OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning

TL;DR

OfficeQA Pro is a comprehensive benchmark for evaluating AI agents on complex, multi-document reasoning tasks involving heterogeneous data, revealing current models' limitations and the potential of structured document representations.

Contribution

Introduces OfficeQA Pro, a challenging enterprise benchmark for grounded reasoning, and demonstrates the impact of structured document representations on AI performance.

Findings

Frontier LLMs achieve less than 5% accuracy relying on parametric knowledge.

Performance improves to 34.1% with document access, but remains limited.

Structured document representations significantly boost agent performance.

Abstract

We introduce OfficeQA Pro, a benchmark for evaluating AI agents on grounded, multi-document reasoning over a large and heterogeneous document corpus. The corpus consists of U.S. Treasury Bulletins spanning nearly 100 years, comprising 89,000 pages and over 26 million numerical values. OfficeQA Pro consists of 133 questions that require precise document parsing, retrieval, and analytical reasoning across both unstructured text and tabular data. Frontier LLMs including Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro Preview achieve less than 5% accuracy on OfficeQA Pro when relying on parametric knowledge, and less than 12% with additional access to the web. When provided directly with the document corpus, frontier agents still struggle on over half of questions, scoring 34.1% on average. We find that providing agents with a structured document representation produced by Databricks' ai_parse_document yields a 16.1% average relative performance gain across agents. We conduct additional ablations to study the effects of model selection, table representation, retrieval strategy, and test-time scaling on performance. Despite these improvements, significant headroom remains before agents can be considered reliable at enterprise-grade grounded reasoning.