Law And Order: Tax Law Autoformalization
Legal systems are increasingly implemented through software, yet scalable methods for translating legal texts into accurate symbolic representations remain underdeveloped. We study this problem through tax law, where forms and filing instructions define large computational structures involving arithmetic, branching, recursion, and tabular reasoning. We propose Law&Order, a neuro-symbolic framework for automatically formalizing tax forms and instructions into executable symbolic programs. Our approach establishes two forms of correspondence between law and logic: structural correspondence, which aligns legal and symbolic components such as cells and schedules, and denotational corresponden...
| Authors | Sophia Simeng Han, Yoshiki Takashima, Anjiang Wei et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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