3 papers
cs.AI2026
ContextPipe: Database-Inspired Context Assembly for Long-Horizon Agents
Peng Xu, Zuyu Zhang, Yuze Sun +3
Long-horizon large language model (LLM) agents require context assembly: the runtime must decide what to include in each prompt, in what order, and when to compact history under a…
cs.CL2026
INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment
Yutong Zhang, Jianshuo Dong, Peng Xu +5
As large language models (LLMs) are deployed as autonomous agents, safety failures increasingly involve consequential actions. We study agentic misalignment, where agents take harm…
cs.AI2026
CLAP: Closed-Loop Training, Evaluation, and Release Control for Domain Agent Post-training
Fangfei Li, Chenyang Zhao, Long Wang +3
Domain agents often face noisy business data, uncertain post-training gains, offline/application mismatch, and adapter-release risk. This paper presents CLAP (Closed-Loop Agent Pos…