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This paper formalizes Streaming Knowledge Compilation for LLM wikis, introducing a materiality signal to proactively pin important documents from a streaming corpus under a token budget. It proves an O(√(T log K)) regret bound and validates the approach in finance and Wikipedia domains, showing that regret analysis is a reliable evaluation metric.