Self-Calibrating AI Framework for Edge Resource Allocation
A new agentic AI framework designed for autonomous edge resource allocation uses a self-calibration mechanism with an ARIMA forecaster to mitigate operational drift and approximate ground truth without continuous human oversight. The framework is applied to profiling resource usage of zero-knowledge workloads in edge computing, addressing the challenge of reliable ground truth in open-ended environments for LLM-driven systems.
Key facts
- arXiv:2607.22400v1
- Announce Type: cross
- Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents
- Framework designed to enforce autonomous integrity within LLM-driven systems
- Self-calibration mechanism mitigates drift and dynamically approximates ground truth
- Incorporates an ARIMA forecaster
- Does not require continuous human oversight
- Applied to profiling resource usage of zero-knowledge workloads in edge computing
Entities
—