arXiv AI By Yungeng Liu, Zan Chen, Yu Guang Wang, Yiqing Shen

TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering

Read the original on arXiv AI →

arXiv:2411. 06024v1 Announce Type: cross Abstract: The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineering research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jul 31

SimpleWikiSearch: A Clean Offline Wikipedia Environment for Agentic Search

arXiv:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.

By Guanming Xiong, Penghui Zhang
arXiv AI
6d ago

FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases

FlyAOC is a benchmark that tests AI agents on end‑to‑end ontology curation of Drosophila scientific literature. Given a gene symbol, a brief description, a large paper corpus, and ontology resources, agents must search for evidence and produce structured annotations such as function terms, expression patterns, and historical synonyms. The benchmark contains 7,397 expert‑curated annotations across 100 genes and evaluates different agent harnesses, revealing system‑level failure modes that single‑task evaluations miss.

By Xingjian Zhang, Sophia Moylan, Ziyang Xiong, Qiaozhu Mei, Yichen Luo, Jiaqi W. Ma