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News/Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering
arxiv
PublishedJuly 30, 2026 at 4:00 AM
▲bullish

Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.25090v1 Announce Type: new Abstract: Machine learning engineering (MLE) tasks require long-horizon decision making over iterative solution debugging and refinement, under expensive and feedback-driven environment interactions. Developing and training a monolithic agent for such tasks is f

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Discussion
Mentioned models
04
  • 01
    Matryoshka Agent
  • 02
    Qwen3-4B-Instruct
  • 03
    o4-mini
  • 04
    Qwen3-30B-Coder
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#artificial intelligence#hierarchical frameworks#long-horizon tasks

No replies yet. Be first.

Mentioned models
04
  • 01
    Matryoshka Agent
  • 02
    Qwen3-4B-Instruct
  • 03
    o4-mini
  • 04
    Qwen3-30B-Coder
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#artificial intelligence#hierarchical frameworks#long-horizon tasks
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Originally published on arxiv ↗
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