paper
Toward a Theory of Context Compilation for Human-AI Systems
This paper proposes that context in AI systems should not merely be retrieved or remembered — it should be compiled. Context Compilation Theory introduces a formal framework where context is selected, transformed, governed, optimized, and lowered into executable context packs for downstream models, agents, and interfaces. The paper defines Context IR as a portable intermediate representation, analogous to compiler IRs in traditional software, and presents CompileBench as a benchmark specification for measuring compilation quality rather than recall alone.
Abstract
Large language model systems increasingly depend on external context, yet current approaches remain fragmented across retrieval-augmented generation, memory architectures, long-context prompting, and interface-specific prompt assembly. We propose Context Compilation Theory as a unifying framework and introduce Context Intermediate Representation (Context IR) as the portable internal object that mediates between raw context sources and runtime-specific execution.
Why it matters
Every enterprise building with LLMs faces the same fragmentation problem: RAG pipelines, memory systems, prompt templates, and agent context are all solving pieces of the same puzzle without a unifying theory. Context Compilation Theory provides that missing layer — a principled way to think about how context flows from sources through transformation to execution. For enterprise AI leaders, this reframes context management from an engineering detail into an architectural concern that determines system quality, governance, and portability.