# PreSIST: Vision-Language-Informed Object Persistence Prediction in Open-World Scenes

arXiv: https://arxiv.org/abs/2607.04057 · HTML: https://mlsanity.com/paper/2607.04057/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 4 Jul 2026.

## Builds on (most cited first, 25 of 25)

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- [HD-EPIC: A Highly-Detailed Egocentric Video Dataset](https://mlsanity.com/paper/2502.04144/) (2025)
- [Precise Detection in Densely Packed Scenes](https://mlsanity.com/paper/1904.00853/) (2019)
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- [Khronos: A Unified Approach for Spatio-Temporal Metric-Semantic SLAM in Dynamic Environments](https://mlsanity.com/paper/2402.13817/) (2024)
- [Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset](https://mlsanity.com/paper/2309.13549/) (2023)
- [EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?](https://mlsanity.com/paper/2506.05287/) (2025)
- [Predicting Routine Object Usage for Proactive Robot Assistance](https://mlsanity.com/paper/2309.06252/) (2023)
- [REN: Fast and Efficient Region Encodings from Patch-Based Image Encoders](https://mlsanity.com/paper/2505.18153/) (2025)
- [Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes](https://mlsanity.com/paper/2605.00121/) (2026)
- [RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Features for Indoor Localization](https://mlsanity.com/paper/2012.00234/) (2020)
- [Where Did I Leave My Glasses? Open-Vocabulary Semantic Exploration in Real-World Semi-Static Environments](https://mlsanity.com/paper/2509.19851/) (2025)
- [Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments](https://mlsanity.com/paper/2507.18808/) (2025)
- [CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning](https://mlsanity.com/paper/2407.09718/) (2024)

## Abstract (arXiv)

Robots deployed over long periods must reason about environments that change over time. Existing long-term perception systems often address object change reactively, updating their maps only after revisiting a scene and observing that an object has moved. Instead, robots should reason proactively about how long objects are likely to persist using the context in which they appear. For example, a car at a traffic light and a car in a parking spot share the same semantic class, but their contexts imply different persistence durations. We propose PreSIST (Predictive Scene-conditioned Instance Survival over Time), a method for predicting whether an observed object will remain in its last seen pose at arbitrary future times. PreSIST estimates instance-level persistence priors from object properties and scene context, then integrates these priors with a probabilistic persistence filter as observations become available. Its key insight is that the reasoning capabilities of vision-language models (VLMs) can relate scene context to likely object use and human activity, enabling persistence prediction before long-term observations are available. We develop two interchangeable variants: PreSIST-Lang, which estimates persistence priors using a VLM, and PreSIST-Vis, a novel vision-only model trained using PreSIST-Lang pseudo-labels for efficient deployment. Experiments on a new dataset of in-the-wild object persistence annotations show that PreSIST-Lang and PreSIST-Vis outperform baselines on open-world persistence prediction.