Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911207764525056 |
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| author | Sahney, Arjun Gorthi, Ram Łastowski, Cezary Vega, Javier |
| author_facet | Sahney, Arjun Gorthi, Ram Łastowski, Cezary Vega, Javier |
| contents | We present Operand Quant, a single-agent, IDE-based architecture for autonomous machine learning engineering (MLE). Operand Quant departs from conventional multi-agent orchestration frameworks by consolidating all MLE lifecycle stages -- exploration, modeling, experimentation, and deployment -- within a single, context-aware agent. On the MLE-Benchmark (2025), Operand Quant achieved a new state-of-the-art (SOTA) result, with an overall medal rate of 0.3956 +/- 0.0565 across 75 problems -- the highest recorded performance among all evaluated systems to date. The architecture demonstrates that a linear, non-blocking agent, operating autonomously within a controlled IDE environment, can outperform multi-agent and orchestrated systems under identical constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering Sahney, Arjun Gorthi, Ram Łastowski, Cezary Vega, Javier Artificial Intelligence We present Operand Quant, a single-agent, IDE-based architecture for autonomous machine learning engineering (MLE). Operand Quant departs from conventional multi-agent orchestration frameworks by consolidating all MLE lifecycle stages -- exploration, modeling, experimentation, and deployment -- within a single, context-aware agent. On the MLE-Benchmark (2025), Operand Quant achieved a new state-of-the-art (SOTA) result, with an overall medal rate of 0.3956 +/- 0.0565 across 75 problems -- the highest recorded performance among all evaluated systems to date. The architecture demonstrates that a linear, non-blocking agent, operating autonomously within a controlled IDE environment, can outperform multi-agent and orchestrated systems under identical constraints. |
| title | Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.11694 |