3 citations · 4 across the 7 of their papers we have counts for
11 papers
Self-improving data synthesis enables scalable adaptation of embedding models
Zucan Lyu, Rui Tang, Jianlyu Chen +3
AI agents require specialised retrieval across domains and tasks, but adapting embedding models is constrained by costly manual training-data design. Here we introduce SYNTRA, a se…
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…
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
Jianlyu Chen, Yuyang Hu, Hongjin Qian +8
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and ve…
AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery
Lei Xiong, Kun Luo, Ziyi Xia +15
Autonomous scientific research is significantly advanced thanks to the development of AI agents. One key step in this process is finding the right scientific literature, whether to…
Benchmarking and Enabling Efficient Chinese Medical Retrieval via Asymmetric Encoders
Angqing Jiang, Jianlyu Chen, Zhe Fang +4
Effective medical text retrieval requires both high accuracy and low latency. While LLM-based embedding models possess powerful retrieval capabilities, their prohibitive latency an…
DeepXiv-SDK: An Agentic Data Interface for Scientific Literature
Hongjin Qian, Ziyi Xia, Ze Liu +11
LLM-agents are increasingly used to accelerate the progress of scientific research. Yet a persistent bottleneck is data access: agents not only lack readily available tools for ret…
Retro*: Optimizing LLMs for Reasoning-Intensive Document Retrieval
Junwei Lan, Jianlyu Chen, Zheng Liu +3
With the growing popularity of LLM agents and RAG, it has become increasingly important to retrieve documents that are essential for solving a task, even when their connection to t…
ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval
Jianlyu Chen, Junwei Lan, Chaofan Li +2
In this paper, we introduce ReasonEmbed, a novel text embedding model developed for reasoning-intensive document retrieval. Our work includes three key technical contributions. Fir…
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis
Chaofan Li, Jianlyu Chen, Yingxia Shao +2
Code embedding models attract increasing attention due to the widespread popularity of retrieval-augmented generation (RAG) in software development. These models are expected to ca…
AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark
Jianlyu Chen, Nan Wang, Chaofan Li +6
Evaluation plays a crucial role in the advancement of information retrieval (IR) models. However, current benchmarks, which are based on predefined domains and human-labeled data,…
Making Text Embedders Few-Shot Learners
Chaofan Li, MingHao Qin, Shitao Xiao +5
Large language models (LLMs) with decoder-only architectures demonstrate remarkable in-context learning (ICL) capabilities. This feature enables them to effectively handle both fam…