arXiv subjects

Yunqi Guo

Publications and source records attributed to Yunqi Guo.

7 recordsLinked to original sources

arXiv2026

Multimodal Federated Learning under Dual-Axis Modality Missingness

Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modality missingness: clients have different modality sets, and individual samples may contain only subsets of the modalities available locally. Existing methods typically address these two axes separately. We propose Flux, a multimodal federated learning framework built around two complementary components. First, modality-aware confidence tempering learns sample-specific confidence for each modality through mask-aware unimodal supervision and fuses the confidence estimates from observed modalities into a sample-adaptive temperature that adjusts predictive sharpness according to evidence quality and completeness. Second, gradient-decoupled private adaptation applies this temperature only to a client-private prediction pathway, while training the shared federated model with a standard, untempered objective. This enables sample-specific, client-local confidence adaptation without allowing confidence-dependent gradients to perturb shared representation learning. Across four multimodal datasets, Flux achieves the highest average macro-F1 on every dataset, outperforming the strongest dataset-specific baseline by 0.8~2.2 points and by 1.6 points on average. Additional analyses demonstrate favorable calibration, temperature sensitivity to both modality missingness and input corruption, and more stable shared optimization under private-only tempering. Our code is available at https://github.com/AdibaOrz/Flux.

cs.LG↗

arXiv2025

ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild

Recent studies have begun to explore proactive large language model (LLM) agents that provide unobtrusive assistance by automatically leveraging contextual information, such as in code editing and in-app suggestions. However, most focus on short, task-specific episodes or on-screen contexts, rather than continuously perceiving and assisting users throughout daily life. Enabling such in-the-wild assistance requires continuous sensing of users' surroundings, which can incur substantial system overhead. In this work, we propose ProAgent, an end-to-end proactive agent system that harnesses on-demand sensory contexts to provide in-the-wild assistance. ProAgent first employs on-demand tiered perception to continuously sense users' surroundings by integrating low-cost contextual cues with richer perception on demand, and uses proactive-oriented context extraction to derive hierarchical contexts integrating both sensory contexts and human preferences. ProAgent then employs a context-aware proactive reasoner to infer user needs and invokes external tools to deliver proactive assistance. We implement ProAgent on AR glasses and evaluate it on a public dataset and a real-world dataset. Results demonstrate that ProAgent achieves up to 27.7% higher proactive prediction accuracy and 20.5% lower false detection than state-of-the-art baselines. A user study with 20 participants shows that 85% were satisfied with ProAgent and willing to use it in daily life.

cs.AI↗

arXiv2025

CoHear: Conversation Enhancement via Multi-Earphone Collaboration

In crowded places such as conferences, background noise, overlapping voices, and lively interactions make it difficult to have clear conversations. This situation often worsens the phenomenon known as "cocktail party deafness." We present ClearSphere, the collaborative system that enhances speech at the conversation level with multi-earphones. Real-time conversation enhancement requires a holistic modeling of all the members in the conversation, and an effective way to extract the speech from the mixture. ClearSphere bridges the acoustic sensor system and state-of-the-art deep learning for target speech extraction by making two key contributions: 1) a conversation-driven network protocol, and 2) a robust target conversation extraction model. Our networking protocol enables mobile, infrastructure-free coordination among earphone devices. Our conversation extraction model can leverage the relay audio in a bandwidth-efficient way. ClearSphere is evaluated in both real-world experiments and simulations. Results show that our conversation network obtains more than 90\% accuracy in group formation, improves the speech quality by up to 8.8 dB over state-of-the-art baselines, and demonstrates real-time performance on a mobile device. In a user study with 20 participants, ClearSphere has a much higher score than baseline with good usability.

cs.SD↗

arXiv2025

VibOmni: Towards Scalable Bone-conduction Speech Enhancement on Earables

Earables, such as True Wireless Stereo earphones and VR/AR headsets, are increasingly popular, yet their compact design poses challenges for robust voice-related applications like telecommunication and voice assistant interactions in noisy environments. Existing speech enhancement systems, reliant solely on omnidirectional microphones, struggle with ambient noise like competing speakers. To address these issues, we propose VibOmni, a lightweight, end-to-end multi-modal speech enhancement system for earables that leverages bone-conducted vibrations captured by widely available Inertial Measurement Units (IMUs). VibOmni integrates a two-branch encoder-decoder deep neural network to fuse audio and vibration features. To overcome the scarcity of paired audio-vibration datasets, we introduce a novel data augmentation technique that models Bone Conduction Functions (BCFs) from limited recordings, enabling synthetic vibration data generation with only 4.5% spectrogram similarity error. Additionally, a multi-modal SNR estimator facilitates continual learning and adaptive inference, optimizing performance in dynamic, noisy settings without on-device back-propagation. Evaluated on real-world datasets from 32 volunteers with different devices, VibOmni achieves up to 21% improvement in Perceptual Evaluation of Speech Quality (PESQ), 26% in Signal-to-Noise Ratio (SNR), and about 40% WER reduction with much less latency on mobile devices. A user study with 35 participants showed 87% preferred VibOmni over baselines, demonstrating its effectiveness for deployment in diverse acoustic environments.

cs.SD↗

arXiv2025

OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis

Traditional Chinese Medicine (TCM) represents a rich repository of ancient medical knowledge that continues to play an important role in modern healthcare. Due to the complexity and breadth of the TCM literature, the integration of AI technologies is critical for its modernization and broader accessibility. However, this integration poses considerable challenges, including the interpretation of obscure classical Chinese texts and the modeling of intricate semantic relationships among TCM concepts. In this paper, we develop OpenTCM, an LLM-based system that combines a domain-specific TCM knowledge graph and Graph-based Retrieval-Augmented Generation (GraphRAG). First, we extract more than 3.73 million classical Chinese characters from 68 gynecological books in the Chinese Medical Classics Database, with the help of TCM and gynecology experts. Second, we construct a comprehensive multi-relational knowledge graph comprising more than 48,000 entities and 152,000 interrelationships, using customized prompts and Chinese-oriented LLMs such as DeepSeek and Kimi to ensure high-fidelity semantic understanding. Last, we empower OpenTCM with GraphRAG, enabling high-fidelity ingredient knowledge retrieval and diagnostic question-answering without model fine-tuning. Experimental evaluations demonstrate that OpenTCM achieves mean expert scores (MES) of 4.378 in ingredient information retrieval and 4.045 in diagnostic question-answering tasks, outperforming state-of-the-art solutions in real-world TCM use cases.

cs.IR↗

arXiv2025

Visualizing the Invisible: A Generative AR System for Intuitive Multi-Modal Sensor Data Presentation

Understanding sensor data can be difficult for non-experts because of the complexity and different semantic meanings of sensor modalities. This leads to a need for intuitive and effective methods to present sensor information. However, creating intuitive sensor data visualizations presents three key challenges: the variability of sensor readings, gaps in domain comprehension, and the dynamic nature of sensor data. To address these issues, we propose Vivar, a novel system that integrates multi-modal sensor data and presents 3D volumetric content for AR visualization. In particular, we introduce a cross-modal embedding approach that maps sensor data into a pre-trained visual embedding space through barycentric interpolation. This approach accurately reflects value changes in multi-modal sensor information, ensuring that sensor variations are properly shown in visualization outcomes. Vivar also incorporates sensor-aware AR scene generation using foundation models and 3D Gaussian Splatting (3DGS) without requiring domain expertise. In addition, Vivar leverages latent reuse and caching strategies to accelerate 2D and AR content generation, demonstrating 11x latency reduction without compromising quality. A user study involving over 503 participants, including domain experts, demonstrates Vivar's effectiveness in accuracy, consistency, and real-world applicability, paving the way for more intuitive sensor data visualization.

cs.HC↗

arXiv2024

SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live Interactions

Social interactions are fundamental to human life. The recent emergence of large language models (LLMs)-based virtual assistants has demonstrated their potential to revolutionize human interactions and lifestyles. However, existing assistive systems mainly provide reactive services to individual users, rather than offering in-situ assistance during live social interactions with conversational partners. In this study, we introduce SocialMind, the first LLM-based proactive AR social assistive system that provides users with in-situ social assistance. SocialMind employs human-like perception leveraging multi-modal sensors to extract both verbal and nonverbal cues, social factors, and implicit personas, incorporating these social cues into LLM reasoning for social suggestion generation. Additionally, SocialMind employs a multi-tier collaborative generation strategy and proactive update mechanism to display social suggestions on Augmented Reality (AR) glasses, ensuring that suggestions are timely provided to users without disrupting the natural flow of conversation. Evaluations on three public datasets and a user study with 20 participants show that SocialMind achieves 38.3% higher engagement compared to baselines, and 95% of participants are willing to use SocialMind in their live social interactions.

cs.AI↗