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DisCo agent distills ML knowledge into skills, boosting research performance · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

A new research paper introduces DisCo, a skill-powered agent designed to enhance autonomous machine learning research. DisCo distills operational knowledge from GitHub repositories and research papers into reusable skills. This distilled knowledge significantly boosts agent performance on various benchmarks, including MLE-bench and PaperBench, when integrated with a GPT-5.5 backbone. The AREX-Skill Library, a component of DisCo, contains over 5,000 verified skills derived from 1,000 ML repositories. AI

IMPACT Enhances autonomous ML research capabilities by providing agents with distilled operational knowledge, leading to significant performance gains.

RANK_REASON The cluster describes a research paper detailing a new method and agent for distilling knowledge into reusable skills for autonomous ML research.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

DisCo agent distills ML knowledge into skills, boosting research performance · 2 sources tracked

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The cluster describes a research paper detailing a new method and agent for distilling knowledge into reusable skills for autonomous ML research.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jianlyu Chen, Yuyang Hu, Hongjin Qian, Jiawei Liu, Wenqing Wei, Xiaolong Chen, Defu Lian, Zhicheng Dou, Chaozhuo Li, Qiwei Ye, Zheng Liu ·

    Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills

    arXiv:2609.02749v1 Announce Type: new Abstract: Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and verification, but this architecture still leaves domai…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills

    DisCo is a research agent that distills operational knowledge into reusable skills, significantly improving autonomous ML research performance across benchmarks.

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