{"ID":3084869,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T05:32:54.120957816Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05733","arxiv_id":"2606.05733","title":"Zero-Copy Semantic Contagion: An In-Memory Streaming Architecture for Evolving Attention Graphs","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.05733","url_pdf":"https://arxiv.org/pdf/2606.05733v1","authors":"[\"Kabir Murjani\"]","published":"2026-06-04T05:48:56Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CE\",\"q-fin.CP\",\"stat.ML\"]","methods":"[]","has_code":false}