11 papers
LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation
Jing Li, Pan Liu, Meng Zhao +7
Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both covariate and label shifts, without access to source d…
TEMPO: Scaling Test-time Training for Large Reasoning Models
Qingyang Zhang, Xinke Kong, Haitao Wu +7
Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the reach of offline training. De…
Quantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model
Wenbo Qiao, Peng Zhang, Qinghua Hu
Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likeliho…
Semantic Energy: Detecting LLM Hallucination Beyond Entropy
Huan Ma, Jiadong Pan, Jing Liu +7
Large Language Models (LLMs) are being increasingly deployed in real-world applications, but they remain susceptible to hallucinations, which produce fluent yet incorrect responses…
DOTA: Distributional Test-Time Adaptation of Vision-Language Models
Zongbo Han, Jialong Yang, Guangyu Wang +4
Vision-language foundation models (VLMs), such as CLIP, exhibit remarkable performance across a wide range of tasks. However, deploying these models can be unreliable when signific…
Large Model Driven Solar Activity AI Forecaster: A Scalable Dual Data-Model Framework
Jingjing Wang, Pengyu Liang, Tingyu Wang +12
Solar activity drives space weather, affecting Earth's magnetosphere and technological infrastructure, which makes accurate solar flare forecasting critical. Current space weather…