Long-Context Large Language Models for Enterprise Document Intelligence and Cross-Document Reasoning
Keywords:
large language models, long-context processing, enterprise document intelligence, cross-document reasoning, retrieval-augmented generation, infrastructure, governance, fairnessAbstract
The rapid evolution of large language models with extended context windows has opened transformative possibilities for enterprise document intelligence and cross-document reasoning. This paper provides a comprehensive systems-level examination of the architectural, infrastructural, and governance challenges that arise when deploying long-context models in organizational settings. We begin by contextualizing the progression from fixed-length transformer models to architectures capable of processing tens of thousands of tokens, highlighting the trade-offs between memory overhead, computational cost, and reasoning fidelity. Building upon this foundation, we analyze how cross-document reasoning tasks—such as multi-document summarization, contractual consistency checking, and regulatory compliance auditing--benefit from extended context windows, yet also introduce new failure modes related to recency bias, positional encoding decay, and information retrieval within unbounded corpora. The discussion turns to enterprise-level deployment considerations, including retrieval-augmented generation pipelines, distributed inference systems, and data governance frameworks necessary to manage the lifecycle of sensitive documents. Sustainability and fairness are examined through the lens of energy consumption, access equity, and algorithmic bias amplification when models are exposed to heterogeneous document collections. Finally, we explore policy implications, including auditability, transparency requirements, and liability frameworks for automated document analysis. The paper concludes with a forward-looking perspective that advocates for hybrid cognitive architectures combining long-context language models with structured knowledge bases and human oversight to achieve robust, trustworthy enterprise intelligence.
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