arXiv AI
Jul 1
Are Video Reasoning Models Ready to Go Outside?
arXiv:2603. 10652v3 Announce Type: replace-cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion.
Human-Level Accuracy, Non-Human Strategies: Revealing Model-Human Divergence in Video Physical Reasoning
Video foundation models now match human accuracy on physical‑reasoning benchmarks, but a new distributional evaluation framework shows that their predictions diverge markedly from human judgments. On the Physion benchmark, ViT‑L models such as V‑JEPA2, VideoMAE‑v2, and DINOv2 achieve near‑human accuracy yet exhibit a 26.4% model‑human disagreement, far above the 4.8% human‑human disagreement, and lower agreement (kappa ~0.48 vs. 0.91). The divergence varies by task: models excel at geometric reasoning but lag on gravitational dynamics and causal chains, indicating they rely on statistical regularities rather than explicit forward simulation.
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arXiv Computer Vision
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SYNCR: A Cross-Video Reasoning Benchmark with Synthetic Grounding
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arXiv AI
Aug 3
Step-Level Visual Grounding Faithfulness Predicts Out-of-Distribution Generalization in Long-Horizon Vision-Language Models
arXiv:2603. 06828v2 Announce Type: replace-cross Abstract: We uncover a behavioral law of long-horizon vision-language models: models that maintain temporally grounded beliefs generalize better.
arXiv Computation and Language
Sep 1
World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning
The paper introduces a framework that combines world models, which generate concrete visual rollouts of possible futures, with multimodal large language models (MLLMs) that perform abstract reasoning. It proposes a controlled concrete reasoning approach and a new training method called Privileged‑Future On‑Policy Self‑Distillation (PF‑OPSD), which uses ground‑truth future videos as privileged teacher context during training while the student model never sees true futures at test time. Experiments on two human‑verified benchmarks, VRQABench and OpenWorldQA, show that PF‑OPSD improves performance by about 10–11% over baselines and enhances robustness to noisy or conflicting rollouts.
arXiv AI
Jul 16
Accuracy Without Grounding: Diagnosing Visual Dependency Dissociation in Video LLM Benchmarks
arXiv:2607. 13305v1 Announce Type: cross Abstract: Benchmark accuracy in video large language models (LLMs) is often treated as evidence of visual understanding.
arXiv AI
Aug 18
CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?
arXiv:2608. 16829v1 Announce Type: cross Abstract: Video world models approximate the stochastic distribution of physical outcomes through generative sampling, but existing benchmarks score individual generations or compare distributions coarsely over a whole dataset, leaving the fine-grained aleatoric uncertainty of specific phenomena untested.
Hugging Face Trending Papers
Jun 2
World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning
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arXiv Machine Learning
Jun 16
How Should World Models Be Evaluated? A Decision-Making-Centric Position
arXiv:2606. 15032v1 Announce Type: new Abstract: World models have rapidly become one of the central abstractions in modern AI.
arXiv AI
Sep 25
Training Object Permanence in World Models
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arXiv AI
Jun 26
Confidence-Aware Tool Orchestration for Robust Video Understanding
arXiv:2606. 26904v1 Announce Type: cross Abstract: Video reasoning language models implicitly assume that every input frame is equally reliable.
arXiv Machine Learning
Jun 2
Perception First: A Frontier Native-Video Model with Self-Consistency for Implicit Video Question Answering
arXiv:2606. 01485v1 Announce Type: cross Abstract: We describe our submission to the VRR Challenge @ CVPR 2026, built on the \emph{ImplicitQA} / \emph{VRR-QA} benchmark~\cite{implicitqa}: multiple-choice video question answering in which answers are deliberately \emph{not} observable in any single frame and must be inferred from spatial layout, motion, depth, viewpoint, causality, and social context across discontinuous frames of creative video.
arXiv AI
Jun 4
Can VLMs Predict Future States? Bootstrapping World Models from Inverse Dynamics
arXiv:2506. 06006v3 Announce Type: replace-cross Abstract: Can unified vision-language models (VLMs) perform forward dynamics prediction (FDP), i.