Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models
Quick Take
The proposed hierarchical control-and-learning framework enhances compact language models by enabling effective schema learning and semantic adaptation under resource constraints, improving reliability and cost-efficiency in agentic systems. This approach outperforms non-hierarchical and distillation-only methods, addressing deployment failure modes through Multi-Fidelity Bayesian Optimization.
Key Points
- Introduces a hierarchical framework for resource-constrained language models.
- Utilizes an oracle-controller loop for online supervision and protocol monitoring.
- Improves reliability and cost-efficiency over traditional methods.
- Characterizes deployment failure modes using Multi-Fidelity Bayesian Optimization.
- Separates schema learning from semantic adaptation for better performance.
Article Content
From source RSS / original summaryarXiv: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 supervised online by an oracle-controller loop. The controller monitors protocol validity and semantic performance, projects accumulated histories into a feasible prompt domain, and triggers lightweight oracle-supervised fine-tuning under drift. This separates schema learning for communication compatibility from semantic adaptation for task-level correction.
We formalize prompt-domain feasibility and attention-induced saturation, motivating control of the effective prompt state rather than reliance on nominal context length. Using Multi-Fidelity Bayesian Optimization as a controlled sequential testbed, we characterize a core deployment failure mode and show improved reliability and cost-efficiency over non-hierarchical, distillation-only, and non-distilled baselines.
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