[2509.13471] An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software

Computer Science > Software Engineering arXiv:2509.13471 (cs) [Submitted on 16 Sep 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software Authors:Sina Gogani-Khiabani (University of Illinois Chicago), Ashutosh Trivedi (University of Colorado Boulder), Diptikalyan Saha (IBM Research), Saeid Tizpaz-Niari (University of Illinois Chicago) View a PDF of the paper titled An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software, by Sina Gogani-Khiabani (University of Illinois Chicago) and 3 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) show promise for translating natural-language statutes into executable logic, but reliability in legally critical settings remains challenging due to ambiguity and hallucinations. We present an agentic approach for developing legal-critical software, using U.S. federal tax preparation as a case study. The key challenge is test-case generation under the oracle problem, where correct outputs require interpreting law. Building on metamorphic testing, we introduce higher-order metamorphic relations that compare system outputs across structured shifts among similar individuals. Because authoring such relations is tedious and error-prone, we use an LLM-driven, role-based framework to automate test generation and code synthesis. We implement a multi-agent system that translates tax code into execut...