SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction
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 l...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01719-x |
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| _version_ | 1849331838812684288 |
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| author | Xin Li Zhixuan Huang Shu Quan Cheng Peng Xiaoming Ma |
| author_facet | Xin Li Zhixuan Huang Shu Quan Cheng Peng Xiaoming Ma |
| author_sort | Xin Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c18ab65eaff64bf7880e197751175db9 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-c18ab65eaff64bf7880e197751175db92025-08-20T03:46:23ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111710.1038/s41524-025-01719-xSLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extractionXin Li0Zhixuan Huang1Shu Quan2Cheng Peng3Xiaoming Ma4Environmental Finance Lab, School of Environment and Energy, Peking University Shenzhen Graduate SchoolEnvironmental Finance Lab, School of Environment and Energy, Peking University Shenzhen Graduate SchoolEnvironmental Finance Lab, School of Environment and Energy, Peking University Shenzhen Graduate SchoolEnvironmental Finance Lab, School of Environment and Energy, Peking University Shenzhen Graduate SchoolEnvironmental Finance Lab, School of Environment and Energy, Peking University Shenzhen Graduate SchoolAbstract 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.https://doi.org/10.1038/s41524-025-01719-x |
| spellingShingle | Xin Li Zhixuan Huang Shu Quan Cheng Peng Xiaoming Ma SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction npj Computational Materials |
| title | SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction |
| title_full | SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction |
| title_fullStr | SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction |
| title_full_unstemmed | SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction |
| title_short | SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction |
| title_sort | slm matrix a multi agent trajectory reasoning and verification framework for enhancing language models in materials data extraction |
| url | https://doi.org/10.1038/s41524-025-01719-x |
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