Temporal Reasoning in VLMs

VLM: Vision-Language Model

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42 papers13 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving
May 22, 2026Hao Vo, Khoa Vo, Phu Loc Nguyen +10VLM Evaluation Autonomous Driving Datasets
University of Arkansas, USA · Google Research, Google · University of Liverpool, UK +1
On the Cultural Anachronism and Temporal Reasoning in Vision Language Models
May 14, 2026Mukul Ranjan, Prince Jha, Khushboo Kumari +1VLM Evaluation VLM Reasoning
MBZUAI, UAE · Inception, UAE
Tracing the Arrow of Time: Diagnosing Temporal Information Flow in Video-LLMs
May 8, 2026Peitao Han, Fei Cheng, Lis K. Pereira +2Video-Language Models Temporal Reasoning in Language Models
The University of Osaka · Center for Information and Neural Networks · National Institute of Information and Communications Technology +2
PushupBench: Your VLM is not good at counting pushups
Apr 25, 2026Shengzhi Li, Jiarun Chen, Karun Sharma +2Temporal Video Understanding Video-Language Model Evaluation
How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study
Apr 8, 2026Roberto Brusnicki, Mattia Piccinini, Johannes BetzVisual Evidence Sensitivity in VLMs Temporal Reasoning in VLMs
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
Probing the Reliability of Driving VLMs: From Inconsistent Responses to Grounded Temporal Reasoning
Mar 10, 2026Chun-Peng Chang, Chen-Yu Wang, Holger Caesar +1PEFT of VLMs Temporal Reasoning in VLMs
DFKI Augmented Vision · TU Delft
ReMoT: Reinforcement Learning with Motion Contrast Triplets
Feb 28, 2026Cong Wan, Zeyu Guo, Jiangyang Li +5Temporal Reasoning in VLMs VLM Reasoning
Xi’an Jiaotong University · Shenzhen University of Advanced Technology · DAMO Academy, Alibaba Group
MATEO: A Multimodal Benchmark for Temporal Reasoning and Planning in LVLMs
Feb 16, 2026Gabriel Roccabruna, Olha Khomyn, Giuseppe RiccardiTemporal Reasoning in VLMs VLM Reasoning
Signals and Interactive Systems Lab, University of Trento, Italy
Temporal Grounding of Video Large Language Model via Holistic Event Perception and Matching
Feb 5, 2026Jiahao Nie, Wenbin An, Gongjie Zhang +4Temporal Video Grounding Video-Language Models
Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · Nanyang Technological University, Singapore · Xi’an Jiaotong University, China +3
RiskCueBench: Benchmarking Anticipatory Reasoning from Early Risk Cues in Video-Language Models
Jan 6, 2026Sha Luo, Yogesh Prabhu, Timothy Ossowski +2Temporal Video Understanding Temporal Reasoning in VLMs
University of Wisconsin–Madison, Madison, WI, USA · University of California San Diego, San Diego, CA, USA
StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos
Dec 1, 2025Daeun Lee, Subhojyoti Mukherjee, Branislav Kveton +6Proactive Video Understanding Streaming Video Understanding
University of North Carolina, Chapel Hill · Adobe Research
PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?
Sep 2, 2025Mennatullah SiamVision-Language Grounding Video-Language Model Evaluation

May 22, 2026J/K move · Enter open · S save

DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving

Hao Vo, Khoa Vo, Phu Loc Nguyen, Sieu Tran+9

University of Arkansas, USA · Google Research, Google · University of Liverpool, UK+1

VLM Evaluation Autonomous Driving Datasets Autonomous Driving Benchmarks Vision-Language Models VLMs for Autonomous Driving Autonomous Driving Perception+3

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Spatiotemporal intelligence in autonomous driving (AD) requires an agent to integrate multi-view observations into a coherent scene representation, maintain object continuity across viewpoints and time, and reason about spatial relations, interactions, and future dynamics. However, existing AD vision-language benchmarks largely focus on single-view, static, ego-centric, or single-source question answering, leaving it unclear whether current Vision-Language Models (VLMs) can truly construct and reason over dynamic driving scenes. We introduce DriveSpatial, a benchmark of 15.6K human-verified QA pairs across 20 tasks from five large-scale AD datasets. DriveSpatial evaluates four abilities: Cognitive Scene Construction, Multi-view Relational Understanding, Temporal Reasoning, and Generalization. Unlike prior benchmarks, DriveSpatial is generated from a dynamic multi-relational scene graph that encodes object states, spatial relations, interactions, camera visibility, and temporal correspondences, enabling QA pairs that enforce genuine cross-view and spatiotemporal reasoning. Evaluating 15 representative VLMs reveals a substantial human-model gap: the strongest model trails humans by 28.4 points, with Cognitive Scene Construction emerging as the key bottleneck. Further diagnostics show that language-only prompting is insufficient, while explicit BEV grounding consistently improves performance. These results suggest that current VLMs lack the scene-construction ability needed for reliable spatiotemporal driving intelligence. DriveSpatial and its construction pipeline will be released to support future research.