AutoLR: Automating the Path from Research to Launch Review in Industrial Recommender Systems

• URL: http://arxiv.org/abs/2609.04871v1
• Date: Fri, 04 Sep 2026 08:30:36 GMT
• Title: AutoLR: Automating the Path from Research to Launch Review in Industrial Recommender Systems
• Abstract summary: In textbfDASHEN, NetEase's gaming-community app, engineers identify promising directions from research papers, technical reports, and prior production experiments.<n> Promising candidates are then advanced to online A/B tests, and those demonstrating robust gains are submitted to Launch Review.<n>We present textbfAutoLR, initially built as textbfAuto Launch Review and later extended upstream into an autonomous research-to-launch harness.
• Score: 7.383658399787325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving an industrial recommender is an iterative research-and-engineering process rather than a direct path from idea to deployment. In \textbf{DASHEN, NetEase's gaming-community app}, algorithm engineers typically identify promising directions from research papers, technical reports, and prior production experiments; reproduce or adapt the underlying methods; implement them in the production codebase; and evaluate the resulting models through training and offline experiments. Promising candidates are then advanced to online A/B tests, and those demonstrating robust gains are submitted to Launch Review---the internal gate for full-traffic rollout. Large language models (LLMs) can assist with individual stages of this workflow, but the overall process remains human-dependent without a harness that can reliably coordinate them across long-running, often multi-day experimental cycles. We present \textbf{AutoLR}, initially built as \textbf{Auto Launch Review} and later extended upstream into an autonomous research-to-launch harness. AutoLR combines three system mechanisms: a \textbf{multi-expert council} that debates and adversarially reviews proposals; a \textbf{deterministic evidence-weighted exploration--exploitation selector} that allocates a limited trial budget across candidate directions and uses Council reranking; and a layered knowledge system that combines external research, production-system knowledge, and DASHEN-specific domain knowledge---such as game communities, player characteristics, and content-interaction patterns---with posterior evidence from configurations, patches, logs, failures, and offline outcomes. LLM agents perform semantic reasoning and code generation, while deterministic controllers retain authority over execution, metric extraction, guardrails, and persistent state transitions.

Related papers

• Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System [20.228106034590336]
  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.
  arXiv  Detail & Related papers  (2026-09-10T00:09:18Z)
• The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows [72.9051182568928]
  We present ReASearch, a unified framework for reasoning-driven optimization.<n>With a shared agent loop and domain-specific tools, ReASearch instantiates the exact same scaffold to optimize prompts, programs, and ML.<n>We observe that complex search behaviors emerge naturally from the agent's reasoning process.
  arXiv  Detail & Related papers  (2026-08-07T02:15:39Z)
• AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems [82.01232437066487]
  AgentX is a production-deployed multi-agent system that restructures this production function.<n>It autonomously generates, implements, evaluates, and learns from recommendation experiments.<n>AgentX orchestrates four tightly coupled stages in a closed loop.
  arXiv  Detail & Related papers  (2026-06-25T10:42:28Z)
• AI for Auto-Research: Roadmap & User Guide [107.0834449839233]
  We present an end-to-end analysis of AI across the complete research lifecycle.<n>We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy.<n>We show that greater automation can obscure rather than eliminate failure modes.
  arXiv  Detail & Related papers  (2026-05-18T17:08:26Z)
• NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation [59.19858972597063]
  LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automation for whom?<n>We propose NanoResearch, a multi-agent framework that addresses these gaps through tri-level co-evolution.<n>A skill bank distills recurring operations into compact procedural rules reusable across projects.<n>A memory module maintains user- and project-specific experience that grounds planning decisions in each user's research history.<n>A label-free policy learning converts free-form feedback into persistent parameter updates of the planner, reshaping subsequent coordination.
  arXiv  Detail & Related papers  (2026-05-11T16:33:47Z)
• Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning [79.88942231770629]
  Reinforcement learning (RL) has become a central post-training tool for improving the reasoning abilities of large language models (LLMs)<n>We formalize rollout pipelines with unified notation and introduce Generate-Filter-Control-Replay (GFCR)<n>We synthesize methods spanning RL with verifiable rewards, process supervision, judge-based gating, guided and tree/segment rollouts, adaptive compute allocation, early-exit and partial rollouts, throughput optimization, and replay/recomposition for self-improvement.
  arXiv  Detail & Related papers  (2026-04-08T00:53:29Z)
• AI-Supervisor: Autonomous AI Research Supervision via a Persistent Research World Model [1.14219428942199]
  Existing automated research systems operate as stateless, linear pipelines.<n>We present textbfAI-Supervisor, a multi-agent orchestration framework.<n>Agents provide end-to-end AI research supervision driven by human interests.
  arXiv  Detail & Related papers  (2026-03-25T15:16:51Z)
• KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems [36.17807193758863]
  KompeteAI is a novel AutoML framework with dynamic solution space exploration.<n>We introduce KompeteAI, a novel AutoML framework with dynamic solution space exploration.<n>We propose Kompete-bench to address limitations in MLE-Bench, where KompeteAI also achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2025-08-13T20:29:56Z)
• On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software [0.577182115743694]
  Automated Driving System (ADS) is a safety-critical software system responsible for the interpretation of the vehicle's environment.<n>Development of ADS requires rigorous processes to verify, validate, assess, and qualify the code before it can be deployed in the vehicle.<n>This study developed and evaluated a prototype for automatic code generation and assessment.
  arXiv  Detail & Related papers  (2025-04-02T21:35:11Z)
• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.