Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System

• URL: http://arxiv.org/abs/2609.10922v1
• Date: Thu, 10 Sep 2026 00:09:18 GMT
• Title: Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System
• Abstract summary: Auto-RecSys is an autonomous research system for long-horizon experimentation on industry-scale recommendation models.<n>Auto-RecSys significantly reduces the human time required per experiment cycle and improves execution reliability as its playbooks mature.
• Score: 20.228106034590336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 research system for long-horizon experimentation on industry-scale recommendation models. Auto-RecSys addresses these challenges through three harness designs: (1) distributed asynchronous execution for running multiple experiments in parallel across servers, (2) centralized cross-server memory for persistent and recoverable execution across sessions and failures, and (3) cognitive-procedural separation, where natural-language skill files guide LLM reasoning while deterministic scripts enforce operational correctness. Auto-RecSys further employs a dual-loop self-evolving architecture: an Execution Evolution Loop in which model-specific playbooks accumulate operational knowledge by recording failed attempts and crystallizing successful pipelines, and an Idea Evolution Loop in which experimental outcomes inform subsequent ideation. Evaluated on recommendation models, Auto-RecSys significantly reduces the human time required per experiment cycle and improves execution reliability as its playbooks mature.

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