[2608.08075] Search over the Visual World: Persistent Visual Memory, Layered Indexes, and Source-Grounded Evidence

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Computer Science > Information Retrieval

arXiv:2608.08075 (cs)

[Submitted on 8 Aug 2026]

Title:Search over the Visual World: Persistent Visual Memory, Layered Indexes, and Source-Grounded Evidence

Authors:Sankalp Nagaonkar, Rohit Garg, Ankit Raj, Ashish Choithani, Ashutosh Trivedi<br>View a PDF of the paper titled Search over the Visual World: Persistent Visual Memory, Layered Indexes, and Source-Grounded Evidence, by Sankalp Nagaonkar and 4 other authors

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Abstract:Most video-retrieval systems assume a bounded corpus and return ranked files or timestamps. Agents operating over cameras, screens, streams, and archives face a different systems problem: observations arrive continuously; models interpret them at different temporal granularities; context must be selected without replaying the complete visual record; and results must stay connected to inspectable source evidence. We argue that search over such a corpus is an infrastructure problem that cannot be reduced to ranking video files. We develop a conceptual and formal model of search over the visual world built on analyzer-defined scenes, persistent understanding artifacts, visual memory as coexisting scene spaces over shared source time, and capability-declared indexes, distinguishing memory (everything retained), context (what is selected for a task), and evidence (the source intervals that ground it). The VideoDB data format (VDB) realizes this model in production, exposed through a typed search surface spanning planned retrieval, stateful investigation, direct access, and grounded synthesis. We contrast this model-agnostic infrastructure, where segmentation, sampling, model choice, embeddings, and ranking are system decisions and live streams are first-class sources, with video-native foundation models offered as fixed APIs. In a semantic-retrieval comparison against a commercial video-native engine spanning 9,800+ queries over four public datasets, a pipeline of general-purpose components achieves higher macro-averaged Recall@1/@3/@10 (73.09/83.39/91.20 versus 65.75/77.13/89.10), while the baseline is higher at Recall@50 (96.42 versus 96.07). Retrieval quality over the visual world is today governed more by system design than by video-specific pretraining, and visual-memory infrastructure can deliver it while keeping playable, source-grounded evidence first-class.

Comments:<br>33 pages, 5 figures, 17 tables. Technical report. Benchmark configurations and reproduction instructions: this https URL

Subjects:

Information Retrieval (cs.IR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)

Cite as:<br>arXiv:2608.08075 [cs.IR]

(or<br>arXiv:2608.08075v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2608.08075

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Ashish Choithani [view email]<br>[v1]<br>Sat, 8 Aug 2026 11:46:54 UTC (53 KB)

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View a PDF of the paper titled Search over the Visual World: Persistent Visual Memory, Layered Indexes, and Source-Grounded Evidence, by Sankalp Nagaonkar and 4 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

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