A Unified Framework for Cross-Domain Computational Reasoning Evaluation in Large Language Models: Beyond Benchmarks

The rapid proliferation of large language models (LLMs) capable of mathematical, logical, algorithmic, and statistical reasoning has exposed a critical gap in evaluation methodologies: existing benchmarks are siloed by domain, use incompatible scoring rubrics, and fail to capture the quality of reasoning processes. We argue that the pursuit of a single “best” model is undermined by the lack of a shared conceptual space for comparing reasoning across heterogeneous tasks. In this paper, we propose the Unified Framework for Cross-Domain Reasoning (UFR-CR), a theory-driven, benchmark-agnostic evaluation architecture that transcends traditional category boundaries. Instead of introducing a new dataset, UFR-CR introduces four universal reasoning dimensions — Chain Depth, Knowledge Type, Creativity Demand, and Computational Load—that enable any existing task to be mapped into a common feature space. Coupled with a three-tier elicitation protocol (standardised prompting, controlled trace extraction, and human-in-the-loop validation) and a robust statistical resampling procedure, the framework provides a fair, reproducible, and interpretable mechanism for comparing LLMs across distinct reasoning domains. We outline a concrete instantiation of the framework using four established benchmarks (MATH, LogiQA, HumanEval, and DROP) and specify an experimental protocol, together with a set of pre-registered, falsifiable hypotheses about expected outcomes and their sensitivity to methodological choices. No experiments have yet been run under this protocol; the hypotheses are offered as a concrete target for empirical validation rather than as findings. We discuss the implications for model development, the limitations of current trace evaluation, and a roadmap for community adoption. This work shifts the focus from creating yet another leaderboard to establishing a principled, unified language for reasoning assessment.