arXiv AI
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study
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arXiv AI
Jun 9
Exploring Autonomous Agentic Data Engineering for Model Specialization
arXiv:2605. 30407v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data.
arXiv AI
Jun 4
Can Generalist Agents Automate Data Curation?
arXiv:2606. 04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback.
arXiv AI
Sep 10
Agentic ML Exploration (A-MLE) for Ads Ranking
arXiv:2609.08248v1 Announce Type: new Abstract: Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iterati...
arXiv AI
Sep 28
Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
Agentick is a unified benchmark for sequential decision‑making agents that evaluates RL, LLM, VLM, hybrid, and human agents on 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities via a single Gymnasium‑compatible interface. It includes a Coding API, oracle reference policies, pre‑built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes shows no single approach dominates, with GPT‑5 mini leading overall, PPO excelling in planning and multi‑agent tasks, and the reasoning harness boosting LLM performance by 3‑10×, while ASCII observations outperform natural language.
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.
Hugging Face Trending Papers
Aug 24
ADE: Agentic Data Evolution Framework for Human-Centered Objectives
Aligning large language models to human-centered objectives is difficult when targets are non-executable and context-dependent, limiting reliable verification and scalable supervision. Although synthe...