CellScientist: Autonomous Biological Discovery via Scientific Alignment and Multifaceted Biological Priors

Recent advances in large language models have catalyzed growing interest in autonomous AI-driven scientific discovery, yet existing frameworks face a fundamental limitation when applied to knowledge-intensive domains such as cellular biology: they either produce scientifically invalid hypotheses that violate experimental constraints and physical laws, or generate methodologically sound but trivial findings that lack biological significance. To address this dual challenge, we observe that rigorous autonomous discovery requires both strict enforcement of validity constraints throughout the research process and systematic injection of domain knowledge to guide hypothesis search toward scientifically meaningful questions. We present CellScientist, an autonomous agent framework that implements this insight through a dual-grounding paradigm. First, Scientific Semantic Alignment enforces formal consistency among research questions, experimental protocols, statistical estimands, and biochemical plausibility, rejecting internally contradictory proposals before resource expenditure. Second, Comprehensive Biological Prior Injection integrates structured ontologies, automated literature retrieval, toolchain execution proficiencies, and empirical research heuristics across multiple decision points, steering the search toward biologically interpretable discoveries. Together, these mechanisms enable CellScientist to autonomously formulate research questions, design and execute multi-stage experiments, critically evaluate evidence through falsification tests, and synthesize findings into coherent scientific manuscripts. Evaluated across 20 single-cell and spatial omics research topics, CellScientist improves composite manuscript quality by 15–19% over the strongest general-purpose autonomous research agent and by 29–37% over prior AI Scientist systems, with the largest gains in reproducibility (+17–30%) and data-conclusion traceability (+24–35%). Running end to end without human intervention, CellScientist arrived at findings that revise prevailing expectations in the systems it studied: in post-influenza lung, it established that apparent myeloid transcriptional recovery is almost entirely demographic, with conditioning on cellular composition collapsing unique stage-associated variance from 72.7% to 0.80%; in human kidney, it showed that lineage-intrinsic transcriptional remodeling, rather than the compositional shifts long assumed to dominate, accounts for 92.09% of corticomedullary variance.