Materials synthesis procedures are predominantly documented as narrative text in protocols and lab notebooks, rendering them inaccessible to conventional structured data optimization. This language-native nature poses a critical challenge for complex, multistage processes--such as the preparation of boron nitride nanosheet (BNNS)--where outcomes depend on path-dependent choices in exfoliation and functionalization. Here, we recast synthesis planning as a text reasoning task enabled by a lightly structured text database, which preserves the conditional logic and causal contexts essential for expert-like decision-making. Building on a heterogeneous schema that indexes both narrative excerpts and computable entities (e.g., reaction conditions), our system implements a hybrid retrieval engine to combine semantic context with precise parameter filtering. On top of this, the framework operates in two modes, i.e. retrieval-augmented generation (RAG), which grounds recommendations in retrieved evidence modules, and experience-augmented reasoning (EAR), which uses iteratively refined text guides distilled from multi-source narrative data. Instead of suggesting single "optimal" settings, the system produces interpretable guidance aligned with expert reasoning patterns--hypotheses, parameter ranges, and citation-backed standard operating procedures--that support iterative planning and failure diagnosis. We validated this framework on the targeted exfoliation of BNNS, a process highly sensitive to multivariate constraints. The system successfully identified optimal combinations of grinding aids, milling configurations, and separation strategies from a wide range of literature-reported methods, which were experimentally verified to yield high-quality nanosheets, illustrating the potential of language-native reasoning to streamline critical operations in materials processing.

翻译：材料合成流程主要记载于实验方案和实验室记录本中的叙述性文本，这使其难以通过传统结构化数据方法进行优化。这种语言原生特性对复杂多阶段工艺（如氮化硼纳米片的制备）构成了关键挑战，其最终结果依赖于剥离与功能化过程中路径依赖的选择。本研究将合成规划重构为一项文本推理任务，通过轻结构化文本数据库实现，该数据库保留了专家决策所必需的条件逻辑与因果语境。基于一种索引叙述性片段与可计算实体（如反应条件）的异构架构，本系统实现了混合检索引擎，将语义语境与精确参数过滤相结合。在此基础上，该框架以两种模式运行：检索增强生成模式通过检索证据模块为建议提供依据；经验增强推理模式则利用从多源叙述数据中提炼的迭代优化文本指南。系统不提供单一“最优”设定，而是生成符合专家推理模式的可解释指导——包括假设、参数范围及引用支持的标准操作规程——以支持迭代规划与故障诊断。我们在对多变量约束高度敏感的氮化硼纳米片定向剥离工艺中验证了该框架。系统成功从大量文献报道方法中识别出研磨助剂、研磨配置与分离策略的最佳组合，实验验证表明其可制备高质量纳米片，这彰显了语言原生推理在优化材料加工关键工序方面的潜力。