The paper introduces COL‑Tree, a compact landmark‑based tree structure designed to accelerate hierarchical object search in spatial networks. It supports diverse query types—such as aggregate k‑nearest neighbor (AkNN), k‑farthest neighbor (kFN), and others—by enabling efficient graph traversal with a more accurate heuristic than Euclidean‑based methods. Experiments on real‑world and synthetic road, terrain, and physics mesh networks show that COL‑Tree outperforms existing approaches by up to three orders of magnitude, with only modest preprocessing overhead.
arXiv:2602.04068v2 Announce Type: replace Abstract: The calculation of shortest-path distances in road networks is a core operation in navigation systems, location-based services, and spatial analyti...
arXiv:2607. 01283v1 Announce Type: cross Abstract: Grid-based approaches to approximate nearest neighbor (ANN) search have been absent from modern scaling analyses.
arXiv:2611.05697v1 Announce Type: new Abstract: Finding specific objects, structures, and spatial patterns supports critical work in agriculture, infrastructure planning, environmental monitoring, an...
The paper evaluates seven graph database engines, including Corvic AI, on a synthetic biomedical property graph with 1.02 million nodes and 5.34 million rows. It benchmarks query latency, bulk‑ingest throughput, point‑update latency, and correctness across a twenty‑query workload that covers neighborhood lookups, bounded paths, set intersections, anti‑joins, aggregation, ranking, temporal filters, full scans, and relational joins. The study finds that no single engine is universally fastest; performance depends on query shape, and the largest cost difference arises from bulk‑ingest throughput, which varies by three orders of magnitude and dominates total cost for workloads with fewer than about 10⁵ queries per data refresh.
arXiv:2601. 16509v2 Announce Type: replace-cross Abstract: The $k$-nearest neighbors ($k$NN) algorithm is a cornerstone of non-parametric classification in artificial intelligence, yet its deployment in large-scale applications is persistently constrained by the computational trade-off between inference speed and accuracy.
arXiv:2606. 18267v1 Announce Type: cross Abstract: Benchmarking shortest-path algorithms is commonly based on aggregate performance over heterogeneous graph sets, which limits insight into how different search paradigms react to instance structure.
arXiv:2606. 26465v1 Announce Type: cross Abstract: Spatial pattern matching is the process of matching query entities and constraints with database entities and relations.
arXiv:2608. 08876v1 Announce Type: cross Abstract: A graph layout is normally a table of $N$ free coordinates.
arXiv:2603. 06660v2 Announce Type: replace-cross Abstract: Approximate Nearest Neighbor Search (ANNS) is fundamental to modern AI applications.
Vision-language models (VLMs) achieve strong semantic understanding but remain unreliable in metric spatial reasoning, particularly when queries require comparing multiple instances of the same object category. We study this problem through the Closest-Instance Distance Query (CIDQ), where a model must identify the nearest visible candidate to a unique reference object and estimate their gravity-aligned floor-plane distance.
arXiv:2601. 06135v3 Announce Type: replace Abstract: Spatial computation in geographic systems increasingly requires query-conditioned, local, interpretable aggregation under metric constraints.
arXiv:2608. 00270v2 Announce Type: replace Abstract: Neural Combinatorial Optimization (NCO) techniques have emerged as a highly efficient alternative to traditional exact algorithms for solving routing problems such as the Traveling Salesman Problem (TSP).