6 citations · 18 across the 16 of their papers we have counts for
9 papers · 1 filter
A Behavior-Guided Online Probabilistic Forecasting Method for Electric vehicle Charging Loads
Chenghan Li, Qingxiang Liu, Yinliang Xu +1
Electric vehicle (EV) charging loads exhibit strong behavioral heterogeneity and temporal variability, posing significant challenges for online probabilistic forecasting under evol…
Perceive to Hypothesize, Verify to Ground: An Agentic Reasoning Framework for Open-World Geo-Localization
Yutian Jiang, Ruijie Li, Sisuo Lyu +4
Open-world geo-localization requires models to reason over ambiguous visual cues through multi-step reasoning and external knowledge grounding. While recent large vision-language m…
LiveHouse-TS: An Open-world Living Benchmark for Time Series Foundation Models
Haomin Wen, Ziyu Zhou, Qingxiang Liu +2
Time Series Foundation Models (TSFMs) have recently emerged as a highly promising paradigm for cross-domain zero-shot forecasting. However, existing evaluation protocols predominan…
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
Qingxiang Liu, Anqi Liang, Heng Wang +1
Federated learning has emerged as a promising paradigm for spatio-temporal forecasting (STF), enabling collaborative model training without sharing raw observations. Existing feder…
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
Qingxiang Liu, Anqi Liang, Zhuoyang Jiang +5
Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While recent methods incorporating lar…
LifeSide: Benchmarking Agents as Lifelong Digital Companions
Yuqian Wu, Zhijie Deng, Wei Chen +8
Lifelong digital companions must integrate cross-session cues, continually update their understanding of users, and adapt to shifting privacy boundaries. Existing evaluations fail…
Discrete Prototypical Memories for Federated Time Series Foundation Models
Liwei Deng, Qingxiang Liu, Xinhe Niu +5
Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to…
TS-Memory: Plug-and-Play Memory for Time Series Foundation Models
Sisuo Lyu, Siru Zhong, Tiegang Chen +6
Time Series Foundation Models (TSFMs) achieve strong zero-shot forecasting through large-scale pre-training, but adapting them to downstream domains under distribution shift remain…
Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models
Qingxiang Liu, Zhiqing Cui, Xiaoliang Luo +7
The underperformance of existing multimodal large language models for time series reasoning lies in the absence of rationale priors that connect temporal observations to their down…