Poster: Leveraging Geo-Spatiality in Geo-Distributed Vector Databases
Bibliographic record
Abstract
Multimodal Retrieval-Augmented Generation (MRAG) systems depend on geo-distributed vector database management systems (GVDBMS) to deliver low-latency multimedia retrieval. Multimedia content that encodes spatial information (e.g., images from Toronto or audio from the Amazon) is produced worldwide. Consequently, GVDBMS face a fundamental trade-off when serving approximate nearest neighbor (ANN) queries: full data replication or broadcast querying. We hypothesize that embeddings produced by pre-trained models exhibit strong geographic locality. By exploiting this non-IID characteristic, query routing can be optimized to reduce cross-region traffic for these datasets and avoid the costs of full replication or query broadcasting. Our evaluation on real-world datasets shows that 42–93% of queries are satisfied by a single geographic partition, and 65–85% require at most half of the partitions to retrieve the top-10 results. Across five datasets (including street-view images and geo-tagged sounds) and multiple embedding models, our approach yields a 2–4× overall system throughput improvement while maintaining 90% accuracy over broadcast querying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".