← 研踪 PaperTrailarXiv:2610.37349 · v1
TimeLineXR: An Interactive Exploration of Time in Vision-Language Models
首次提交 2026-10-27 22:50
版本更新 2026-10-27 22:50
雷达观测 2026-10-29 00:34
摘要
Recently, it has been shown that vision-language models (VLMs) have a certain intrinsic understanding of time. They can estimate the temporal origin of objects and encode time along a low-dimensional, non-linear manifold inside their embedding space~\cite{tekaya2025matter}. That result is quantitative and reproducible, but the structure itself stays out of reach for end users: they cannot see it, query it with their own images, or inspect where a query lands relative to other years on the time axis. We present TimeLineXR, an interactive demonstration that turns this time axis into an explorable 3D scene. A web demo lets users upload or select images and see each placed on a color-coded 3D time axis; a Three.js WebXR scene renders the same time axis as a room-scale representation that can be grabbed, scaled, inspected, and queried by throwing image cards into it. Both clients run the same prediction pipeline against the same cached temporal index: encode the query with CLIP, project it through cosine-kernel KPCA, and snap it to the Bézier time axis. The system turns each image into a point on the time axis, so users can compare where different objects land and read off their estimated first year of appearance.Code source and reproduction steps can be found in: https://github.com/sterngefeuert/timeline-xr.
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v1 · 2026-10-27 22:50 · 最新观测版本
TimeLineXR: An Interactive Exploration of Time in Vision-Language Models
Recently, it has been shown that vision-language models (VLMs) have a certain intrinsic understanding of time. They can estimate the temporal origin of objects and encode time along a low-dimensional, non-linear manifold inside their embedding space~\cite{tekaya2025matter}. That result is quantitative and reproducible, but the structure itself stays out of reach for end users: they cannot see it, query it with their own images, or inspect where a query lands relative to other years on the time axis. We present TimeLineXR, an interactive demonstration that turns this time axis into an explorable 3D scene. A web demo lets users upload or select images and see each placed on a color-coded 3D time axis; a Three.js WebXR scene renders the same time axis as a room-scale representation that can be grabbed, scaled, inspected, and queried by throwing image cards into it. Both clients run the same prediction pipeline against the same cached temporal index: encode the query with CLIP, project it through cosine-kernel KPCA, and snap it to the Bézier time axis. The system turns each image into a point on the time axis, so users can compare where different objects land and read off their estimated first year of appearance.Code source and reproduction steps can be found in: https://github.com/sterngefeuert/timeline-xr.
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