When Global Gating Is Enough: Admission-Time Hubness Control in Anisotropic Vector Retrieval Systems

Vector hubness, where a few points become nearest neighbors of many queries, creates a poisoning risk in retrieval-augmented generation (RAG): one injected document can influence unrelated requests. Existing defenses use periodic reverse-kNN scans, leaving an exposure window and repeated corpus-wide work. We study admission-time control, scoring each candidate against sentinel queries and quarantining hub-like documents before insertion. Across two 100,000-document corpora, five encoders, and disjoint attacker and defender query sets, a global gate achieves recall 1.0 at the decisive embedding-space point (>=0.92 across the effective range) and 0.91 +/- 0.07 on HotFlip attacks, with 1% false positives on general documents. A per-topic gate provides no reliable benefit, consistent with anisotropy coupling local and global visibility. Thresholds are maintained incrementally, with corpus-size-independent insertion cost and amortized deletion cost. On HNSW, admission adds about 3.1% to ingestion latency, scoring remains flat to 10^6 vectors, and 1.2% of decisions flip under approximate indexing, none involving attacks. Provenance complements the gate for natural or tight-domain hubs.

Pre-Execution Query Slot-Time Prediction in Cloud Data Warehouses: A Feature-Scoped Machine Learning Approach

Cloud data warehouses bill compute based on slot-time consumed. In shared multi-tenant environments, query cost is highly variable and hard to estimate before execution, causing budget overruns and degraded scheduling. Static query-planner heuristics fail to capture complex SQL structure, data skew, and workload contention. We present a feature-scoped machine learning approach that predicts BigQuery slot-time before execution using only pre-execution observable signals: a structured query complexity score derived from SQL operator costs, data volume features from planner estimates and workload metadata, and textual features from query text. We deliberately exclude runtime factors (slot-pool utilization, cache state, realized skew) unknowable at submission. The model uses a HistGradientBoostingRegressor trained on log-transformed slot-time, with a TF-IDF + TruncatedSVD-512 text pipeline fused with numeric and categorical features. Trained on 749 queries across seven deployment environments and evaluated out-of-distribution on 746 queries from two held-out environments, the model achieves MAE 1.17 slot-minutes, RMSE 4.71, and 74% explained variance on the full workload. On cost-significant queries (slot-time >= 0.01 min, N=282) the model achieves MAE 3.10 versus 4.95 for a predict-mean baseline and 4.54 for predict-median, a 30-37% reduction. On long-tail queries (>= 20 min, N=22) the model does not outperform trivial baselines, consistent with the hypothesis that long-tail queries are dominated by unobserved runtime factors outside the current feature scope. A complexity-routed dual-model architecture is described as a practical refinement, and directions for closing the long-tail gap are identified as future work.