---
id: 20260912-T0-12
title: "Auto-RecSys用自主研究Agent做工业级推荐系统，解决长反馈周期难题"
title_en: "Auto-RecSys Uses Autonomous Research Agents for Industry-Scale Recommender Systems"
url: https://ai.daily.yangsir.net/daily/20260912-T0-12
issue_date: 2026-09-12
publish_date: 2026-09-11T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2609.10922
---

# Auto-RecSys用自主研究Agent做工业级推荐系统，解决长反馈周期难题

一篇新论文提出Auto-RecSys，将自主研究Agent范式扩展到工业级推荐模型。自主研究Agent已能自动完成假设生成、实验执行和迭代优化，但在推荐系统场景下面临两大挑战：反馈周期长，以及实验成本高。论文针对这些问题设计了适配方案，目标是让Agent在工业推荐系统中自动完成模型迭代。目前该工作尚处于论文阶段，具体实验结果和落地效果有待进一步验证。

## English Version

**Auto-RecSys Uses Autonomous Research Agents for Industry-Scale Recommender Systems**

A new paper introduces Auto-RecSys, extending the autonomous research agent paradigm to industry-scale recommendation models. While autonomous research agents can already automate hypothesis generation, experiment execution, and iterative refinement, scaling this to recommender systems introduces two challenges: long feedback loops and high experiment costs. The paper designs solutions tailored to these problems, aiming to let agents automate model iteration in industrial recommender systems. The work remains at the paper stage, with experimental results and deployment outcomes yet to be validated.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2609.10922)

**详情页**：https://ai.daily.yangsir.net/daily/20260912-T0-12

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