SOURCE-LINKED INTELLIGENCE
Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System
Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale recommendation models introduces two challenges: (1) long feedback loops, where model training can take days, making serial iteration prohibitively slow and requiring parallel exploration across multiple research directions; and (2) system complexity, where large configurations, fragile infrastructure dependencies, and multi-day GPU jobs require robust and recoverable execution. We present Auto-RecSys, an autonomous re
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T00:09:18.000Z
First collected: 2026-09-20T19:12:12.556Z. This is not the publication date.