5 citations · 15 across the 19 of their papers we have counts for
21 papers
PluginRSI: Recursive Improvement of Agent Harnesses with Reusable Plugins
Yaorui Shi, Yuchun Miao, Yuxin Chen +5
The harness surrounding a language model is a central determinant of agent performance. Recent methods optimize harnesses by searching over complete programs, where individual mech…
Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning
Shiding Zhu, Yudi Qi, Yajie Wang +6
Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly r…
Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments
Yuxin Chen, Xiaodong Cai, Junfeng Fang +9
Recent advances in large language models (LLMs) have facilitated the widespread deployment of LLMs as interactive agents capable of reasoning, planning, and tool use. Despite stron…
VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions
Yuxin Chen, Yi Zhang, Zhengzhou Cai +11
Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such settings increasingly depends on…
When to Stop Reusing: Dynamic Gradient Gating for Sample-Efficient RLVR
Yuchun Miao, Sen Zhang, Yuqi Zhang +4
Reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for advanced reasoning in Large Language Models (LLMs), but rollout samples are expensive to…
Look Before You Leap: Autonomous Exploration for LLM Agents
Ziang Ye, Wentao Shi, Yuxin Liu +6
Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acquiring sufficient environment-…
Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
Yaorui Shi, Yuxin Chen, Zhengxi Lu +6
A persistent skill library allows language model agents to reuse successful strategies across tasks. Maintaining such a library requires three coupled capabilities. The agent selec…
Joint Optimization of Multi-agent Memory System
Wenyu Mao, Haoyang Liu, Haosong Tan +4
Memory systems are critical for LLMs, mitigating context window limitations and supporting long-horizon user-LLM interactions. Such systems typically comprise multiple agents respo…
When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning
Leheng Sheng, Yongtao Zhang, Wenchang Ma +6
While reasoning over long context is crucial for various real-world applications, it remains challenging for large language models (LLMs) as they suffer from performance degradatio…
Learning to Self-Verify Makes Language Models Better Reasoners
Yuxin Chen, Yu Wang, Yi Zhang +9
Recent large language models (LLMs) achieve strong performance in generating promising reasoning paths for complex tasks. However, despite powerful generation ability, LLMs remain…
MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning
Yaorui Shi, Shugui Liu, Yu Yang +7
Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memory systems serialize history as t…
Reasoning-Enhanced Large Language Models for Molecular Property Prediction
Jiaxi Zhuang, Yaorui Shi, Jue Hou +8
Molecular property prediction is crucial for drug discovery and materials science, yet existing approaches suffer from limited interpretability, poor cross-task generalization, and…
Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents
Yaorui Shi, Yuxin Chen, Siyuan Wang +5
Large language models face challenges in long-context question answering, where key evidence of a query may be dispersed across millions of tokens. Existing works equip large langu…
Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented Reasoning
Yaorui Shi, Sihang Li, Chang Wu +5
Large language models have demonstrated impressive reasoning capabilities but are inherently limited by their knowledge reservoir. Retrieval-augmented reasoning mitigates this limi…
Language-Enhanced Representation Learning for Single-Cell Transcriptomics
Yaorui Shi, Jiaqi Yang, Changhao Nai +5
Single-cell RNA sequencing (scRNA-seq) offers detailed insights into cellular heterogeneity. Recent advancements leverage single-cell large language models (scLLMs) for effective r…
NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
Zhiyuan Liu, Yanchen Luo, Han Huang +7
3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers,…
Intelligent System for Automated Molecular Patent Infringement Assessment
Yaorui Shi, Sihang Li, Taiyan Zhang +12
Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven pro…
SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding
Sihang Li, Jin Huang, Jiaxi Zhuang +7
Scientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery. Despite the remarka…
ReactXT: Understanding Molecular "Reaction-ship" via Reaction-Contextualized Molecule-Text Pretraining
Zhiyuan Liu, Yaorui Shi, An Zhang +5
Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction. Beyond single molecules,…
ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction
Yaorui Shi, An Zhang, Enzhi Zhang +2
Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process. Conventional techniques, notably tho…
Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
Zhiyuan Liu, Yaorui Shi, An Zhang +4
Masked graph modeling excels in the self-supervised representation learning of molecular graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three ke…