Zero-Copy Semantic Contagion: An In-Memory Streaming Architecture for Evolving Attention Graphs

• URL: http://arxiv.org/abs/2606.05733v1
• Date: Thu, 04 Jun 2026 05:48:56 GMT
• Title: Zero-Copy Semantic Contagion: An In-Memory Streaming Architecture for Evolving Attention Graphs
• Abstract summary: We introduce a Rust-Python streaming architecture that maps cross-company attention as a continuous-time graph driven directly from text.<n>We show that on the ingestion side, a zero-copy Rust edge parses news records in $sim$100 ns and scans the target equity universe in $sim$1.2 $$s.<n>We demonstrate an end-to-end processing latency of $sim$13 ms per incoming news record on a single commodity CPU.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Per-ticker forecasting models dominate financial time-series work yet remain blind to cross-company propagation: a foundry disruption in Taiwan does not register in a single-asset model until Apple's own price has already moved. To address this limitation, we introduce a heterogeneous Rust-Python streaming architecture that maps cross-company attention as a continuous-time graph driven directly from text. We show that on the ingestion side, a zero-copy Rust edge parses news records in $\sim$100 ns and scans the target equity universe in $\sim$1.2 $μ$s. On the inference end, a multivariate Neural Hawkes Process featuring per-node continuous-time LSTM states and a bilinear latent projection propagates directed excitation, while an adaptive pruning rule bounds the computational cost of dynamic neighborhood updates. Combining these stages, we demonstrate an end-to-end processing latency of $\sim$13 ms per incoming news record on a single commodity CPU. Evaluated on a one-month temporal holdout of the FNSPID corpus (638 articles across 47 tickers), the system delivers a $1.70\times$ precision lift over random at the 90th-percentile next-day return threshold, and $3.36\times$ over a same-sector baseline. Crucially, removing the graph topology collapses precision to zero, confirming that the dynamic attention network is the sole driver of cross-company signal in this architecture.

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