23 citations · 37 across the 8 of their papers we have counts for
10 papers
AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks
Hongjin Qian, Chaofan Li, Kun Luo +11
We present AREX-2, an effort to advance the self-improving capability of LLM agents, which we define as the ability to iteratively refine a solution at test time. This ability rest…
Just-In-Time Agent Memory with Runtime Agentic Research
Bingyu Yan, Chaofan Li, Hongjin Qian +3
Memory is critical for AI agents. Many existing agent-memory systems follow an Ahead-of-Time (AOT) design, constructing memory before a specific request arrives. While this reduces…
AREX: Towards a Recursively Self-Improving Agent for Deep Research
Shuqi Lu, Chaofan Li, Kun Luo +21
Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed i…
AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse
Zhang Zhang, Shuqi Lu, Hongjin Qian +2
Building LLM-based agents has become increasingly important. Recent works on LLM-based agent self-evolution primarily record successful experiences as textual prompts or reflection…
General Agentic Memory Via Deep Research
B. Y. Yan, Chaofan Li, Hongjin Qian +2
Memory is critical for AI agents, yet the widely-adopted static memory, aiming to create readily available memory in advance, is inevitably subject to severe information loss. To a…
End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction
Qingsi Lai, Fanjie Xu, Lin Yao +8
Powder X-ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most auto…
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+
Shuqi Lu, Zhifeng Gao, Di He +2
Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic s…
Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?
Yuejiang Yu, Shuqi Lu, Zhifeng Gao +2
Molecular docking, given a ligand molecule and a ligand binding site (called ``pocket'') on a protein, predicting the binding mode of the protein-ligand complex, is a widely used t…
3D Molecular Generation via Virtual Dynamics
Shuqi Lu, Lin Yao, Xi Chen +3
Structure-based drug design, i.e., finding molecules with high affinities to the target protein pocket, is one of the most critical tasks in drug discovery. Traditional solutions,…
Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder
Shuqi Lu, Di He, Chenyan Xiong +6
Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. Autoencoder-based language models are appealing in dense ret…