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Research questionHow can LLM agents answer recurring questions over unstructured documents without repeatedly rereading them?Agents may need to reopen large reports, filings, contracts, web pages, earnings calls, or PDFs to recover evidence that related questions have already uncovered. Repeatedly processing that evidence makes broad enterprise knowledge tasks expensive even when the underlying information is reused.
AI Agents
Inference Optimization
Information Retrieval
Reasoning
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.Agentic Context Cracking: Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured DataThe source examines LLM agents reasoning over unstructured enterprise data and adaptive, speculative structuring performed during that reasoning. On the FanOutQA benchmark with one related question per test question, the reported approach reduced cost by 53% while preserving accuracy; the evidence does not establish performance beyond that setting.research paper · Sep 4, 2026
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