mlsanityData to 14 Oct 2026

Context Engineering 2.0: The Context of Context Engineering

arXiv 2510.26493 ↗First posted 30 Oct 2025cs.AI cs.CLcited by 18

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Abstract (arXiv)

Karl Marx once wrote that ``the human essence is the ensemble of social relations'', suggesting that individuals are not isolated entities but are fundamentally shaped by their interactions with other entities, within which contexts play a constitutive and essential role. With the advent of computers and artificial intelligence, these contexts are no longer limited to purely human--human interactions: human--machine interactions are included as well. Then a central question emerges: How can machines better understand our situations and purposes? To address this challenge, researchers have recently introduced the concept of context engineering. Although it is often regarded as a recent innovation of the agent era, we argue that related practices can be traced back more than twenty years. Since the early 1990s, the field has evolved through distinct historical phases, each shaped by the intelligence level of machines: from early human--computer interaction frameworks built around primitive computers, to today's human--agent interaction paradigms driven by intelligent agents, and potentially to human--level or superhuman intelligence in the future. In this paper, we situate context engineering, provide a systematic definition, outline its historical and conceptual landscape, and examine key design considerations for practice. By addressing these questions, we aim to offer a conceptual foundation for context engineering and sketch its promising future. This paper is a stepping stone for a broader community effort toward systematic context engineering in AI systems.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 41 of its 116 references that are in the map. First posted on arXiv 30 Oct 2025.

Builds on

41 of 116 references.

References · 41

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 14.

18 papers.

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  1. 2025SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarioscited by 59
  2. 2026Meta Context Engineering via Agentic Skill Evolutioncited by 49
  3. 2025CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolutioncited by 34
  4. 2026Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agentscited by 19
  5. 2026From Context to Skills: Can Language Models Learn from Context Skillfully?cited by 19
  6. 2026Building Effective AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learnedcited by 18
  7. 2026Autonomous Research Agents: A Survey of AI Scientists and the Verification Gapcited by 9
  8. 2025ContextPilot: Fast Long-Context Inference via Context Reusecited by 8
  9. 2025Everything is Context: Agentic File System Abstraction for Context Engineeringcited by 6
  10. 2026Gated Differentiable Working Memory for Long-Context Language Modelingcited by 6
  11. 2026Large Language Model Agents Are Not Always Faithful Self-Evolverscited by 5
  12. 2026Experience-Driven Multi-Agent Systems Are Training-free Context-aware Earth Observerscited by 4
6 more
  1. 2026Decocted Experience Improves Test-Time Inference in LLM Agentscited by 2
  2. 2026A Training-Free Guess What Vision Language Model from Snippets to Open-Vocabulary Object Detectioncited by 1
  3. 2026Context Engineering: A Practitioner Methodology for Structured Human-AI Collaborationcited by 1
  4. 2026daVinci-LLM:Towards the Science of Pretrainingcited by 0
  5. 2026PromptMN: Pseudo Prompting Languagecited by 0
  6. 2026DTOC: Dynamic Tool Output Compression for Adaptive Context Management in AI Agentscited by 0