BioSage: Human-Agent Collaboration Platform for Cross-Disciplinary Knowledge Discovery and Synthesis

Abstract

Experts face increasing cognitive demands due to the volume of scientific artifacts, databases, frontier models, and evolving hypotheses across domains. Traditional AI systems often prioritize model performance over user integration, limiting their effectiveness in real-world workflows. This paper presents BioSage, a Compound AI (CAI) platform designed through a workflow-centric, human-centered approach. Guided by principles from human factors, cognitive science, and industrial-organizational psychology, BioSage incorporates multiple collaborative agents—retrieval, reasoning, and translator—each aligned to a specific user need in the scientific discovery process. The system supports key workflows such as knowledge retrieval and synthesis, debate, brainstorming, and experimental design. Through initial user testing with domain experts and cross-disciplinary benchmarks, BioSage demonstrates how user-aligned CAI architecture can reduce cognitive load, improve insight generation, and build trust through transparency and perspective-taking. This work underscores the value of integrating human-centered design early in CAI development to enhance adoption and impact in high-stakes environments.