---
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?](https://huggingface.co/papers/2605.10848).

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](https://ricky42613.github.io/piserini)
- **GitHub Repository:** [https://github.com/justram/pi-serini](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

```bibtex
@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}
}
```