元算法司法推理引擎
我们正在尝试一种自动裁决的架构，该架构不依赖于规则库或统计预测。与其将法律编码为“如果-那么”规则或基于过去案例训练模型，不如将抽象法律推理建模为一种元算法：一个控制层，协调多个异构组件——硬编码逻辑、数值建模以及由大型语言模型（LLM）执行的结构化自然语言程序。
核心思想是，法律推理的结构（运行哪些阶段、如何选择和解释规范、如何平衡竞争利益、何时修正早期结论）用一种强类型的伪代码/元语言表达。这个元算法的某些部分直接用代码实现（程序检查、基本资格、图形更新），某些部分是数学的（效用、均衡、模糊不确定性），还有一些则以自然语言的高级指令形式编写，由LLM在严格约束下进行解释。在这种设置中，LLM并不是结果的预测者，而是给定程序脚本的解释者。
该系统不基于案例法进行训练，也不试图“预测”法院的裁决。它重建推理流程：从提取当事方的事实叙述和证据结构，到规范选择和加权，再到生成可以在内部操作图中逐步追溯的决策。相同的元算法可以通过更换规范包在不同的司法管辖区中工作；到目前为止，我们已在一系列国际和国内争议中进行了测试。
这里有一个早期的公开演示：
https://portal.judgeai.space/
如果您上传一份小型的索赔声明和回应，系统将运行完整的流程并输出一份结构化的决策文件。
我们非常感谢从事混合符号/语义系统、“LLM作为解释者”架构或复杂决策的形式模型的人员提供反馈。对我们来说，显而易见的开放性问题包括：如何最好地测试这种元控制的失败模式、使用什么形式工具检查推理图的一致性，以及在达到严格理论限制之前，这种方法可以推进多远。
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We’re experimenting with an architecture for automated adjudication that doesn’t rely on rule bases or statistical prediction. Instead of encoding law as “if–else” rules or training a model on past cases, we model abstract legal reasoning as a meta-algorithm: a control layer that orchestrates several heterogeneous components — hard-coded logic, numerical modeling, and structured natural-language procedures executed by an LLM.<p>The core idea is that the structure of legal reasoning (which stages to run, how to select and interpret norms, how to balance competing interests, when to revise earlier conclusions) is expressed in a strongly typed pseudocode / meta-language. Some parts of this meta-algorithm are implemented directly in code (procedural checks, basic qualification, graph updates), some are mathematical (utilities, equilibria, fuzzy uncertainty), and some are written as high-level instructions in natural language, which the LLM interprets under tight constraints. In that setting, the LLM is not a predictor of outcomes but an interpreter of a given procedural script.<p>The system doesn’t train on case law and doesn’t try to “predict” courts. It reconstructs the reasoning pipeline itself: from extracting the parties’ factual narratives and evidence structure, through norm selection and weighting, up to generating a decision that can be traced back step-by-step in the internal graph of operations. The same meta-algorithm can work with different jurisdictions by swapping norm packages; we’ve tested it so far on a set of international and domestic disputes.<p>There is an early public demo here:
https://portal.judgeai.space/<p>If you upload a small statement of claim and a response, the engine runs the full pipeline and outputs a structured decision document.<p>We’d be grateful for feedback from people working on hybrid symbolic/semantic systems, “LLM as interpreter” architectures, or formal models of complex decision-making. Obvious open questions for us are: how best to test failure modes of this kind of meta-control, what formal tools to use for checking consistency of the reasoning graph, and how far one can push this approach before hitting hard theoretical limits.