arXiv AI

Continual Visual and Verbal Learning Through a Child's Egocentric Input

arXiv:2606. 05115v1 Announce Type: cross Abstract: Children learn the meanings of words from a continuous, temporally structured stream of egocentric experience.

arXiv AI

Jul 15

The TIME Machine: On The Power of Motion for Efficient Perception

arXiv:2605. 23045v2 Announce Type: replace-cross Abstract: Video representation learning has seen tremendous progress in recent years.

By Mantas Skackauskas, Xinyue Hao, Laura Sevilla-Lara

rag benchmarks

arXiv AI

2d ago

EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding

EgoMemReason is a new benchmark for week‑long egocentric video understanding that focuses on memory‑driven reasoning rather than simple perception tasks. It tests three memory types—entity, event, and behavior—across 500 questions, each requiring evidence from an average of 5.1 video segments and 25.9 hours of backtracking. Evaluation of 17 models shows that even the best achieves only 39.6% accuracy, highlighting the difficulty of long‑horizon memory in multimodal systems.

By Ziyang Wang, Yue Zhang, Shoubin Yu, Ce Zhang, Zengqi Zhao, Jaehong Yoon, Hyunji Lee, Gedas Bertasius, Mohit Bansal

agents nlp robotics multimodal benchmarks

arXiv AI

Aug 14

EgoMonth: A Month-Level Egocentric Video Benchmark for Long-Term Spatiotemporal Memory

arXiv:2608. 13113v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to substantial progress in video understanding, accompanied by a growing number of long video benchmarks.

By Weitao Chen, Hu Jiaxin, Xie Tianyidan, Yang Li, Yuyi Qian, Banghao Xu, Ziheng Tang, Shenyi Wang, Mingyue Yu, Duo Li, Jiacheng Shi, Gao Wang, Zhan Xu, Zhicheng Qiu, Xuanfu Li, Jian Yang, Lanjun Wang, Zili Yi

llms multimodal benchmarks

arXiv AI

Jul 2

LeVLJEPA: End-to-End Vision-Language Pretraining Without Negatives

arXiv:2607. 00784v1 Announce Type: cross Abstract: Vision-language pretraining remains dominated by contrastive objectives, whereas vision-only self-supervised learning has largely adopted non-contrastive methods.

By Lukas Kuhn, Giuseppe Serra, Randall Balestriero, Florian Buettner

llms rag computer-vision multimodal

arXiv AI

Jun 26

Semantic Generative Tuning for Unified Multimodal Models

arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.

By Songsong Yu, Yuxin Chen, Ying Shan, Yanwei Li

computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 3

Visual Instruction Tuning Aligns Modalities through Abstraction

arXiv:2606. 03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text.

By Luis Palacios, Lorenzo Basile, Diego Doimo, Alberto Cazzaniga

llms rag fine-tuning multimodal benchmarks safety

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

4d ago

Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning

arXiv:2608. 15869v1 Announce Type: cross Abstract: Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments.

By Xiaoyu Zhu, Xinke Deng, Suresh Taddewadikar, Arnab Kumar Mondal, Zhongyu Jiang, Ian Fasel, Joerg Liebelt

llms rag robotics fine-tuning multimodal

arXiv AI

Jun 12

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers

arXiv:2606. 13289v1 Announce Type: cross Abstract: Holistic visual tokenizers are fundamental to unified multimodal models (UMMs) as they map diverse visual inputs into a unified representation space.

By Guozhen Zhang, Xuerui Qiu, Yutao Cui, Tianhui Song, Changlin Li, Junzhe Li, Tao Huang, Xiao Zhang, Yang Li, Jianbing Wu, Miles Yang, Zhao Zhong, Liefeng Bo, Limin Wang

llms rag diffusion computer-vision nlp multimodal

arXiv AI

Jun 2

SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition

arXiv:2606. 00732v1 Announce Type: new Abstract: Learning long-range non-stationary temporal patterns remains a core challenge for modern sequence models, particularly in strict streaming settings.

By Jayanta Dey, Shikhar Srivastava, Itamar Lerner, Christopher Kanan, Dhireesha Kudithipudi

llms benchmarks

arXiv AI

Jun 9

MOSS-Video-Preview: Toward Real-Time Video Understanding via Cross-Attention

arXiv:2606. 07639v1 Announce Type: cross Abstract: Video understanding is shifting from the offline paradigm -- taking a fully recorded video as input and producing a single answer after it ends -- toward real-time interaction, in which the model perceives new frames while still replying, revises its answer as new evidence appears, and remains silent when there is nothing to say.

By Pengyu Wang, Chenkun Tan, Shaojun Zhou, Wei Huang, Qirui Zhou, Zhan Huang, Zhen Ye, Jijun Cheng, Xiaomeng Qian, Yanxin Chen, Xingyang He, Huazheng Zeng, Chenghao Wang, Pengfei Wang, Hongkai Wang, Shanqing Gao, Yixian Tian, Chenghao Liu, Xinghao Wang, Botian Jiang, Xipeng Qiu

efficiency multimodal

arXiv AI

Jun 16

Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning

arXiv:2606. 14765v1 Announce Type: cross Abstract: Self-supervised video representation learning has recently advanced through contrastive learning, masked reconstruction, and predictive representation learning.

By Qinwu Xu

rag safety

Related stories