MARS: Reflective Tree Search for Molecular Structure Elucidation from Mass Spectra

Mass spectrometry (MS) is a fundamental tool for molecular identification, yet automatically elucidating molecular structures from MS spectra remains a long-standing challenge. Existing approaches either rely on retrieval-based methods constrained by database coverage, or on one-shot *de novo* generation, which often yields chemically implausible structures due to the lack of verification mechanisms. Inspired by the iterative reasoning workflow of human experts, we propose **MARS**, a **M**olecular **A**gent framework for **R**eflective Tree **S**earch based on MS spectra. MARS reformulates one-shot molecular prediction as a structured, reflection-driven search process powered by LLMs, following a three-stage expert workflow of intuitive hypothesis, logical reflection, and local refinement. It first retrieves structurally similar references from a large-scale external knowledge base, and an Intuitive-Prior Chain-of-Thought (IP-CoT) synthesizes them with spectral evidence into high-quality initial candidates. A Spectral Diagnostic Chain-of-Thought (SD-CoT) then translates spectrum-level discrepancies into verbalized correction signals, which guide a Monte Carlo Tree Search (MCTS) to iteratively refine candidates within the chemical space. MARS achieves competitive performance on NPLIB1 and MolPuzzle datasets and generalizes well to real wastewater spectra under distribution shifts, highlighting the value of combining prior-guided hypotheses with spectrum-grounded structural refinement. Our code can be found at https://anonymous.4open.science/r/MARS-DC0B.