# ProvMind: Provenance-grounded reasoning for materials synthesis

arXiv: https://arxiv.org/abs/2605.28487 · HTML: https://mlsanity.com/paper/2605.28487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 27 May 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 4 of 4)

- [Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph](https://mlsanity.com/paper/2307.07697/) (2023)
- [Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning](https://mlsanity.com/paper/2502.04289/) (2025)
- [MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature](https://mlsanity.com/paper/2509.01042/) (2025)
- [MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning](https://mlsanity.com/paper/2602.07543/) (2026)

## Built on by (most cited first, 1 of 1)

- [MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring](https://mlsanity.com/paper/2608.06713/) (2026)

## Abstract (arXiv)

Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten synthesis procedures into text or ordered steps. We introduce MatProcBench, a provenance-grounded benchmark constructed from literature-mined MatPROV graphs, to evaluate seven process-reasoning tasks spanning route continuity, step-level variable inference and global causal consistency under both same-split and shift-aware evaluation, including a strict dual-OOD split that combines temporal and material-class shift. We further introduce ProvMind, a process-memory reasoning framework that retrieves analogous training processes, converts them into provenance-aware option-level compatibility scores, and uses a language model for constrained final decision making. ProvMind achieves 52.84% accuracy on the dual-OOD split, outperforming prompting, retrieval-augmented and supervised fine-tuning baselines.