Context Engineering 2.0: The Context of Context Engineering (arxiviq.substack.com)

A new paper, "Context Engineering 2.0," reframes the recent trend around prompts and RAG as a long‑evolving discipline and offers a formal, practice‑oriented foundation. The authors define context engineering as entropy reduction—transforming the high‑entropy of human intentions into low‑entropy, machine‑processable representations—and introduce a four‑stage evolutionary model (from primitive computation through agent‑centric LLMs to speculative human‑level and superhuman eras). By historicizing the field and unifying disparate tactics under a common vocabulary, the work shifts the focus from ad hoc prompt tweaks to strategic design of cognitive architectures, making it a practical roadmap for scalable, long‑horizon AI systems. Technically, the paper formalizes context as the union of characterization information and defines f_context to optimize task T, along with a layered memory architecture: short‑term (M_s), long‑term (M_l), and memory transfer functions. Era‑2 design patterns are organized into three pillars—collection (minimal sufficiency, semantic continuity), management (context abstraction, multimodal fusion, context isolation), and usage (context selection, sharing, proactivity)—and illustrated with real systems like Google’s Gemini CLI and Tongyi DeepResearch. It flags core challenges—Transformer O(n^2) costs, context pollution, lifelong evaluation—and proposes a “Semantic Operating System for Context”: a dynamic, self‑managing memory/knowledge system as the research agenda for persistent, collaborative AI.