arXiv Computation and Language
EgoVoice: Proactive Spoken Assistance from Egocentric Multimodal Streams
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EgoArgus: Benchmarking VLMs as Situational Assistants for Modality-Grounded User Supports
EgoArgus is a new, human‑annotated dataset that tests visual‑language models (VLMs) as situational assistants in five everyday dialogue‑video scenarios. It evaluates how well VLMs understand and decide when to intervene, especially when visual and textual cues are helpful, irrelevant, or conflicting. The study finds that current VLMs still struggle to reliably act as egocentric assistants and that existing modality‑bias mitigation methods offer limited improvement.
Hugging Face Trending Papers
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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
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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?
arXiv Machine Learning
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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.
arXiv AI
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Ambient @ EgoProactive 2026 : Proactive Egocentric Assistance with Visually Grounded Supervision
arXiv:2609.07099v1 Announce Type: cross Abstract: We present our submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, which ranked first in the large-model division and second...
arXiv Computer Vision
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OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs
OmniAssistBench is a new benchmark for evaluating omni-modal large language models (Omni-LLMs) as real‑time video assistants that actively guide users toward goals. The benchmark addresses the challenge of dynamic interaction paths by providing models with predefined priors from source videos, forcing them to follow the same routes as users. The dataset was constructed by reverse‑engineering existing Internet videos into multi‑turn clips, a process that required over 1,000 expert person‑hours. Results show that proprietary Gemini‑3‑Pro scores 66.4/100 while open‑source Qwen3‑Omni‑Instruct scores 51.2, revealing that current models often give incorrect or incomplete answers, struggle with visual prompts, and fail to maintain context or delay responses until target events.