# Can LLMs Identify Tax Abuse?

arXiv: https://arxiv.org/abs/2508.20097 · HTML: https://mlsanity.com/paper/2508.20097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas](https://mlsanity.com/paper/2506.20803/) (2025)
- [Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark](https://mlsanity.com/paper/2110.00806/) (2021)
- [Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence](https://mlsanity.com/paper/2306.07075/) (2023)
- [Catala: A Programming Language for the Law](https://mlsanity.com/paper/2103.03198/) (2021)
- [A Survey on Large Language Models for Automated Planning](https://mlsanity.com/paper/2502.12435/) (2025)
- [J&H: Evaluating the Robustness of Large Language Models Under Knowledge-Injection Attacks in Legal Domain](https://mlsanity.com/paper/2503.18360/) (2025)
- [Using LLMs to Discover Legal Factors](https://mlsanity.com/paper/2410.07504/) (2024)
- [Black-Box Optimization with Implicit Constraints for Public Policy](https://mlsanity.com/paper/2310.18449/) (2023)
- [Can AI expose tax loopholes? Towards a new generation of legal policy assistants](https://mlsanity.com/paper/2503.17339/) (2025)

## Built on by (most cited first, 2 of 2)

- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [Language Models and Logic Programs for Trustworthy Tax Reasoning](https://mlsanity.com/paper/2508.21051/) (2025)

## Abstract (arXiv)

We investigate whether large language models can discover and analyze U.S. tax-minimization strategies. This real-world domain challenges even seasoned human experts, and progress can reduce tax revenue lost from well-advised, wealthy taxpayers. We evaluate the most advanced LLMs on their ability to (1) interpret and verify tax strategies, (2) fill in gaps in partially specified strategies, and (3) generate complete, end-to-end strategies from scratch. This domain should be of particular interest to the LLM reasoning community: unlike synthetic challenge problems or scientific reasoning tasks, U.S. tax law involves navigating hundreds of thousands of pages of statutes, case law, and administrative guidance, all updated regularly. Notably, LLM-based reasoning identified an entirely novel tax strategy, highlighting these models' potential to revolutionize tax agencies' fight against tax abuse.