Tree-of-Thought-Augmented LLMs for Automated Document Review Across Industrial Domains

Zehui Zhu, Mohamad Iliya Haziq Mohamad Lokman, Meenakshi Mishra, Peng Xu, Jennifer Shin, Hua Qian, Zhifu Shu

Keywords: Large Language Model, Tree-of-Thought, Decision-Making, Document Intelligence

Abstract: We present AI Screener, an end-to-end automated document review system that integrates a 12-billion-parameter pretrained large language model with a Tree-of-Thought reasoning framework to emulate and scale expert-level decision-making. Designed for high-stakes, domain-specific analysis, AI Screener empowers subject matter experts to encode their domain knowledge and reasoning processes in a no-code, efficient manner—enabling rapid customization without technical barriers. The system has been deployed across three different and unrelated mission-critical business functions: (1) accelerating scientific literature reviews to support the development of occupational exposure limits for worker health protection, (2) streamlining patent screening to optimize intellectual property portfolio management, and (3) automating procurement contract analysis to identify value leakage and drive better commercial terms. Across these diverse deployments, subject matter experts encoded their knowledge with AI Screener to transform traditional workflows—significantly reducing manual review time while maintaining expert-grade accuracy and consistency. This work highlights how Tree-of-Thought-augmented LLMs can be pragmatically applied to reshape enterprise document intelligence at scale.

Submission Number: 8