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Law&Order framework achieves 100% accuracy in tax law autoformalization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a neuro-symbolic framework called Law&Order to automatically translate tax law into executable symbolic programs. This system combines large language models with cell-level verification and iterative error repair using human-written tax returns. The framework achieves 100% accuracy on held-out tax returns, significantly outperforming standalone LLMs which reached only 66% accuracy. AI

IMPACT Demonstrates a novel approach to formalizing complex legal texts, potentially accelerating the development of AI systems for legal and financial applications.

RANK_REASON Academic paper detailing a new method for AI-driven legal text formalization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Law&Order framework achieves 100% accuracy in tax law autoformalization

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Academic paper detailing a new method for AI-driven legal text formalization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sophia Simeng Han, Yoshiki Takashima, Anjiang Wei, Zhaoyu Li, Michael Genesereth ·

    Law And Order: Tax Law Autoformalization

    arXiv:2610.02792v1 Announce Type: new Abstract: 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 filin…

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