Streaming Knowledge Compilation: Proactive Materiality-Scored Pinning for Time-Evolving LLM Wikis

Juan M. Huerta

paper

Streaming Knowledge Compilation: Proactive Materiality-Scored Pinning for Time-Evolving LLM Wikis

arXiv:2606.09877

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 $I ¨ _{t} (k, n) \in [0, 1]$ that scores document importance for entity $k$ at time $t$ , acting as a query-relevance surrogate for proactive pinning before queries arrive; we prove an $O (T lo g K)$ regret bound where $ε = E [∣ I ¨ _{t} - I ¨^_{t} ∣]$ is the only domain-specific quantity. We instantiate in two domains: finance, where $I ¨ _{t}$ is abnormal stock volatility predicted by frozen Llama 3.1 8B classification head (AUROC = 0.728 on 76K articles, strict temporal split; $1.49 \times$ higher realized forward volatility for predicted-material articles); and Wikipedia, where $I ¨ _{t}$ is the Abnormal Edit Ratio (AER), a cross-sectionally normalized edit velocity -- showing the same algorithm generalizes beyond the finance domain. End-to-end QA evaluation on 173 matched pairs (finance) and 119 (Wikipedia) reveals a pervasive LLM-as-judge confound on post-training knowledge, establishing that regret analysis -- not absolute QA scores -- is the reliable evaluation metric for compiled knowledge systems. Finance cumulative regret converges to -20.0 (-0.12/step); Wikipedia to +16.0 (+0.13/step), with the positive sign confirming that Wikipedia edit content is genuinely post-training -- richer context consistently improves scores (No Wiki 3.80 vs. Oracle 4.74) -- and eliminates this confound. The $O (T lo g K)$ guarantee applies to any domain where knowledge gaps can be predicted from streaming signals.