17 papers
R3D: Quantitative 3D Spatial Reasoning for Egocentric Wearables
Maxwell Horton, Wei Lu, Quan Tran +6
Quantitative 3D spatial reasoning from egocentric RGB-D video is a critical capability for next-generation wearable assistants. Yet existing benchmarks do not reflect the challenge…
TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
Zhepei Wei, Xiao Yang, Kai Sun +12
While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly…
SaliMory: Orchestrating Cognitive Memory for Conversational Agents
Kai Zhang, Xinyuan Zhang, Hongda Jiang +11
Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context windows with raw retrieval degrad…
Pixel-Grounded Retrieval for Knowledgeable Large Multimodal Models
Jeonghwan Kim, Renjie Tao, Sanat Sharma +8
Visual Question Answering (VQA) often requires coupling fine-grained perception with factual knowledge beyond the input image. Prior multimodal Retrieval-Augmented Generation (MM-R…
WearVox: An Egocentric Multichannel Voice Assistant Benchmark for Wearables
Zhaojiang Lin, Yong Xu, Kai Sun +17
Wearable devices such as AI glasses are transforming voice assistants into always-available, hands-free collaborators that integrate seamlessly with daily life, but they also intro…
WearVQA: A Visual Question Answering Benchmark for Wearables in Egocentric Authentic Real-world scenarios
Eun Chang, Zhuangqun Huang, Yiwei Liao +19
We introduce WearVQA, the first benchmark specifically designed to evaluate the Visual Question Answering (VQA) capabilities of multi-model AI assistant on wearable devices like sm…