Advancing Context Engineering in Large Language Models: Mechanisms, Benchmarks, and Future Challenges

A comprehensive review titled "Advancing Context Engineering in Large Language Models: Mechanisms, Benchmarks, and Future Challenges" introduces Context Engineering as a distinct scientific discipline that extends beyond traditional prompt engineering. This field offers a systematic framework for designing, refining, and controlling the informational inputs that influence Large Language Models (LLMs). The overview highlights its core components, including the categorization of context sources such as retrieval and generation methods, processing techniques like long-sequence handling and multimodal integration, and management strategies involving memory architectures. The paper also discusses system implementations, including retrieval-augmented generation, memory systems, tool integration, and multi-agent collaborations, emphasizing the importance of modularity and dynamic knowledge integration. Key insights reveal ongoing challenges, such as the asymmetry between comprehension and generation capabilities, evaluation limitations, and open research questions in theory, scalability, cross-modal integration, and ethical deployment. The article underscores the transformative potential ...