自然语言输入、语义航迹表示与大语言模型推理：使海上信息交换模型可处理
Natural Language Input, Semantic Track Representation, and LLM Inference: Making the Maritime Information Exchange Model Tractable
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中文总结 AI 辅助
该研究提出基于LLM的架构，通过自然语言输入自动生成SARs，实现MIEM与富语义航迹模型的可处理与快速部署，适用于国防执法等场景。
中文摘要 AI 辅助
我们描述了一种实用架构，可利用当前大语言模型（LLM）技术使海上信息交换模型（Maritime Information Exchange Model, MIEM）及更广泛的富语义航迹模型变得可处理。在国防与执法领域，语义航迹模型的采用障碍在于操作人员需学习形式化本体语言，并将观测结果手动编码为类型化逻辑断言。我们提出彻底消除这一障碍：操作人员以自然语言提交观测结果；LLM将其转换为类型化语义断言记录（Semantic Assertion Records, SARs），这是一种命名的论元结构，能在单一紧凑结构中捕获n元关系；知识图谱累积SARs；第二轮LLM对图谱执行推理、异常检测与假设排序。我们通过两个详细示例（9·11事件前的攻击指标场景与海上货物检查场景）展示从自然语言输入到SAR表示再到推理输出的完整流程。我们认为该架构使航迹模型与MIEM可利用当前技术立即部署，确立了针对该方法专有封闭的现有技术，并将该方法建立在连接语义航迹表示与神经流形几何的理论框架基础上。
英文摘要
We describe a practical architecture for making the Maritime Information Exchange Model (MIEM) and the broader Rich Semantic Track model tractable using current large language model (LLM) technology. The barrier to adoption of semantic track models in defense and law enforcement has been the requirement that operators learn formal ontology languages and manually encode observations as typed logical assertions. We propose eliminating this barrier entirely: operators contribute observations in natural language; an LLM translates these into typed Semantic Assertion Records (SARs), which are named case frames that capture n-ary relations in a single compact structure; a knowledge graph accumulates the SARs; and a second LLM pass performs inference, anomaly detection, and hypothesis ranking over the graph. We work through two detailed examples (a 9/11-era pre-attack indicator scenario and a maritime cargo inspection scenario) showing the full pipeline from natural language input to SAR representation to inference output. We argue that this architecture makes the Track Model and MIEM immediately deployable with current technology, establishes prior art against proprietary enclosure of the approach, and grounds the method in a theoretical framework connecting semantic track representations to neural manifold geometry.
