Streaming Knowledge Compilation: Proactive Materiality-Scored Pinning for Time-Evolving LLM Wikis
arXiv:2606.09877v1 Announce Type: cross Abstract: LLM wiki systems compile knowledge into pre-filled KV caches for efficient inference, but assume a static corpus -- an assumption that fails whenever the underlying information landscape evolves. We formalize Streaming Knowledge Compilation: given a document stream, a fixed token budget, and future queries unknown at ingestion time, maintain a compiled wiki that minimizes cumulative regret against an offline oracle with perfect foresight. The enabling insight is a materiality signal $\phi_t(k,n)\in[0,1]$ that scores document importance for enti
The proliferation of LLM systems has highlighted the inefficiency of updating knowledge bases, making dynamic compilation a critical area of research as information landscapes evolve rapidly.
This research addresses a core limitation of current LLM systems, promising significantly more efficient and accurate inference for applications requiring real-time knowledge integration.
LLM-powered knowledge systems will become more adaptable and less reliant on static datasets, enabling more dynamic and up-to-date applications without the need for frequent, costly re-training.
- · LLM developers
- · AI-powered information services
- · Enterprises using LLMs for real-time data analysis
- · Knowledge management platforms
- · Companies with static, outdated LLM deployments
- · Manual data compilation services
- · AI systems reliant on batch updates
More accurate and current information retrieval from LLM applications.
Accelerated development of AI agents capable of continuous learning and adaptation.
Enhanced operational intelligence and decision-making capabilities across various industries due to real-time AI insights.
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Read at arXiv cs.CL