Neural Paging: Learning Context Management Policies for Turing-Complete Agents

Researchers introduce Neural Paging, a new architecture that addresses the computational bottleneck of finite context windows in Large Language Models by implementing a hierarchical system that decouples reasoning from memory management. The approach reduces computational complexity from O(N²) to O(N·K²) for long-horizon reasoning tasks, potentially enabling more efficient AI agents.

• →Neural Paging architecture separates symbolic reasoning from information resource management in LLMs.
• →The system addresses the Context Window bottleneck that limits current AI agent implementations.
• →Computational complexity is reduced from quadratic O(N²) to O(N·K²) for long-horizon reasoning.
• →A lightweight Page Controller approximates 'Semantic Belady's Optimality' to retain high-utility tokens.
• →Theoretical guarantees are validated on synthetic paging traces with identified optimization opportunities.