1 citations · 3 across the 13 of their papers we have counts for
13 papers
TRACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding
Sparsh Rastogi, Tanmay Kumar, Baiyu Chen +3
Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to activity labels. Recent LM-based…
RubricsTree: Scalable and Evolving Open-Ended Evaluation of Personal Health Agents across Health Memory and Medical Skills
Weizhi Zhang, Zechen Li, Hamid Palangi +16
The LLM-empowered personal health agents with user health (sensor) metrics have offered a promising pathway to alleviate global disparities in healthcare access. However, large-sca…
AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild
Baiyu Chen, Zechen Li, Wilson Wongso +5
As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highl…
MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs
Ruiyi Yang, Zechen Li, Hao Xue +2
Self-evolving language-model agents must decide what to learn next and how to preserve what they have learned across iterations. Existing systems typically carry this cross-iterati…
GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
Zechen Li, Keerthana Natarajan, Weizhi Zhang +11
Continuous glucose monitoring (CGM) provides a dense view of daily metabolic physiology, yet existing generic time-series and CGM-specific foundation models often encode glucose tr…
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
Hada Melino Muhammad, Zechen Li, Flora Salim +1
Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; -cell dysfunction), but population-scale deployment faces two coupled probl…
Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?
Qianshan Wei, Yishan Yang, Siyi Wang +12
Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual tools) and Knowledge Exp…
ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents
Zechen Li, Baiyu Chen, Hao Xue +1
Motion sensor time-series are central to Human Activity Recognition (HAR), yet conventional approaches are constrained to fixed activity sets and typically require costly parameter…
COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition
Baiyu Chen, Wilson Wongso, Zechen Li +3
The goal of creating intelligent, human-centered wearable systems for continuous activity understanding faces a fundamental trade-off: Egocentric video-based models capture rich se…
SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition
Zechen Li, Shohreh Deldari, Linyao Chen +2
We introduce SensorLLM, a two-stage framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from sensor time-series data. Despite their stro…
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Du Yin, Jinliang Deng, Shuang Ao +6
Training models on spatio-temporal (ST) data poses an open problem due to the complicated and diverse nature of the data itself, and it is challenging to ensure the model's perform…
QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning
Zechen Li, Anders Søgaard
Synthetic datasets have successfully been used to probe visual question-answering datasets for their reasoning abilities. CLEVR (johnson2017clevr), for example, tests a range of vi…
Self-supervised Regularization for Text Classification
Meng Zhou, Zechen Li, Pengtao Xie
Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training classification models is limited, which re…