most citedGene-associated Disease Discovery Powered by Large Language Models

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

collaborators

5 papers

cs.AI2026

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

Lin Shi, Haowei Lin, Zixuan Zhu +123

Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a…

cs.SE2026

CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents

Zhongming Yu, Hengjia Yu, Boqin Yuan +12

Coding agents repeatedly search, navigate, and retain context from evolving repositories, but disconnected indexes, language servers, and task-local histories force repeated discov…

cs.AI2026

MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning

Yaolun Zhang, Yujie Zhao, Nan Wang +6

Automatic multi-agent systems aim to instantiate agent workflows without relying on manually designed or fixed orchestration. However, existing automatic MAS approaches remain only…

cs.AI2026

LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification

Yanrui Wu, Lingling Zhang, Xinyu Zhang +5

Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real-world…

q-bio.QM20243 cited

Gene-associated Disease Discovery Powered by Large Language Models

Jiayu Chang, Shiyu Wang, Chen Ling +2

The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific…