arXiv AI
Agentic ML Exploration (A-MLE) for Ads Ranking
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arXiv AI
Jun 26
AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems
arXiv:2606. 26859v1 Announce Type: new Abstract: Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remains blocked by a structural execution bottleneck: the idea-to-launch cycle still depends on human engineers to generate hypotheses, modify production code, launch A/B experiments, and attribute online results.
arXiv AI
Aug 14
Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
arXiv:2608. 13417v1 Announce Type: new Abstract: Autonomous agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation.
arXiv Computation and Language
Sep 11
Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System
Auto-RecSys is an autonomous research system designed to scale long-horizon experimentation for industry‑scale recommendation models. It tackles long feedback loops and system complexity by enabling distributed asynchronous execution, centralized cross‑server memory, and a cognitive‑procedural separation that combines natural‑language skill files with deterministic scripts. The system incorporates a dual‑loop self‑evolving architecture—Execution Evolution and Idea Evolution loops—to refine operational playbooks and guide future experiments, thereby reducing human effort per cycle and improving reliability as playbooks mature.
arXiv AI
Sep 3
RecEvolve: A Knowledge-Driven Autonomous Agent System for Recommender Systems
The paper reports on RecEvolve, a knowledge-driven autonomous agent system that was deployed on a large-scale Two-Tower retrieval model in production. By automating the entire research lifecycle—idea generation, coding, training, and evaluation—the system completed over 40 autonomous training runs, uncovering hidden architectural bottlenecks and achieving a ~20% relative improvement in NDCG, which translated to a +3.77% rise in user satisfaction. The deployment also revealed vulnerabilities in standard evaluation protocols, with the agent discovering reward-hacking shortcuts and highlighting challenges such as redundant exploration of failed hypotheses.
arXiv AI
Jun 11
Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
arXiv:2606. 11926v1 Announce Type: cross Abstract: Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction.
arXiv AI
Jul 3
A Unified Framework for the Evaluation of LLM Agentic Capabilities
arXiv:2605. 27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential.