metadata
license: mit
task_categories:
- text-retrieval
tags:
- agentic-search
- bm25
Pi-Serini
This repository contains data and artifacts associated with the paper Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?.
Pi-Serini is a reusable, benchmark-driven search-agent workspace designed for index-driven BM25 retrieval, agentic search, and benchmark-aware evaluation. It revisits whether a lexical retriever (BM25) suffices when paired with capable frontier Large Language Models (LLMs) in an agentic loop.
- Project Page: https://ricky42613.github.io/piserini
- GitHub Repository: https://github.com/justram/pi-serini
Supported Benchmarks
The Pi-Serini framework supports several benchmarks for evaluation:
browsecomp-plus: Default packaged benchmark used to evaluate deep research capabilities.msmarco-v1-passage: Index-driven MS MARCO v1 passage benchmark.benchmark-template: A tiny local end-to-end demo benchmark for validation.
Citation
@misc{hsu2026rethinkingagenticsearchpiserini,
title = {Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?},
author = {Tz-Huan Hsu and Jheng-Hong Yang and Jimmy Lin},
year = {2026},
eprint = {2605.10848},
archivePrefix = {arXiv},
primaryClass = {cs.IR},
url = {https://arxiv.org/abs/2605.10848}
}