REVIEW 10 cited by
Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
read the original abstract
Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors. Achieving substantial improvements in these areas is a non-trivial task, traditionally relying on extensive manual iterations to test new hypotheses. We propose a self-evolving system that leverages Large Language Models (LLMs), specifically those from Google's Gemini family, to autonomously generate, train, and deploy high-performing, complex model changes within an end-to-end automated workflow. The self-evolving system is comprised of an Offline Agent (Inner Loop) that performs high-throughput hypothesis generation using proxy metrics, and an Online Agent (Outer Loop) that validates candidates against delayed north star business metrics in live production. Our agents act as specialized Machine Learning Engineers (MLEs): they exhibit deep reasoning capabilities, discovering novel improvements in optimization algorithms and model architecture, and formulating innovative reward functions that target long-term user engagement. The effectiveness of this approach is demonstrated through several successful production launches at YouTube, confirming that autonomous, LLM-driven evolution can surpass traditional engineering workflows in both development velocity and model performance.
Forward citations
Cited by 10 Pith papers
-
What Do Evolutionary Coding Agents Evolve?
Evolutionary coding agents achieve most benchmark gains through a small subset of edit types and by cycling previously deleted code lines rather than developing new algorithmic structures.
-
SAGER: Self-Evolving User Policy Skills for Recommendation Agent
SAGER equips LLM recommendation agents with per-user evolving policy skills via two-representation architecture, contrastive CoT diagnosis, and skill-augmented listwise reasoning, yielding SOTA gains orthogonal to mem...
-
NeuroClaw Technical Report
NeuroClaw is a domain-specialized multi-agent framework with NeuroBench benchmark that improves executability and reproducibility for multimodal neuroimaging research.
-
NeuroClaw Technical Report
NeuroClaw introduces a three-tier multi-agent framework and NeuroBench benchmark that improve executability and reproducibility scores for neuroimaging tasks when used with multimodal LLMs.
-
NOVA: A Verification-Aware Agent Harness for Architecture Evolution in Industrial Recommender Systems
NOVA deploys a level-aware agent system with architecture gradient and verification cascade for recommender architecture evolution, reporting 54.5-60% effective pass rates, 13x faster cycles, and online GMV gains of 1...
-
NOVA: A Verification-Aware Agent Harness for Architecture Evolution in Industrial Recommender Systems
NOVA introduces a level-aware agent harness with architecture gradient and verification cascade to automate recommender architecture evolution while reducing silent failures and human effort.
-
EvoRec: Self Evolving Agentic Recommender Systems
EvoRec deploys four collaborating LLM agents that co-evolve recommendation models and their optimization methods, reporting up to 5.54% offline gains and 1.85% revenue lift in an online A/B test.
-
VirtualMLE: A Virtual ML Engineer that Optimizes Sequential Recommenders
VirtualMLE deploys an LLM agent with execution-reflection-memory to tune sequential recommenders, reaching competitive quality on Amazon benchmarks with fewer trials and transferring heuristics across datasets.
-
AgenticRecTune: Multi-Agent with Self-Evolving Skillhub for Recommendation System Optimization
AgenticRecTune deploys five LLM agents (Actor, Critic, Insight, Skill, Online) and a self-evolving Skillhub to handle end-to-end configuration optimization for multi-stage recommendation systems.
-
AgenticRecTune: Multi-Agent with Self-Evolving Skillhub for Recommendation System Optimization
AgenticRecTune deploys Actor, Critic, Insight, Skill, and Online agents plus a self-evolving Skillhub to propose, filter, test, and learn from recommendation system configurations using Gemini LLMs.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.