# A Functional Pilot for Certified Freshness-Aware Semantic--Spatial Range Retrieval

arXiv: https://arxiv.org/abs/2609.19855 · HTML: https://mlsanity.com/paper/2609.19855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [SPFresh: Incremental In-Place Update for Billion-Scale Vector Search](https://mlsanity.com/paper/2410.14452/) (2024)
- [Approximate Nearest Neighbor Search with Window Filters](https://mlsanity.com/paper/2402.00943/) (2024)
- [UNIFY: Unified Index for Range Filtered Approximate Nearest Neighbors Search](https://mlsanity.com/paper/2412.02448/) (2024)
- [Quake: Adaptive Indexing for Vector Search](https://mlsanity.com/paper/2506.03437/) (2025)
- [WISK: A Workload-aware Learned Index for Spatial Keyword Queries](https://mlsanity.com/paper/2302.14287/) (2023)
- [iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search](https://mlsanity.com/paper/2409.02571/) (2024)
- [Range Retrieval with Graph-Based Indices](https://mlsanity.com/paper/2502.13245/) (2025)
- [WoW: A Window-to-Window Incremental Index for Range-Filtering Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2508.18617/) (2025)
- [FAST: Frequency-Aware Spatio-Textual Indexing for In-Memory Continuous Filter Query Processing](https://mlsanity.com/paper/1709.02529/) (2017)
- [LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries](https://mlsanity.com/paper/2403.07331/) (2024)

## Abstract (arXiv)

Geographic applications need every object inside a radius that satisfies a semantic threshold, yet embedding indexes return approximate top-ranked lists and may omit qualifying records silently. We present FRESH-GEORANGE, a semantic- spatial range design that separates source-watermark freshness from optional record age. Geographic cells and semantic mi- croblocks provide admissible pruning bounds; a graph proposes verification order but supplies no correctness evidence. Exact mode scans every nonprunable block and the delta overlay. Certified mode may stop early and reports a deterministic query- specific recall lower bound from verified answers and unresolved records. A reproducible CPU pilot uses 2,500 real OpenFlights airport records, a 2,000-record base, and 740 simulated insert, delete, and text-revision events; it evaluates 180 unique queries over five seeds. Exact mode achieved 100.00% set recall on every query. The 95-percent mode achieved 99.91% empirical mean recall with a 99.41% reported mean certificate and no observed bound violation. However, its 7.24 ms median latency was 5.85 times the 1.24 ms spatial-first exact baseline, and full-history delta replay became slower than rebuilding at larger batches. The prototype therefore validates the completeness mechanism, not performance superiority or production freshness. Submission- scale evaluation requires real map diffs, official recent baselines, and truly incremental versioned maintenance.