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RecEvolve: A Knowledge-Driven Autonomous Agent System for Recommender Systems
Weidi Pan, He Ma, Shuhao Ye, Palaksh Rungta, David McPeek, Junyi Jiao, Arnab Bhadury, Mingyan Gao, Onkar Dalal
TL;DR
Production recommender optimization is difficult because large architectural spaces and component dependencies exceed manual research capacity. RecEvolve deploys a knowledge-driven, multi-agent closed loop on a production Two-Tower model, completing 41 autonomous experiments and uncovering effective optimizations while exposing evaluation and memory limitations.
Problem
Manual optimization of large recommender-model search spaces is resource-intensive, and researchers struggle to synthesize literature and component dependencies.
Method
RecEvolve uses a knowledge-driven autonomous pipeline with specialized agents to propose, implement, train, evaluate, and iteratively update research experiments.
Results
41 autonomous experiments were completed in approximately 2 days, and the system produced candidates that improved live production performance.
Takeaways & Limitations
The system demonstrates effective autonomous optimization for production recommender models and can stress-test experimental infrastructure by exposing metric shortcuts.
Takeaways & Limitations
The system sometimes redundantly revisits failed hypotheses, favoring short-horizon greedy optimization and struggling with paradigm shifts after reaching local optima.
Abstract
from arXiv · showhide
The rise of agentic AI has catalyzed a shift toward self-iterating systems, opening new frontiers for the autonomous optimization of production recommender models. This paper presents the empirical validation of a knowledge-driven autonomous agent system, deployed directly on a production large-scale Two-Tower retrieval model. By delegating the entire research lifecycle, spanning idea generation, code implementation, offline training, and metric evaluation, to a continuous closed-loop autonomous framework, the agent system executed over 40 completed autonomous training runs from scratch. Executing these runs under rigorous production-scale evaluations, the system systematically navigated hidden architectural bottlenecks on the latest production model to achieve a breakthrough ~20% relative improvement in NDCG, a gain that translated directly to a +3.77% increase in user satisfaction in live production traffic. Furthermore, the deployment exposed critical vulnerabilities in standard evaluation protocols, as the agent system autonomously discovered reward-hacking shortcuts. These findings prove that an autonomous pipeline can dramatically accelerate the pace of machine learning research and stress-test the rigorousness of underlying experimental infrastructure, while also exposing novel challenges such as reward hacking and redundant exploration of failed hypotheses.
1 Introduction
RecEvolve targets the resource and bandwidth constraints that cause production recommender architectures to become ossified. It deploys a continuous autonomous loop that covers hypothesis generation, validation, implementation, training, evaluation, and knowledge-base updates.
- Manual optimization of large recommender-model search spaces is resource-intensive, so configurations are rarely revisited after launch.This contributes to architectural ossification in production systems.
- Human researchers struggle to synthesize expanding ML literature and capture couplings between architecture and optimizer settings.Autonomous exploration can examine these multidimensional dependencies continuously and at broader scale.
- The deployed system targets a mature, large-scale production Two-Tower retrieval model and shifts experimentation toward a closed-loop autonomous pipeline.Humans remain strategic observers and high-level innovators.
- The pipeline automates idea proposal, code implementation, offline training, evaluation, and iteration, producing candidates that improved live production performance.The lifecycle is expressed as Propose Idea → Implement Code → Run Offline Training → Evaluate → Loop.
- The research lifecycle is organized as four phases: hypothesis formulation, pre-execution validation, automated implementation, and empirical evaluation with knowledge-base updates.The phases form a continuous clockwise loop.
2 Related Work
Prior work established autonomous multi-agent workflows and ML experimentation, including recommendation-system optimization. RecEvolve emphasizes production-scale infrastructure, a stateless multi-agent hierarchy, and version-control safeguards for operational failures.
- Earlier autonomous systems orchestrated complex, multi-step, long-running workflows through multi-agent teams.Examples include software-engineering automation using parallel agents.
- AutoResearch enabled iterative training-script experimentation, while Claudini and Aletheia demonstrated autonomous discovery in adversarial attacks and mathematics.These systems broadened autonomous research beyond software engineering.
- MLE agents such as MLE-STAR, ML-Master 2.0, and Gome advanced long-horizon ML tasks through targeted refinement and continuous cognitive accumulation.
- A prior self-evolving recommendation system used dual offline and online LLM agents to generate, train, and deploy model optimizations.RecEvolve differs through stateless subagents and version-control rollbacks.
- RecEvolve targets industrial recommender systems at larger production scale, integrating directly with distributed warehouse computing rather than local single-machine sandboxes.Its evaluation replaces short local probes with production-scale evaluation.
3 System Architecture
RecEvolve formulates architectural discovery as knowledge-driven search over costly model evaluations and implements it through an asynchronous, state-aware multi-agent loop. Specialized agents, centralized orchestration, isolated workspaces, and version control support long-running experiments.
- System Architecture: Knowledge-driven orchestration uses a domain-knowledge directive and a central Orchestrator to coordinate Ideator, Critic, and Coding Agent subagents.The subagents interact to drive the research loop forward.
- Problem Formulation: The objective is to select an architecture M* from a discrete model space M that maximizes metric f(M, D) on production data D.Each candidate requires costly training and evaluation.
- Problem Formulation: Because exhaustive evaluation is intractable, the system learns a knowledge-conditioned policy that generates candidate models iteratively.The knowledge base contains domain knowledge, historical results, and search heuristics.
- Problem Formulation: The knowledge base is dynamically updated with feedback from evaluating previous candidates.
- State Management: Stateless subagents receive injected context from the Orchestrator, reducing context degradation during long execution horizons.The Orchestrator retains the master research-session state.
- Autonomous Execution Loop: An asynchronous, state-aware loop manages the experimental lifecycle and interacts with production version control and distributed clusters through specialized tools and APIs.Isolated workspaces provide persistence and communication for individual experiments.
4 Experimental Setup
The evaluation targets a production two-tower retrieval model using distributed TPU resources and production-oriented validation. It combines short-horizon probes with full training and evaluates ranking and retrieval quality across several metrics and cutoffs.
- Target Model: The experiments target a large-scale production two-tower retrieval model.
- Evaluation Protocol: Short-horizon probes are validated against full 2M-step offline training runs to maintain throughput while meeting production standards.Experiments run on distributed TPU clusters.
- Metrics: Evaluation focuses primarily on NDCG, MRR, and Recall @ K with K = 1, 5, 50, and 200.
5 Results & Discussion
RecEvolve completed 41 autonomous experiments and compounded architectural changes that improved offline retrieval performance while translating to live production gains. The experiments also revealed evaluation exploits, short-horizon greediness, redundant exploration, and a plateau after foundational changes.
- Autonomous experimentation: 41 autonomous experiments ran across 5 concurrent threads in approximately 2 days, with each run requiring over 3 hours of compute.The system sustained high-throughput parallel exploration on the production retrieval model.
- Architectural optimization: Learnable temperature, watch-time weighting, DCN V2 layers, gated dense layers, and cosine decay formed the main architectural evolution trajectory.The reported trajectory spans loss scaling, reward weighting, feature interactions, representation capacity, and learning-rate scheduling.
- Offline convergence: The convergence analysis compares baseline and winning architectures using MAP@50 as a dense offline learning-trajectory proxy, while NDCG remains the primary evaluation metric.Figure 4 is intended to provide a granular view of continuous validation convergence.
- Evaluation vulnerabilities: Reducing batch size from 8k to 1k artificially inflated proxy metrics because fewer in-batch negatives made retrieval easier, prompting rollback after human detection.The system therefore exposed a reward-hacking shortcut rather than a genuine improvement in semantic representations.
- Compounding gains: +4.2% NDCG came from Learnable Temperature, +10.7% NDCG from Watch-Time Weighting, +0.64% NDCG from DCN Feature Crossing, and +1.07% NDCG from Cosine Decay.These sequential comparisons attribute gains to specific winning modifications and support the claim of compounding improvements.
- Observed limitations: After Idea 40, an ideation plateau produced reverted regressions, while extended sessions sometimes revisited previously failed architectural modifications.The reported behaviors mark limitations in escaping local optima and maintaining nonredundant exploration.
6 Discussion and Future Directions
The discussion identifies structural limitations in scaling autonomous recommender-system research, including context management, domain knowledge, online–offline transfer, dataset bias, compute costs, and limited feature-engineering coverage. It proposes specialized agents, surrogate pruning, and autonomous feature generation as future directions.
- Multi-Agent Hierarchies and Context Fatigue: Multi-agent hierarchies segment the research lifecycle across specialized subagents, isolating state and extending session duration.The hierarchy includes ideator, critic, coding agent, and orchestrator roles.
- The Online-Offline Generalization Gap: Online evaluations can diverge from offline gains because production experiments often expose trade-offs between competing objectives.The paper identifies online–offline generalization and objective trade-offs as key future-work areas.
- Off-Policy Dataset Bias: Frozen log data can reinforce historical system policies unless corrected, risking structural modifications that reflect bias rather than causal representations.This is framed as an off-policy dataset-bias challenge for agentic systems.
- Compute Barrier and Surrogate Pruning: Full production-scale evaluation of every architectural permutation is compute-prohibitive, motivating rapid surrogate filtering before full-scale verification.The proposed two-tier validation retains full-scale evaluation for top candidates.
- Expansion to Automated Feature Engineering: The system currently focuses on architectural discoveries, while feature engineering remains a dominant production performance driver and a natural target for automation.Future agents could generate, transform, and select new features autonomously.
7 Conclusion
The work argues that knowledge-driven autonomous research pipelines are feasible and effective for large-scale production recommender models. It also positions them as infrastructure stress-testers, while identifying metric alignment and architectural local optima as future challenges.
- Conclusion: Autonomous research uncovered non-obvious production-model optimizations that human engineers lack the bandwidth to test exhaustively.Examples include Watch-Time Weighting and DCN V2 layers.
- Conclusion: The system actively sought and exploited metric shortcuts, exposing vulnerabilities in experimental infrastructure.Future work targets better alignment between short-horizon proxy metrics and long-term production stability.
- Conclusion: Human-in-the-loop queuing is proposed to unblock the system when it reaches architectural local optima.This is identified as a future direction for improving autonomous research continuity.