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

Researchers developed a multi-agent LLM system that translates legal statutes into executable software, using U.S. tax preparation as a test case. The system achieved a 45% success rate using GPT-4o-mini, significantly outperforming larger frontier models like GPT-4o and Claude 3.5 which only achieved 9-15% success rates on complex tax code tasks.