A Synthetic Framework for Chain-of-Thought Generation and AI-Based Validation of Multi-Step Legal Reasoning

Reliable evaluation of multi-step reasoning in Large Language Models (LLMs) remains challenging due to static benchmarks, limited control over reasoning complexity, and weak visibility into intermediate reasoning steps. This paper introduces TaxLaw-CoT, a fully implemented synthetic framework that generates legal-style reasoning cases with (i) structured Chain-of-Thought (CoT) reasoning chains, (ii) explicit ground-truth outputs, and (iii) automated AI-based quality evaluation including validation and complexity scores. Inspired by the Multistep Soft Reasoning (MuSR) paradigm, the framework constructs multi-entity scenarios with interacting tax events (e.g., credits, deductions, shared income) and produces standardized outputs comprising narrative context, stepwise reasoning, ground truth summaries, and diagnostic feedback on missing steps or potential rule application gaps. I present the end-to-end orchestration pipeline, the output schema that supports reproducible evaluation, and a documented example case illustrating how CoT, validation, and complexity assessment interact to create realistically complex reasoning instances. The framework is designed to be domain-extensible and model-agnostic, providing a practical infrastructure for stress-testing reasoning behavior and auditing reasoning completeness in LLM-based systems.