activity
20242026
most citedIntelligent System for Automated Molecular Patent Infringement Assessment

3 citations · 5 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

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…

cs.AI2026

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…

cs.CL2025

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…

cs.CL2025

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…

cs.LG2025

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…

cs.LG2025★ 3 cited

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…

cs.LG2024★ 2 cited

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…