SAGE is a governed multi‑stage LLM pipeline that transforms enterprise guideline documents—containing narrative text, tables, and images—into structured artifacts. It uses a shared versioned rule store, schema‑validated contracts, and provenance tracking to validate, score, and reconcile extracted rules, automatically approving high‑confidence outputs while flagging uncertain items for human review. In a test on 120 documents, SAGE reduced processing time from days to 20–100 minutes and achieved a 96% success rate with only 3.2% hallucination.
By Mohammadreza Sediqin, Shivali Dalmia, Sumukha Thoppanahalli, Srinivasa Karthikeya Reddy Kovvuri, Abhishek Mukherji
arXiv:2607. 19865v1 Announce Type: new Abstract: As autonomous agents rapidly evolve, their ability to reliably manipulate ubiquitous digital documents has become critical for enabling general-purpose AI assistants and automating complex workspace workflows.
By Jiazhen Jiang, Boxi Cao, Lingyong Yan, Yaojie Lu, Hongyu Lin, Shuaiqiang Wang, Dawei Yin, Xianpei Han, Le Sun
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
By Boyang Zhang, Adrian Lyjak, Eli Stewart, Zhaoqi Li, Simon Suo
arXiv:2607. 18816v1 Announce Type: cross Abstract: LLM-powered agents increasingly tackle complex tasks by invoking tools, querying databases, executing code, and manipulating intermediate artifacts.
By Eden Wu, Sonia Castelo, Yurong Liu, Cl\'audio T. Silva, Juliana Freire
arXiv:2606. 05415v1 Announce Type: cross Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics.
By Padmaja Jonnalagedda, Yuguang Yao, Xiang Gao, Hilaf Hasson, Kamalika Das
arXiv:2607. 26075v1 Announce Type: cross Abstract: We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines.
By David Kaleko, Sergey Ivanov, Md Mofijul Islam
arXiv:2609.22719v1 Announce Type: cross
Abstract: Business requirements for enterprise software systems are rarely captured in structured form; the logic resides instead in source code, configuration...
By Garima Agrawal, Prasun Das, Priyanka L, Minisha N, Akshay Sarvade, Hemath Manivanan, Sravani Joshna, Prasad Kalyansundaram
arXiv:2608. 16763v1 Announce Type: new Abstract: Financial document validation in production, such as payroll auditing, tax compliance, and loan underwriting, demands exceptional accuracy, consistency, and reproducibility under strict enterprise constraints.
By Ruoqi Shu, Xuhui Wang, Isaac Wang, Yanming Mai, Bo Wan
arXiv:2609.22209v1 Announce Type: cross
Abstract: Empirical legal research often relies on turning research questions into structured data extracted from large collections of rulings and judgments. D...
By Albert Sawczyn, Jakub Binkowski, Kamil Tagowski, {\L}ukasz Augustyniak, Berenika Kaczmarek-Templin, Tomasz Kajdanowicz
arXiv:2609.01575v1 Announce Type: new
Abstract: Extracting structured fields from hundreds of millions of documents annually remains costly in regulated industries: bespoke OCR cascades cover only a...
By Maksim Evdokimov, Matvey Ivanov, Dmitrii Tsiupin, Olga Tsymboi, Anatolii Potapov, Aleksandr Ivanov
arXiv:2606. 27386v1 Announce Type: cross Abstract: Scientific publication is still organized primarily around static manuscripts, even though much of scientific progress depends on tacit know-how: how to run code, reproduce figures, interpret edge cases, choose useful follow-up directions, and avoid failed paths.
By Sirui Lu, Xiao-Liang Qi
arXiv:2606. 21005v2 Announce Type: replace Abstract: Scientific discovery workflows often depend on structured curation from the literature.
By Sheng Zhang, Qin Liu, Renqian Luo, Shufang Xie, Reuben Tan, Sean Hayes, Gregory Bryman, Wendong Ge, Ruilian Zhang, Oluwaseun Egbelowo, Kelly Yee, Hoifung Poon