employ Gemini to generate fine-grained, modality…
Team of One: Cracking Complex Video QA with Model Synergy
Jun Xie, Zhaoran Zhao, Xiongjun Guan +5
We propose a novel framework for open-ended video question answering that enhances reasoning depth and robustness in complex real-world scenarios, as benchmarked on the CVRR-ES dat…
Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles
Jun Xie, Xiongjun Guan, Yingjian Zhu +5
In this paper, we present the runner-up solution for the Ego4D EgoSchema Challenge at CVPR 2025 (Confirmed on May 20, 2025). Inspired by the success of large models, we evaluate an…