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Add authors to dataset card

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  **Developed and released by [Kay.ai](https://kay.ai).**
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  LongListBench measures **long-list structured extraction** from insurance and commercial trucking PDFs: return every target record without omissions, merged rows, invented extras, or lost fields under complex layout, OCR, and long-range evidence. It contains 32 synthetic PDFs and 29,599 target records, with no real customer PII.
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  The task is per-document extraction: give a system one PDF or OCR transcript plus the target contract, then score the complete list. `core_operations` emphasizes scale and output completeness; `claim_multihop` and `policy_packets` require inherited context, heterogeneous schemas, distant supporting sections, and distractors.
 
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  **Developed and released by [Kay.ai](https://kay.ai).**
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+ **Authors:** [Anton Fedoruk](https://orcid.org/0009-0004-0260-1704), [Serhii Shchoholiev](https://orcid.org/0009-0007-2014-4828), and [Akhil Mehta](https://orcid.org/0009-0001-0134-2905)
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  LongListBench measures **long-list structured extraction** from insurance and commercial trucking PDFs: return every target record without omissions, merged rows, invented extras, or lost fields under complex layout, OCR, and long-range evidence. It contains 32 synthetic PDFs and 29,599 target records, with no real customer PII.
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  The task is per-document extraction: give a system one PDF or OCR transcript plus the target contract, then score the complete list. `core_operations` emphasizes scale and output completeness; `claim_multihop` and `policy_packets` require inherited context, heterogeneous schemas, distant supporting sections, and distractors.