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Point Resolution and Path Composability in Local-Update Systems: Structural, Schedule-Respecting, Executable, and Continuation-Preserving Arrival
One-line summary
An AI research paper on Point Resolution and Path Composability in Local-Update Systems: Structural, Schedule-Respecting, Executable, and Continuation-Preserving Arrival.
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Chinese explanation / 中文解读
中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。
Original abstract
This preprint studies how point resolution affects path composability, reachability, and arrival structure in local-update systems. A point-resolution map from resolved update states to coarse points is used to distinguish coarse adjacency from compatible resolved path composition. The paper introduces an internal transit depth for coarse intermediate points, compares coarse shortest-path distance with minimum resolved fiber arrival, and analyzes nested structural, schedule-respecting, executable, and continuation-preserving transition layers. It also distinguishes generic graph-path length from stage-indexed Propagation Bound arrival and separates point identification from directional state continuation. The contribution is organizational rather than a proposal of a new metric or quotient theory. AI collaboration: Developed with OpenAI’s ChatGPT (GPT-5.5 Thinking and GPT-5.6 Sol) for formulation, drafting, and consistency review.
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