8 citations · 15 across the 20 of their papers we have counts for
13 papers · 1 filter
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…
MobileLLM-Pro Technical Report
Patrick Huber, Ernie Chang, Wei Wen +16
Efficient on-device language models around 1 billion parameters are essential for powering low-latency AI applications on mobile and wearable devices. However, achieving strong per…
CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmark
Jiaqi Wang, Xiao Yang, Kai Sun +38
Wearable devices such as smart glasses are transforming the way people interact with their surroundings, enabling users to seek information regarding entities in their view. Multi-…
AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval
Kai Zhang, Xinyuan Zhang, Ejaz Ahmed +11
Accurate recall from large scale memories remains a core challenge for memory augmented AI assistants performing question answering (QA), especially in similarity dense scenarios w…
Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage
Siddhant Arora, Haidar Khan, Kai Sun +14
End-to-end speech-in speech-out dialogue systems are emerging as a powerful alternative to traditional ASR-LLM-TTS pipelines, generating more natural, expressive responses with sig…
SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning
Shicheng Liu, Kai Sun, Lisheng Fu +8
Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complicates usage, and reliably extract…
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs?
Kai Sun, Yin Huang, Srishti Mehra +11
The advent of Large Language Models (LLMs) has significantly advanced web-based Question Answering (QA) systems over semi-structured content, raising questions about the continued…
Memory-QA: Answering Recall Questions Based on Multimodal Memories
Hongda Jiang, Xinyuan Zhang, Siddhant Garg +10
We introduce Memory-QA, a novel real-world task that involves answering recall questions about visual content from previously stored multimodal memories. This task poses unique cha…
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…
PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning
Mohammad Kachuee, Teja Gollapudi, Minseok Kim +10
Retrieval-augmented generation (RAG) often falls short when retrieved context includes confusing semi-relevant passages, or when answering questions require deep contextual underst…
Proactive Assistant Dialogue Generation from Streaming Egocentric Videos
Yichi Zhang, Xin Luna Dong, Zhaojiang Lin +5
Recent advances in conversational AI have been substantial, but developing real-time systems for perceptual task guidance remains challenging. These systems must provide interactiv…
ConfRAG: Confidence-Guided Retrieval-Augmenting Generation
Yin Huang, Yifan Ethan Xu, Kai Sun +12
Can Large Language Models (LLMs) be trained to avoid hallucinating factual statements, and can Retrieval-Augmented Generation (RAG) be triggered only when necessary to reduce retri…