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arXiv:2608.06530v1 Announce Type: new Abstract: Planning a degree from official university sources requires solving two problems in order. The institution's curriculum must first be reconstructed from catalogs, departme
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传统学位规划要求学生从分散的课程目录、院系规定和先修条件中手动梳理出一条符合毕业要求的路径，过程繁琐且容易出错。KNOWPLAN 的核心思路是将这一过程分解为两个有序步骤：首先，AI 智能体需要从非结构化的官方文档中重建出结构化的课程知识图谱，这包括课程间的先修关系、学期开设情况以及学位要求的逻辑规则；其次，基于重建的知识库，智能体再为特定学生生成满足所有约束的可行修读计划。这种“先理解、再规划”的两阶段设计，使得系统能够处理现实世界中复杂且动态变化的学术规则。
从技术实现角度看，该框架利用了大型语言模型的语义理解能力来解析目录文本，并结合符号化的规划算法来确保路径的严谨性。这与同期 arXiv 上关于结构化流程和可解释性的研究趋势相呼应，例如有工作探索用 LLM 将社区发现过程符号化以提升可解释性，或通过对比贡献度来忠实解释混合专家模型的预测机制。KNOWPLAN 将这种知识驱动的方法应用于教育领域，其生成的规划不仅是一份课表，更附带了对每一步决策的溯源解释，例如指出某门课为何是必修、其先修要求如何被满足，这增强了系统输出在真实教育场景下的可信度。
该研究的潜在影响在于，它展示了 AI 智能体在高度结构化、规则密集的垂直领域（如教育行政管理）中的应用潜力。通过将官方知识源作为 ground truth，系统能够提供比通用对话式 AI 更可靠、更符合制度规范的个性化建议。当然，原文摘要未提供具体的实验评估数据，其在不同大学课程体系下的泛化能力、以及处理课程变更等动态信息的鲁棒性，仍有待后续研究验证。
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arXiv:2608.06530v1 Announce Type: new Abstract: 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