5 citations · 7 across the 9 of their papers we have counts for
13 papers
CoworkEnv: Synthesizing Training Environments for Long-Horizon Cowork Agents
Zhongwei Xie, Yiming Du, Jiaxin Bai +10
AI coworkers must coordinate tools, communicate across turns, and act on changing workspace state while respecting workflow constraints. Constructing training experience for these…
CASE: Cost-Aware Stopping for Efficient Long-Video Agents
Yiming Du, Chenghao Liu, Zhiyuan Liu +4
Long-video agents can actively gather question-relevant evidence, but they typically leave a central decision implicit: when has the agent seen enough to answer? We propose CASE, a…
Write, Execute, Refine: From Skill Follower to Skill Optimizer via Reinforcement Learning from Execution Feedback
Kang Peng, Zhiwei Zhang, Yichen Zhang +7
Expert-written natural language skills can improve tool-using agents, yet agent-authored skills perform 8--11 points worse than using no skill. While inference-time loops can repai…
Mitigating Context Interference for Reliable and Efficient Search Agents
Boyang Xue, Bin Wu, Shuofei Qiao +8
Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved. However, the contexts…
MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models
Xiyu Ren, Zhaowei Wang, Yiming Du +11
Memory is essential for large vision-language models (LVLMs) to handle long, multimodal interactions, with two method directions providing this capability: long-context LVLMs and m…
Can Retrieval Heads See Images? Multimodal Retrieval Heads in Long-Context Vision-Language Models
Aaron Branson Cigres Li, Zhaowei Wang, Yu Zhao +9
Large vision-language models increasingly rely on long-context modeling to reason over documents, hour-level videos, and long-horizon agent trajectories, requiring them to locate r…
Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents
Yiming Du, Baojun Wang, Yifan Xiang +11
Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents. However, existing works and our pilot study have shown that as dialogue hi…
ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning
Yiming Du, Yifan Xiang, Bin Liang +3
Fine-tuning multi-turn dialogue systems requires high-quality supervision but often suffers from degraded performance when exposed to low-quality data. Supervision errors in early…
MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents
Yiming Du, Bingbing Wang, Yang He +7
Modern task-oriented dialogue (TOD) systems increasingly rely on large language model (LLM) agents, leveraging Retrieval-Augmented Generation (RAG) and long-context capabilities fo…
Rethinking Memory in LLM based Agents: Representations, Operations, and Emerging Topics
Yiming Du, Wenyu Huang, Danna Zheng +5
Memory is fundamental to large language model (LLM)-based agents, but existing surveys emphasize application-level use (e.g., personalized dialogue), while overlooking the atomic o…
EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems
Zhengyi Zhao, Shubo Zhang, Yiming Du +5
Large language models have improved dialogue systems, but often process conversational turns in isolation, overlooking the event structures that guide natural interactions. Hence w…
PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering
Yiming Du, Hongru Wang, Zhengyi Zhao +5
Long-term memory plays a critical role in personal interaction, considering long-term memory can better leverage world knowledge, historical information, and preferences in dialogu…
A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models
Hongru Wang, Lingzhi Wang, Yiming Du +4
Dialogue systems (DS), including the task-oriented dialogue system (TOD) and the open-domain dialogue system (ODD), have always been a fundamental task in natural language processi…