Cross-Disciplinary Knowledge Retrieval and Synthesis: A Compound AI Architecture for Scientific Discovery

2025-11-23

AI Summary

The explosive growth of scientific knowledge impedes interdisciplinary knowledge discovery and collaboration. To address this, we propose BioSage—a composite AI architecture integrating large language models (LLMs) with retrieval-augmented generation (RAG) to enable collaborative intelligence across biomedicine, artificial intelligence, data science, and biosafety. Our contributions are threefold: (1) a specialized agent design for cross-disciplinary terminology alignment and traceable reasoning; (2) a modular agent coordination paradigm supporting query planning, response synthesis, cross-modal translation, and multimodal analysis (text, figures, structured data); and (3) a user-centered interactive mechanism. Evaluated on multiple scientific benchmarks, BioSage outperforms baseline LLM and RAG methods by 13–21%. Moreover, on a newly constructed bio-AI cross-modal benchmark, it significantly enhances knowledge acquisition efficiency and research collaboration capability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The exponential growth of scientific knowledge has created significant barriers to cross-disciplinary knowledge discovery, synthesis and research collaboration. In response to this challenge, we present BioSage, a novel compound AI architecture that integrates LLMs with RAG, orchestrated specialized agents and tools to enable discoveries across AI, data science, biomedical, and biosecurity domains. Our system features several specialized agents including the retrieval agent with query planning and response synthesis that enable knowledge retrieval across domains with citation-backed responses, cross-disciplinary translation agents that align specialized terminology and methodologies, and reasoning agents that synthesize domain-specific insights with transparency, traceability and usability. We demonstrate the effectiveness of our BioSage system through a rigorous evaluation on scientific benchmarks (LitQA2, GPQA, WMDP, HLE-Bio) and introduce a new cross-modal benchmark for biology and AI, showing that our BioSage agents outperform vanilla and RAG approaches by 13%-21% powered by Llama 3.1. 70B and GPT-4o models. We perform causal investigations into compound AI system behavior and report significant performance improvements by adding RAG and agents over the vanilla models. Unlike other systems, our solution is driven by user-centric design principles and orchestrates specialized user-agent interaction workflows supporting scientific activities including but not limited to summarization, research debate and brainstorming. Our ongoing work focuses on multimodal retrieval and reasoning over charts, tables, and structured scientific data, along with developing comprehensive multimodal benchmarks for cross-disciplinary discovery. Our compound AI solution demonstrates significant potential for accelerating scientific advancement by reducing barriers between traditionally siloed domains.

Problem

• Addressing barriers to cross-disciplinary knowledge discovery and synthesis
• Integrating specialized AI agents for retrieval and translation across domains
• Overcoming scientific knowledge silos through compound AI architecture

Innovation

• Integrates LLMs with RAG and specialized agents
• Features retrieval agents with query planning capabilities
• Uses translation agents to align cross-disciplinary terminology