Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 13 Oct 2025.
Builds on
9 of 17 references.
References · 9
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
- 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
- 2024MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineeringcited by 469
- 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 383
- 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentationcited by 366
- 2023CodeT5+: Open Code Large Language Models for Code Understanding and Generationcited by 333
- 2024AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLcited by 87
- 2023AutoML-GPT: Automatic Machine Learning with GPTcited by 81
- 2023AutoML-GPT: Large Language Model for AutoMLcited by 6
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Citing papers per year: 2026: 2.
2 papers.