Large language models (LLMs) demonstrate strong general language capabilities but remain limited in chemical reasoning, particularly for tasks requiring structured, mechanistic understanding of molecular reactions. We present Knowledge Graph Reaction LLM (KGRxn-LLM), a framework that augments LLMs with a hierarchical chemical knowledge graph (KG) to ground reasoning in molecular transformations and reaction patterns. Existing benchmarks primarily emphasize reaction or molecular fact recall, providing limited assessment of reaction-level mechanistic reasoning. To address this gap, we introduce KGRxn-Bench, a benchmark of 1,200 questions designed to evaluate LLMs on reaction-centric reasoning tasks, including functional group identification, reaction type classification, and product and reagent prediction. Experimental results show that our approach of grounding LLMs in structured KG substantially improves performance across multiple tasks and model backbones, outperforming domain-specific fine-tuned models on KG-covered splits and most hold-out splits.

Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. This raises important concerns about their robustness and reliability in scientific applications. To support more rigorous evaluation of LLMs in chemical reasoning, we present the MolErr2Fix benchmark, designed to assess LLMs on error detection and correction in molecular descriptions. Unlike existing benchmarks focused on molecule-to-text generation or property prediction, MolErr2Fix emphasizes fine-grained chemical understanding. It tasks LLMs with identifying, localizing, explaining, and revising potential structural and semantic errors in molecular descriptions. Specifically, MolErr2Fix consists of 1,193 fine-grained annotated error instances. Each instance contains quadruple annotations, i.e., (error type, span location, the explanation, and the correction). These tasks are intended to reflect the types of reasoning and verification required in real-world chemical communication. Evaluations of current state-of-the-art LLMs reveal notable performance gaps, underscoring the need for more robust chemical reasoning capabilities. MolErr2Fix provides a focused benchmark for evaluating such capabilities and aims to support progress toward more reliable and chemically informed language models. All annotations and an accompanying evaluation API will be publicly released to facilitate future research.