Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving

ArXi:2603.07642v1 Announce Type: new Large language models (LLMs) with reasoning abilities have nstrated growing promise for tackling complex scientific problems. Yet such tasks are inherently domain-specific, unbounded and open-ended, demanding exploration across vast and flexible solution spaces. Existing approaches, whether purely learning-based or reliant on carefully designed workflows, often suffer from limited exploration efficiency and poor generalization.