HomeResearch LibraryLearning when to attend: Conditional memory access for …
Chapter 4 · 2026

Learning when to attend: Conditional memory access for long-context LLMs

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Abstract

This paper explores conditional memory access to extend LLM context length while managing KV footprint. It evaluates long-context performance by increasing the Rotary Position Embedding base.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper contributes useful building blocks for Zone III architecture through its work on conditional memory access, long-context LLMs, KV footprint. While not exclusively focused on enterprise deployment, the insights translate directly to the challenges of long-horizon agentic workflows. The key lesson for Zone III practitioners: the problems identified here do not disappear at scale — they compound. Understanding them at the research level is prerequisite to solving them in production.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

conditional memory accesslong-context LLMsKV footprint