Hugging Face Trending Papers

Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos

When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than merely reactive.

arXiv AI

Jul 14

Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos

arXiv:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?

By Gong Sitong, Tianyu Yan, Caixin Kang, Bo Zheng, Xiang Ruan, Huchuan Lu, Kaipeng Zhang, Yoichi Sato, Yifei Huang

rag agents benchmarks safety

arXiv AI

5d ago

EgoCITE: Context-Augmented Indexing and Time-Aware Retrieval for Long-Horizon Egocentric Memory

arXiv:2608. 12627v1 Announce Type: cross Abstract: Long-horizon egocentric memory transforms continuous first-person video and audio into a searchable record of past experiences.

By Le Zhang, Ke Sun

llms rag agents multimodal safety

arXiv AI

5d ago

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

Jun 29

EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning

arXiv:2603. 09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-term physical consequences of actions from an egocentric viewpoint.

By Chengjun Yu, Xuhan Zhu, Chaoqun Du, Pengfei Yu, Wei Zhai, Yang Cao, Zheng-Jun Zha

llms agents robotics multimodal benchmarks

arXiv AI

Jun 15

StreamMemBench: Streaming Evaluation of Agent Memory for Future-Oriented Assistance

arXiv:2606. 14571v1 Announce Type: new Abstract: A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance.

By Guanming Liu, Yuqi Ren, Hansu Gu, Peng Zhang, Weihang Wang, Jiahao Liu, Ning Gu, Tun Lu

agents benchmarks

arXiv Machine Learning

Aug 3

EgoEverything: A Benchmark for Human Behavior Inspired Long Context Egocentric Video Understanding in AR Environment

arXiv:2604. 08342v2 Announce Type: replace Abstract: Long context egocentric video understanding has recently attracted significant research attention, with augmented reality (AR) highlighted as one of its most important application domains.

By Qiance Tang, Ziqi Wang, Jieyu Lin, Ziyun Li, Barbara De Salvo, Sai Qian Zhang

benchmarks

arXiv AI

Aug 6

GROVE: Growing and Reasoning over Temporally Stratified Memory from Streaming Video Experience

arXiv:2608. 02392v2 Announce Type: replace-cross Abstract: A wearable assistant should both answer questions about its visual history and recognize when that history is useful to the present situation.

By Sitong Gong, Caixin Kang, Tianyu Yan, Guo Chen, Bo Zheng, Kaipeng Zhang, Yunzhi Zhuge, Xiang Ruan, Huchuan Lu, Yifei Huang

benchmarks

arXiv AI

Aug 3

SERUM: State Extraction and Refinement for User Modeling

arXiv:2607. 29181v1 Announce Type: cross Abstract: Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow.

By Andy J. Phu, James Mooney, Karin de Langis, Khanh Chi Le, Dongyeop Kang

rag agents multimodal safety

arXiv AI

Jun 2

Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion

arXiv:2606. 00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos.

By Shivam Singh, Saptarshi Majumdar, Pratik Prabhanjan, Zicheng Liu, Emad Barsoum

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 7

Agentic Very Long Video Understanding

arXiv:2601. 18157v3 Announce Type: replace-cross Abstract: The advent of always-on personal AI assistants, enabled by all-day wearable devices such as smart glasses, demands a new level of contextual understanding, one that goes beyond short, isolated events to encompass the continuous, longitudinal stream of egocentric video.

By Aniket Rege, Arka Sadhu, Yuliang Li, Kejie Li, Ramya Korlakai Vinayak, Yuning Chai, Yong Jae Lee, Hyo Jin Kim

llms rag agents multimodal benchmarks

arXiv AI

Jun 8

Watch, Remember, Reason: Human-View Video Understanding with MLLMs

arXiv:2606. 07433v1 Announce Type: cross Abstract: Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios.

By Jiahao Meng, Yue Tan, Qi Xu, Kuan Gao, Weisong Liu, Yanwei Li, Jason Li, Lingdong Kong, Haochen Wang, Qianyu Zhou, Jiangning Zhang, Guangliang Cheng, Yunhai Tong, Lu Qi, Minghsuan Yang

llms multimodal benchmarks safety

arXiv AI

Jul 29

ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop

arXiv:2607. 24770v1 Announce Type: new Abstract: Procedural tasks such as furniture assembly and home repair impose substantial cognitive demands because users must interpret instructions, track task progress, reason about spatial state, and recover from errors while performing physical actions.

By Azizul Zahid, Subrata Biswas, Bashima Islam, Sai Swaminathan

llms agents nlp multimodal safety

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