arXiv AI July 7, 2026 By Youngkil Song, Yoonjae Baek, Dongwon Kim, Inho Kim, Dongkeun Kim, Suha Kwak

EventCoT: Event-centric Video Chain-of-thought for Reasoning Temporal Localization

arXiv:2607. 04872v1 Announce Type: cross Abstract: Reasoning temporal localization (RTL) requires a model to generate an answer that itself contains the time interval supporting it, so high-level reasoning and precise temporal grounding must be produced jointly in a single response.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

Sep 1

Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting

The paper introduces structured video prompting, a training‑free inference‑time technique that augments input videos with lightweight spatial and temporal structure to provide explicit anchors for evidence organization. By applying this method to two video benchmarks and two open video‑language models, the authors demonstrate performance improvements across several tasks, with gains varying by model and task. The study suggests that failures in video‑language models stem not only from reasoning capacity but also from how video evidence is presented during inference.

By Sadegh Mohammadian

llms benchmarks

arXiv Machine Learning

Jun 2

TLG: Temporal-Logic Grounding for Video Question Answering via Source-Annotation Reconstruction and Category-Targeted Reasoning

arXiv:2606. 01591v1 Announce Type: cross Abstract: The TimeLogic Challenge evaluates formal temporal-logic reasoning over video - 16 operators (before, after, until, since, always, co-occur, ordering, ...

By Ali Alavi

llms nlp multimodal benchmarks

arXiv Computation and Language

Sep 15

Interpretable Temporal Video Reasoning with EventGraph and EventField

arXiv:2609.13258v1 Announce Type: cross Abstract: We present a structured temporal video reasoning pipeline built around a discrete EventGraph, a continuous EventField, and a human-readable EventGlyp...

By Durgendra Narayan Singh

llms multimodal benchmarks

arXiv Computer Vision

Sep 23

TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action

arXiv:2505.01583v2 Announce Type: replace Abstract: Understanding causal event relationships and achieving fine-grained temporal grounding in videos remain challenging for vision-language models (VLM...

By Jen-Hao Cheng, Yi-Hao Peng, Huapeng Zhou, Vivian Wang, Huayu Wang, Hsiang-Wei Huang, Wenhao Chai, Hou-I Liu, Kuang-Ming Chen, Cheng-Yen Yang, Yi-Ling Chen, Vibhav Vineet, Qin Cai, Jenq-Neng Hwang

llms computer-vision multimodal benchmarks

arXiv AI

Jun 30

Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task

arXiv:2512. 10359v1 Announce Type: cross Abstract: Video Question Answering (VideoQA) task serves as a critical playground for evaluating whether foundation models can effectively perceive, understand, and reason about dynamic real-world scenarios.

By Sunqi Fan, Jiashuo Cui, Meng-Hao Guo, Shuojin Yang

llms agents nlp multimodal

arXiv AI

Jul 29

Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding

arXiv:2607. 24794v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding.

By Linghao Meng, Qiankun Li, Junyuan Mao, Pujin Liao, Zhicheng He, Enbo Zhang, Kun Wang, Yang Liu, Huazhu Fu, Yueming Jin

llms multimodal benchmarks safety