Title: MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs
Primary area: datasets and benchmarks
Keywords: chemical language model, chemical reasoning model, chemistry, large language model, molecular graph, molecular structure

Abstract: Large Language Models (LLMs) are increasingly applied to chemistry, tackling tasks such as molecular name conversion, captioning, text-guided generation, and property or reaction prediction. A molecule’s properties are fundamentally determined by its composition and structure, encoded in its molecular graph; thus, reasoning about molecular properties requires understanding and reasoning over the molecular structure. Yet, most existing benchmarks emphasize general chemical knowledge, rely on literature or surrogate labels that risk leakage or bias, or reduce evaluation to multiple-choice questions.
We introduce MolecularIQ, a molecular structure reasoning benchmark focused exclusively on symbolically verifiable tasks. 
MolecularIQ spans three orthogonal axes — molecular complexity, multi-task load, and reasoning complexity — covering feature counting, index-based feature attributions, and constrained generation.
MolecularIQ enables fine-grained evaluation of reasoning over molecular graphs and produces capability fingerprints that localize model failures to specific tasks and molecular regimes. This provides actionable insights into the strengths and limitations of current chemistry LLMs and guides the development of models that reason faithfully over molecular structure.
On MolecularIQ, large MoE models with higher reasoning budgets lead across categories, while chemistry-tuned LLMs underperform their generalist bases, indicating limited transfer from narrow task fine-tuning.

TL;DR: We propose MolecularIQ, a molecular structure reasoning benchmark focused exclusively on symbolically verifiable tasks.