4 citations · 6 across the 9 of their papers we have counts for
12 papers
LatentRemedy: Efficient Remedy For Non-Prefix KV Cache Reuse in Multi-Agent Systems
Zhiwei Chen, Zhongtao Rao, Waikit Xiu +4
Non-prefix KV cache reuse is a promising approach for improving inference efficiency in multi-agent systems as agents share the same context blocks. However, when agents have confl…
DivOPD: Spread Wide, Look Close for Asynchronous On-Policy Distillation of Multi-turn Agents
Hanyang Wang, Zeyuan Liu, Zhengyu Chen +7
On-policy distillation (OPD) trains student agents through teacher supervision on their own interactions with an environment. However, in asynchronous multi-turn training, arrival-…
SAGE: Structured Strategic Reasoning for Efficient LLM Game Playing
Zhiwei Chen, Tianchun Wang, Zhongtao Rao +3
A strong LLM strategic agent should reason prospectively over uncertain futures, adapt its strategy to opponents' behavioral tendencies, and continuously recalibrate its decision p…
BrainPilot: Automating Brain Discovery with Agentic Research
Haoxuan Li, Tianci Gao, Jianhe Li +13
Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines. Addressing a single research question therefore requires a coordina…
BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents
Hanyang Wang, Weijieying Ren, Yuxiang Zhang +4
Stepwise group-based RL is an attractive way to train long-horizon LLM agents without a learned critic: it reuses multiple sampled rollouts to estimate local advantages. Its weakne…
Graph of Trace: Visualizing Execution Traces of Scientific Agent
Tianci Gao, Haoxuan Li, Jianhe Li +5
Scientific AI agents can autonomously carry out complex research workflows, yet these unfolded workflows often remain difficult for humans to inspect and review, limiting interpret…
Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation
Yuchen Cai, Ding Cao, Liang Lin +9
On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, existing studies largely attribute this advantage to denser and…
A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models
Weijieying Ren, Jingxi Zhu, Zehao Liu +2
Artificial intelligence (AI) has demonstrated significant potential in transforming healthcare through the analysis and modeling of electronic health records (EHRs). However, the i…
DiaLLMs: EHR Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction
Weijieying Ren, Tianxiang Zhao, Lei Wang +2
Recent advances in Large Language Models (LLMs) have led to remarkable progresses in medical consultation. However, existing medical LLMs overlook the essential role of Electronic…
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions
Weijieying Ren, Tianxiang Zhao, Yuqing Huang +1
Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challen…
Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning
Weijieying Ren, Xinlong Li, Lei Wang +2
Existing research has shown that large language models (LLMs) exhibit remarkable performance in language understanding and generation. However, when LLMs are continuously fine-tune…
T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data
Weijieying Ren, Tianxiang Zhao, Wei Qin +1
In many real-world scenarios, distribution shifts exist in the streaming data across time steps. Many complex sequential data can be effectively divided into distinct regimes that…