Meta Context Engineering via Agentic Skill Evolution
Haoran Ye, Xuning He, Vincent Arak, Haonan Dong, Guojie Song

TL;DR
Meta Context Engineering (MCE) introduces a bi-level, adaptive framework that co-evolves CE skills and context artifacts, significantly improving large language model performance over traditional static methods.
Contribution
MCE presents a novel bi-level, agentic framework that dynamically co-evolves CE skills and context artifacts, surpassing static heuristics in efficiency and adaptability.
Findings
Achieves 5.6--53.8% performance improvement over state-of-the-art methods.
Demonstrates superior context adaptability and transferability.
Maintains efficiency in context usage and training.
Abstract
The operational efficacy of large language models relies heavily on their inference-time context. This has established Context Engineering (CE) as a formal discipline for optimizing these inputs. Current CE methods rely on manually crafted harnesses, such as rigid generation-reflection workflows and predefined context schemas. They impose structural biases and restrict context optimization to a narrow, intuition-bound design space. To address this, we introduce Meta Context Engineering (MCE), a bi-level framework that supersedes static CE heuristics by co-evolving CE skills and context artifacts. In MCE iterations, a meta-level agent refines engineering skills via agentic crossover, a deliberative search over the history of skills, their executions, and evaluations. A base-level agent executes these skills, learns from training rollouts, and optimizes context as flexible files and code.…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Machine Learning and Data Classification · Spreadsheets and End-User Computing
