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New TreasuryQA Bench tests AI agents on scanned financial records

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the TreasuryQA Bench, a new benchmark designed to evaluate AI agents' ability to process scanned financial documents. Unlike previous benchmarks, TreasuryQA requires agents to both search through scanned PDFs via a Model Context Protocol server and extract specific figures using OCR, simulating real-world archival work. Initial tests show significant performance variation among agents, with Claude Opus 5.5 achieving the highest accuracy at 64.8%, while GPT-5.6 Terra scored 16.7%. The benchmark also highlights the impact of scan quality on agent performance, as all tested agents struggled more with blurrier scans. AI

IMPACT This benchmark could drive improvements in AI agents' ability to process and extract information from unstructured, scanned documents, crucial for many enterprise applications.

RANK_REASON The cluster describes a new benchmark and research paper evaluating AI agent capabilities on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TreasuryQA Bench tests AI agents on scanned financial records

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The cluster describes a new benchmark and research paper evaluating AI agent capabilities on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ritiz Tambi, Sahar Shayegan, Joshua Meyer, Sun Kim, Ali Khan, Victor Shih, Mehdi Jamei, Andi Partovi ·

    TreasuryQA Bench: Can AI Agents Work an Archive of Scanned Financial Records?

    arXiv:2610.38924v1 Announce Type: new Abstract: Organisations keep decades of financial records as scanned PDFs on shared drives, and AI agents can now reach those drives through Model Context Protocol (MCP) servers. Existing benchmarks do not mirror this production setting becau…

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