Universal Reasoning Protocol (URP): A Deterministic Chain for Auditable AI-Assisted Decision Making

Abstract

This paper introduces the Universal Reasoning Protocol (URP), a deterministic and auditable reasoning framework designed for human–AI collaborative decision making. The protocol enforces a structured six-step chain: Anchor, Model, Simulate, Falsify, Trace, and Verify. Unlike conventional prompting approaches that rely on implicit reasoning, URP externalizes inference into explicit procedural stages that ensure traceability, falsifiability, and verification prior to conclusion generation.

Experimental comparisons across multiple task domains show that URP improves output consistency, reduces unsupported claims, and increases reproducibility relative to baseline prompting. The framework functions as a protocol-level reasoning layer rather than a prompt pattern, enabling integration into decision systems, agent pipelines, and analytical workflows.

URP demonstrates that structured reasoning enforcement can transform generative AI usage from probabilistic interaction into auditable computation, establishing a generalizable methodology for reliable human–AI cognition. 

Keywords: Artificial Intelligence, Human-AI Collaboration, Reasoning Protocol, Decision Framework, Structured Reasoning, AI Reliability, Auditable AI, Cognitive Systems, Prompt Engineering, Computational Reasoning