AI 中文总结
KnowPlan将学位路径规划拆分为提取与优化阶段，CatalogBrowse负责课程爬取解析，DegreeMap基于超图优化，在多赛道实验中实现了高召回率、可行性与效用提升。
AI 中文摘要
从官方大学资源规划学位需依次解决两个问题：首先需从目录、院系页面、JSON端点及无统一模式的PDF中重构院校课程，之后才能在先修逻辑与重叠要求约束下优化学生专属路径。将两者耦合会使各失败模式相互掩盖，因为自主爬取的规划器永远不会学习当前计划不需要的事实。本文提出KnowPlan，它强制实施提取优先边界并测量阶段间接口，而非假设接口存在。CatalogBrowse在无任何用户配置文件访问权限的情况下进行探索，它按单位源访问量对有限原子目录义务集的低置信度预期边际增益评分，通过平台适配器结合跨度约束的子句到AST模型回退进行确定性解析，并在索引、模式、来源及参考完整性的闭合证书上终止，而非奖励阈值。其输出契约为三个来源关联的JSON文档。DegreeMap仅使用这些文档，将它们编译为类型化需求超图并通过CP-SAT按字典顺序优化硬可行性、完成期限、负荷与风险、个性化效用及选项价值，使每个阶段在前一阶段的已优化范围内优化并在求解器预算内保持可验证性。在100所大学的广泛赛道与6所学校的密集赛道中，CatalogBrowse的库存召回率达96.2%，掩码源恢复率达88.7%，源访问量比穷尽爬取器少47%；DegreeMap保持100.0%的硬可行性，同时比最强基线提升0.066的个性化效用；完整流水线可验证99.5%的请求，与特权黄金图的效用差距为0.015。
英文摘要
Planning a degree from official university sources requires solving two problems in order. The institution's curriculum must first be reconstructed from catalogs, departmental pages, JSON endpoints, and PDFs that share no schema, and only then can a student-specific path be optimized under prerequisite logic and overlapping requirement constraints. Coupling the two lets each failure mode hide the other, because a planner that drives its own crawling never learns facts its current plan does not need. We present KnowPlan, which enforces an extraction-first boundary and measures the interface between the stages rather than assuming it. CatalogBrowse explores with no access to any user profile. It scores legal actions by lower-confidence expected marginal gain over a finite set of atomic catalog obligations per unit of source access, parses deterministically through platform adapters with a span-constrained clause-to-AST model fallback, and terminates on a closure certificate over index, schema, provenance, and reference completeness instead of a reward threshold. Its output contract is three provenance-linked JSON documents. DegreeMap consumes only those documents. It compiles them into a typed requirement hypergraph and optimizes lexicographically with CP-SAT over hard feasibility, completion horizon, load and risk, personalized utility, and option value, so that each stage optimizes inside the previous stage's proven optimum and stays certifiable within the solver budget. Across a 100-university broad track and a six-school dense track, CatalogBrowse reaches 96.2% inventory recall and 88.7% masked-source recovery at 47% less source access than an exhaustive crawler, DegreeMap holds 100.0% hard feasibility while improving personalized utility by +0.066 over the strongest baseline, and the full pipeline certifies 99.5% of requests with a utility gap to the privileged gold graph of 0.015.
