SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction
Year: 2025
Type: article
Abstract: Small Language Models offer an efficient alternative for structured information extraction. We present SLM-MATRIX , a multi-path collaborative reasoning and verification framework based on SLMs, designed to extract material names, numerical values, and physical units from materials science literature. The framework integrates three complementary reasoning paths: a multi-agent collaborative path, a generator–discriminator path, and a dual cross-verification path. SLM-MATRIX achieves an accuracy of 92.85% on the BulkModulus dataset and reaches 77.68% accuracy on the MatSynTriplet dataset, both outperforming conventional methods and single-path models. Moreover, experiments on general reasoning benchmarks such as GSM8K and SVAMP validate the framework’s strong generalization capability. Ablation studies evaluate the effects of agent number, Mixture-of-Agents (MoA) depth, and discriminator design on overall performance. Overall, SLM-MATRIX presents an effective approach for high-quality material information extraction in resource-constrained and offers new insights into structured scientific text understanding tasks.
Source: npj Computational Materials
Authors: Xin Li, Zhixuan Huang, Shu Quan, Cheng Peng, Xiaoming Ma
Institution: Peking University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,490
OpenAlex ID: W4412874265