Think in Fragments: Fragment-Centric Reasoning and Credit Assignment for LLM Molecular Optimization

Large language models (LLMs) provide a flexible interface for molecular optimization, yet most LLM-based methods describe intended changes using chemical concepts while generating molecules token by token and learning from a single molecule-level reward. This granularity mismatch makes it difficult to faithfully execute chemically meaningful edits and assign credit to the structural changes responsible for property improvement. We propose , a fragment-centric framework that uses functional groups and molecular scaffolds as a common interface for reasoning, editing, experience reuse, and policy optimization. combines hierarchical retrieval of task-relevant fragments, an evolvable dual-trace memory of optimization experience, and fragment-aware group relative policy optimization (FA-GRPO), which estimates the contributions of edited fragments and propagates fragment-level advantages to their corresponding output tokens. Across the 23 tasks of the Practical Molecular Optimization benchmark under a 1,000-query budget, achieves higher aggregate Top-10 AUC than the strongest evaluated LLM baseline across the tested backbone models. These results demonstrate the effectiveness of aligning molecular reasoning and learning with chemically meaningful fragments for improving the sample efficiency of LLM-based molecular optimization.