Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914420825784320 |
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| author | Zhu, Xinyu Cai, Yuzhu Liu, Zexi Zheng, Bingyang Wang, Cheng Ye, Rui Zhang, Yuzhi Zhang, Linfeng E, Weinan Chen, Siheng Wang, Yanfeng |
| author_facet | Zhu, Xinyu Cai, Yuzhu Liu, Zexi Zheng, Bingyang Wang, Cheng Ye, Rui Zhang, Yuzhi Zhang, Linfeng E, Weinan Chen, Siheng Wang, Yanfeng |
| contents | The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative correction over experimental cycles spanning days or weeks. While Large Language Models (LLMs) have demonstrated prowess in short-horizon reasoning, they are easily overwhelmed by execution details in the high-dimensional, delayed-feedback environments of real-world research, failing to consolidate sparse feedback into coherent long-term guidance. Here, we present ML-Master 2.0, an autonomous agent that masters ultra-long-horizon machine learning engineering (MLE) which is a representative microcosm of scientific discovery. By reframing context management as a process of cognitive accumulation, our approach introduces Hierarchical Cognitive Caching (HCC), a multi-tiered architecture inspired by computer systems that enables the structural differentiation of experience over time. By dynamically distilling transient execution traces into stable knowledge and cross-task wisdom, HCC allows agents to decouple immediate execution from long-term experimental strategy, effectively overcoming the scaling limits of static context windows. In evaluations on OpenAI's MLE-Bench under 24-hour budgets, ML-Master 2.0 achieves a state-of-the-art medal rate of 56.44%. Our findings demonstrate that ultra-long-horizon autonomy provides a scalable blueprint for AI capable of autonomous exploration beyond human-precedent complexities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_10402 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering Zhu, Xinyu Cai, Yuzhu Liu, Zexi Zheng, Bingyang Wang, Cheng Ye, Rui Zhang, Yuzhi Zhang, Linfeng E, Weinan Chen, Siheng Wang, Yanfeng Artificial Intelligence The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative correction over experimental cycles spanning days or weeks. While Large Language Models (LLMs) have demonstrated prowess in short-horizon reasoning, they are easily overwhelmed by execution details in the high-dimensional, delayed-feedback environments of real-world research, failing to consolidate sparse feedback into coherent long-term guidance. Here, we present ML-Master 2.0, an autonomous agent that masters ultra-long-horizon machine learning engineering (MLE) which is a representative microcosm of scientific discovery. By reframing context management as a process of cognitive accumulation, our approach introduces Hierarchical Cognitive Caching (HCC), a multi-tiered architecture inspired by computer systems that enables the structural differentiation of experience over time. By dynamically distilling transient execution traces into stable knowledge and cross-task wisdom, HCC allows agents to decouple immediate execution from long-term experimental strategy, effectively overcoming the scaling limits of static context windows. In evaluations on OpenAI's MLE-Bench under 24-hour budgets, ML-Master 2.0 achieves a state-of-the-art medal rate of 56.44%. Our findings demonstrate that ultra-long-horizon autonomy provides a scalable blueprint for AI capable of autonomous exploration beyond human-precedent complexities. |
| title | Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.10402 |