Residual Frontier Refinement: Exact Shortest-Path Propagation with Adaptive Partial Ordering

Residual Frontier Refinement (RFR) is an exact shortest-path propagation method that replaces unconditional global priority-queue ordering with adaptive partial ordering over residual distance bands. The method processes frontier bands in batches only when conservative safety checks prove that doing so preserves Dijkstra-equivalent shortest-path distances; ambiguous bands are split or resolved with local exact fallback. This release contains the RFR whitepaper, Python reference implementation, tests, benchmark artefacts, and topographic routing simulator. The work validates exactness against Dijkstra across tested graph families and demonstrates how structured frontiers can reduce global ordering work while exposing useful diagnostics such as safe batches, split pressure, local fallback, and residual frontier ambiguity. The current implementation is a research prototype. It demonstrates the algorithmic work model and operational implications for structured maps, grids, rasters, terrain routing, and future dynamic repathing systems, but does not claim universal wall-clock superiority over Dijkstra in Python.