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cs.CL2026
Prune the Summary, Not the History: Source-Judged Compaction for Long-Horizon Agents
Guanghui Min, Chen Chen
Long-horizon LLM agents rely on context compaction: when the interaction history outgrows the context window, a compressor replaces it with a natural-language summary. We show that…
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…