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cs.AI2026
Agentic ML Exploration (A-MLE) for Ads Ranking
Erwin Gao, Vinodh Kumar Sunkara, Jingyi Guan +36
Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iteration - the cycles of research,…
cs.AI2025
Hide and Seek with LLMs: An Adversarial Game for Sneaky Error Generation and Self-Improving Diagnosis
Rui Zou, Mengqi Wei, Yutao Zhu +3
Large Language Models (LLMs) excel in reasoning and generation across domains, but still struggle with identifying and diagnosing complex errors. This stems mainly from training ob…
cs.AI2025
RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation
Zhentao Xie, Chengcheng Han, Jinxin Shi +4
Although multi-agent systems based on large language models show strong capabilities on multiple tasks, they are still limited by high computational overhead, information loss, and…