[2603.20578] Context Cartography: Toward Structured Governance of Contextual Space in Large Language Model Systems

Computer Science > Artificial Intelligence arXiv:2603.20578 (cs) [Submitted on 21 Mar 2026] Title:Context Cartography: Toward Structured Governance of Contextual Space in Large Language Model Systems Authors:Zihua Wu, Georg Gartner View a PDF of the paper titled Context Cartography: Toward Structured Governance of Contextual Space in Large Language Model Systems, by Zihua Wu and 1 other authors View PDF HTML (experimental) Abstract:The prevailing approach to improving large language model (LLM) reasoning has centered on expanding context windows, implicitly assuming that more tokens yield better performance. However, empirical evidence - including the "lost in the middle" effect and long-distance relational degradation - demonstrates that contextual space exhibits structural gradients, salience asymmetries, and entropy accumulation under transformer architectures. We introduce Context Cartography, a formal framework for the deliberate governance of contextual space. We define a tripartite zonal model partitioning the informational universe into black fog (unobserved), gray fog (stored memory), and the visible field (active reasoning surface), and formalize seven cartographic operators - reconnaissance, selection, simplification, aggregation, projection, displacement, and layering - as transformations governing information transitions between and within zones. The operators are derived from a systematic coverage analysis of all non-trivial zone transformations and are organ...