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 fine-tuning remains limited by scarce data and compute. We propose a hierarchical control-and-learning framework in which a compact model is first distilled to learn the required output schema, then su
The proliferation of agentic systems built on Large Language Models necessitates methods for efficient operation under real-world constraints like memory, latency, and cost, pushing research towards optimized control and learning frameworks.
This research addresses fundamental resource limitations in deploying autonomous AI agents, enabling wider adoption and more sophisticated applications across industries by making powerful models more practical and cost-effective.
Current reliance on prompt extension is yielding to more sophisticated hierarchical control-and-learning architectures, allowing compact models to maintain performance and adapt in dynamic, constrained environments.
- · AI Agent Developers
- · Cloud Providers (for efficiency gains)
- · Industries deploying AI agents
- · Developers of foundational compact LLMs
- · Companies reliant solely on prompt extension
- · Less efficient large language models
More resource-efficient and reliable deployment of agentic AI systems becomes feasible.
Increased adoption of AI agents across various enterprise functions, leading to workflow automation.
Competitive advantage for organizations that successfully integrate and scale these optimized agentic AI architectures.
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Read at arXiv cs.AI