Hugging Face Blog
TimeScope: How Long Can Your Video Large Multimodal Model Go?
Read the original on Hugging Face Blog →The Flow has not summarised this story yet — read it at Hugging Face Blog.
Related stories
Hugging Face Trending Papers
3d ago
WorldProbe: When Video World Models Fall Apart under Long-Horizon Spatial Exploration
WorldProbe is a new diagnostic benchmark that tests video world models by letting a goal‑conditioned multimodal agent adapt camera actions online during generation, creating 164 test cases across four probing families. The benchmark evaluates 10 state‑of‑the‑art models over more than 1,600 rollouts, totaling roughly 800 minutes and horizons up to 180 seconds, revealing that while local visual and content quality can be maintained for longer, long‑range memory and evolution remain weak. The study highlights a gap between locally stable generation and persistent world consistency that existing short‑interaction benchmarks miss.
arXiv Computer Vision
4d ago
TimeLineXR: An Interactive Exploration of Time in Vision-Language Models
arXiv:2610. 37349v1 Announce Type: new Abstract: Recently, it has been shown that vision-language models (VLMs) have a certain intrinsic understanding of time.
arXiv AI
Jul 1
Temporal Preservation over Processing: Diagnosing and Designing Spatiotemporal Single-Stage Video Detectors
arXiv:2606. 31421v1 Announce Type: cross Abstract: Single-stage video object detectors are increasingly deployed in time-critical applications, yet it remains unclear whether these models genuinely reason over temporal context or merely exploit a single informative frame-a gap hidden by standard metrics, which reward correct predictions regardless of how they are reached.
arXiv AI
Sep 10
TimeBlind: A Spatio-Temporal Compositionality Benchmark for Video LLMs
TimeBlind is a diagnostic benchmark designed to evaluate fine‑grained spatio‑temporal compositionality in video large language models (LLMs). It categorizes temporal understanding into three levels—atomic event recognition, event property characterization, and reasoning about event interdependencies—and uses a minimal‑pairs paradigm where video pairs share identical static content but differ only in temporal structure. Across 20 state‑of‑the‑art MLLMs tested on 600 curated instances, the best model achieved only 48.2% instance accuracy, far below human performance of 98.2%, highlighting a reliance on static visual shortcuts rather than true temporal reasoning.
arXiv Computer Vision
Sep 15
DiVA: Enabling Interactive Digital Life Simulation via Video Models
arXiv:2609.13830v1 Announce Type: new Abstract: We present DiVA, a deeply interactive digital life simulator pioneering a new paradigm for long-term, open-ended interactive experiences within digital...
arXiv Computer Vision
Sep 1
Dancing Stick Figures: An Introductory Dataset for Training Video Generation Models
arXiv:2608.29123v1 Announce Type: new Abstract: Training a video-generation model from scratch is hard for reasons that precede model design. The feedback loop is long: a failure that appears only af...