BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications

2026-04-19

AI Summary

Existing benchmarks struggle to comprehensively evaluate the reasoning capabilities of large language models (LLMs) in complex business scenarios and their underlying theoretical foundations. To address this gap, this work proposes BizCompass, a novel benchmark featuring an innovative “knowledge–application” dual-axis design that spans four core domains—finance, economics, statistics, and operations research—and incorporates role-specific tasks for analysts, traders, and consultants. BizCompass enables systematic evaluation of both open-source and commercial LLMs, not only diagnosing the root causes of performance disparities but also uncovering patterns in how theoretical knowledge translates into practical competence. The benchmark provides actionable insights for model selection and optimization in real-world business applications, and the associated dataset and code are publicly released to support further research.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large language models (LLMs) hold great promise for business applications, yet business analysis remains inherently complex, demanding rigorous reasoning and the integration of diverse knowledge sources. Existing benchmarks typically target narrow tasks and thus leave a fundamental question unanswered: how can LLMs be reliably applied in business, and how are these applications grounded in underlying theoretical capabilities? To address this gap, we introduce BizCompass, a benchmark explicitly designed to connect theoretical foundations with practical business knowledge and applications. At the knowledge level, BizCompass covers four core domains--finance, economics, statistics, and operations management. At the application level, it structures tasks around three representative roles: the analyst, the trader, and the consultant. This dual-axis design not only exposes performance differences across realistic scenarios but also diagnoses which foundational capabilities enable or constrain success. We systematically evaluate both open-source and commercial LLMs, revealing how theoretical knowledge translates into practical performance in business. The results provide actionable insights for model selection and training optimization in real-world business contexts. All datasets and evaluation code are publicly released to support reproducibility and future research: https://bizcompass.dev.ypemc.com.

Problem

• large language models
• business applications
• reasoning capabilities
• benchmarking
• theoretical foundations

Innovation

• business reasoning
• LLM benchmarking
• knowledge-application alignment
• dual-axis evaluation
• real-world business tasks