Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models 事件

Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models arXiv:2605.27703v1 Announce Type: new Abstract: Large Language Models are increasingly deployed inside agentic systems, where they must follow structured protocols, adapt to evolving states, and operate under memory, latency, and cost constraints. In such regimes, prompt extension is unreliable: growing contexts can push compact models outside their effective prompt domain, while deployment-time fin