A small-scale molecular reasoning model that outperforms ultra-large foundation models via structured chain-of-thought and RL.
arXiv · March 16, 2026 · 2603.12808
Why it matters
It demonstrates that embedding explicit scientific logic and 'reflective' reasoning into smaller models is more effective than scaling parameters for molecular science. This shifts the focus from purely data-driven molecular predictions to knowledge-guided computational reasoning.
From the abstract
Advancements in artificial intelligence for molecular science are necessitating a paradigm shift from purely data-driven predictions to knowledge-guided computational reasoning. Existing molecular models are predominantly proprietary, lacking general molecular intelligence and generalizability. This underscores the necessity for computational methods that can effectively integrate scientific logic with deep learning architectures. Here we introduce a multi-task large reasoning model designed to