Pantheon: LLM-Based Bio Assistant

Demo

System overview

This project focuses on the development of Pantheon, an open-source multi-agent LLM platform for biological scientific workflows
(Pantheon Project).

My work specifically targets Gene Panel Selection (GPS), a core capability of Pantheon that automates the design of gene panels for spatial transcriptomics experiments.

The GPS system coordinates five specialized LLM agents, built on open-source models and extended with custom toolchains and domain-specific reasoning. A Leader Agent interprets ambiguous biological intent, decomposes high-level scientific questions into structured computational steps, and orchestrates agents such as the Selection Expert, with iterative validation and error handling.

I am currently fine-tuning and continually pretraining Qwen3 LLMs to power Pantheon’s self-evolving agent backbone, leveraging reinforcement learning from verifiable rewards (RLVR) and tool-augmented inference to build domain-adaptive, evolvable agents for autonomous genomic reasoning. I also collaborate with Vizgen to integrate a reinforcement learning module for autonomous gene panel design by Pantheon.

This research is conducted in collaboration with Prof. Xiaojie Qiu (Qiu Lab, Stanford) and Dr. Weize Xu (Stanford Profile).

Deployed in collaboration with Vizgen, Pantheon processes large-scale multi-omics datasets and produces curated gene panels that previously required extensive manual expertise.

Publication: Xu, Poussi et al., PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery, bioRxiv 2026. Read preprint

GitHub Repository