Sibyl: A Multi-Agent System for Literature-Based Scientific Discovery

We present Sibyl, a multi-agent LLM pipeline that autonomously produces falsifiable predictions from published scientific literature, covering literature synthesis, knowledge representation, hypothesis generation, and hypothesis evaluation. The system uses a tiered agent architecture with two mandatory human-in-the-loop checkpoints and an automated provenance audit, evaluated through a temporal backtesting framework. In a proof-of-concept deployment on X-ray binary astrophysics, the system generated 60 predictions from pre-2015 literature, of which 11 (18%) were confirmed by independent post-2015 publications (12.5% under the most conservative provenance filters). These are preliminary results from an ongoing project.