{
  "format": "ssr-development-record-v1",
  "date": "2026-10-11",
  "title": "Upgrade Lab: exact-cost search and adversarial evaluation",
  "author": "Jonathan Simone with Codex AI assistance",
  "aiContribution": "Codex implemented Dijkstra and Manhattan-distance A* with tie breaking, visual search traces, seeded adversarial cases, a separate Bellman-Ford reference, tests and exports under Jonathan Simone's direction.",
  "model": "Codex; exact underlying model revision not recorded",
  "candidateOrigin": "Implementation of established algorithms during AI-assisted development, not a novel algorithm discovery or autonomous search.",
  "userContribution": "Directed the experience, quality requirements and lesson purpose. Visitors edit terrain, conditions and goal, then run the authored programs.",
  "originalRule": "Current opening baseline: Dijkstra with actual terrain cost. The older deliberately inaccurate estimates remain selectable.",
  "proposedRule": "A* with exact terrain cost plus Manhattan distance; ties favor lower remaining distance.",
  "evaluationScope": "13 visible development cases in the workshop; a separate seed-controlled panel generates 32 map entries across 4 conditions (128 related development cases, including repeated open-ground controls). Both planners are compared with Bellman-Ford. Unreachable maps must return no route. Search expansions are measured, not elapsed time.",
  "status": "Original interactive teaching example; no independent reproduction or visitor learning result claimed.",
  "source": "/next-lessons/self-improvement/routing-model.mjs",
  "sourceSha256": "f9ba7460673a6e801db0b750924dd4546f41a2c266d580dd58378f81eb2c285d",
  "evaluationSource": "/next-lessons/self-improvement/evaluation.mjs",
  "evaluationSourceSha256": "851eed768d96a5cc0567d3f5eb426dd9a1b84bd8c9c6287c29033cf5d60a809f",
  "taskInput": "Create an original playable policy workshop with editable terrain, comparable executable strategies, honest improvement checks, rollback and a usable offline artifact.",
  "humanEdits": "Jonathan supplied the lesson purpose, evidence contract and possibility-first quality requirements. No manual human code edits are recorded in this branch.",
  "rejectedAttempt": "Historical first version: the dry-ground shortcut improved the opening dry map against the fixed-five estimate, then regressed on wet cases. Against the current exact-cost Dijkstra baseline it has no dry energy advantage and still regresses in wet conditions.",
  "hashEncoding": "UTF-8 with CRLF normalized to LF and exactly one trailing newline",
  "stressSource": "/next-lessons/self-improvement/stress.mjs",
  "revisionNote": "The original fixed-estimate comparison was a weak baseline. This revision defaults to a correct planner and measures search effort at equal route cost.",
  "upstream": "Dijkstra, A*, Manhattan-distance heuristics and Bellman-Ford are established algorithms. Original teaching implementation; no third-party code copied.",
  "metric": "Route cost first, then expanded squares. No measured human comparison, wall-clock speedup, or independent reproduction claimed.",
  "stressSourceSha256": "df50883b69527d25157ec8e6e0983d61cf52bcfa9afc36a0b4d85e8ba1d34101",
  "learningPromise": "Compare an AI-assisted routing change against a correct baseline and a separate reference implementation on visible development and reproducible stress cases. Distinguish valid, non-regressing and improved under the stated rule.",
  "evaluationData": "No untouched evaluation set. Seeded stress cases are development evidence, including seeds used while building and testing this release.",
  "referenceIndependence": "Bellman-Ford is a separate implementation from Dijkstra/A*, written by the same authoring team. This does not establish independent evaluation data or external reproduction.",
  "remainingBriefDifference": "The original brief requested practice beside separate evaluation. This implementation narrows the promise to development-case comparison; separate untouched evaluation remains unimplemented."
}