Jingru Gan

Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions. The vast combinatorial search space makes this task challenging even for expert chemists. Traditional methods combine tree search with offline-trained value networks that score candidates in isolation, without reasoning about complete multi-step routes. Recent work leverages Large Language Models (LLMs) for this task, but relies on simple interfaces that limit exploration of the full search space. We introduce RetroAgent, an LLM agent that bridges symbolic search and neural reasoning through a harness with structured memory. Through memory and chemistry tools, the agent observes the full search state, including explored routes, available alternatives, and properties of intermediates, enabling informed decisions grounded in both global progress and domain knowledge. Experiments on in-distribution and out-of-distribution benchmarks demonstrate that RETROAGENT delivers strong performance and generalization.

This paper studies the problem of constrained novel crystal discovery, which aims to identify a set of physical feasible and property-aligned material candidates within a complicated search space. Existing approaches typically integrate large language models (LLMs) with domain knowledge of material properties and constraints for material discovery. However, these approaches usually adopt shallow memory mechanisms or unstructured toolsets, leading to suboptimal performance. Towards this end, we propose a novel approach named Structured Action Orchestration with Trio-Reflection (CrystalSTAR) for constrained novel crystal discovery. The core of our CrystalSTAR is to incorporate hierarchical memories into a structured action orchestration from modification scopes and dominant components, facilitating a smooth and efficient crystal discovery procedure. In particular, our CrystalSTAR first formulates the toolset as a structured action space along two orthogonal dimensions, resulting in four complementary tools for material exploration. More importantly, we adopt a trio-reflection mechanism to guide the trial process in a hierarchical manner. Here, our bottom-up memory records local anomalies, stuck trajectories, and cross-run knowledge, enabling effective strategy planning with grounding evidence. Extensive experiments across multiple benchmark datasets validate the effectiveness of our proposed CrystalSTAR in comparison to extensive state-of-the-art approaches. Our codebase is open-sourced at https://github.com/oldwizard7/CrystalSTAR