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Research questionHow can industrial recommender teams reliably coordinate long-running experiments from research through launch review?Industrial recommender improvements require repeated handoffs among research, code changes, training, offline evaluation, online tests, and launch decisions. These activities can span multiple days, while evidence from configurations, logs, failures, and outcomes must remain usable across iterations.
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Machine Learning
Multi-agent Systems
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Latest papersRecent research connected to this question, newest first.AutoLR: Automating the Path from Research to Launch Review in Industrial Recommender SystemsThe evidence concerns AutoLR in NetEase’s DASHEN gaming-community app, covering research-to-launch work with LLM agents, multi-expert review, deterministic trial selection and execution controls, and layered production knowledge. The supplied description does not establish quantitative results or evidence beyond this application context.research paper · Sep 4, 2026
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