AI RESEARCH
Context Cartography: Toward Structured Governance of Contextual Space in Large Language Model Systems
arXiv CS.AI
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ArXi:2603.20578v1 Announce Type: new The prevailing approach to improving large language model (LLM) reasoning has centered on expanding context windows, implicitly assuming that tokens yield better performance. However, empirical evidence - including the "lost in the middle" effect and long-distance relational degradation - nstrates that contextual space exhibits structural gradients, salience asymmetries, and entropy accumulation under transformer architectures.