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cs.DB2026
KnowFeat: Knowledge-Guided Feature Engineering via LLM Agents
Chengsong You, Wangyue Li, Weiqiao Que +5
Automated feature engineering with large language models (LLMs) can produce semantically meaningful features for tabular data, yet existing methods lack structured domain knowledge…
cs.CL2026
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering
Taolin Zhang, Dongyang Li, Chen Chen +5
Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These diffic…
cs.CL2026
Taming "Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution
Taolin Zhang, Pukun Zhao, Qizhou Chen +5
Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To improve overall efficiency, existing methods o…