research
Neural Paging System Reduces LLM Context Management Complexity from O(N²) to O(N·K²)
A new research paper introduces Neural Paging, a hierarchical architecture that optimizes how LLMs manage their limited context windows by learning semantic caching policies. The approach reduces asymptotic complexity for long-horizon reasoning from O(N²) to O(N·K²) under bounded context window size K, addressing a fundamental bottleneck in deploying universal agents with external memory.