{"ID":2856513,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11694","arxiv_id":"2510.11694","title":"Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.11694","url_pdf":"https://arxiv.org/pdf/2510.11694v1","authors":"[\"Arjun Sahney\",\"Ram Gorthi\",\"Cezary Łastowski\",\"Javier Vega\"]","published":"2025-10-13T17:54:02Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"LoRA\"]","has_code":false}