Electroencephalography (EEG)-based closed-loop neuromodulation calls for a subject-specific structured state, as opposed to a single disease probability, specifying which brain regions are deviant, at which frequencies, and at which lags. Sensor-space models keep the strongest diagnostic evidence without anatomy, sourc...
Jia-Ying Wang, Shou-Qian Shi, Yu-Tong Chen et al.· 0 citations
Lens is a training-free framework that makes representation readout task-directed, and achieves an overall Precision@1 of 63.9 across all 36 MMEB datasets, outperforming the closest same-backbone training-free embedding baseline by 10.2 points.
Xin-Ran Liu, Shou-Qian Shi, Yi-Xian Chen et al.· 0 citations
Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions. CLIP provides a strong foundation for these tasks by learning a shared image-text embedding space from large-s...
The proposed IRIS (Identity Representations from Internal States), a training-free framework that constructs for each entity an iris-like signature encoding its distinctive and stable identity characteristics, thereby forming a shared space in which each entity is encoded once and can be aligned across different KGs th...
Xin-Ran Liu, Shengtao Li, Shouqian Shi et al.· arXiv.org· 0 citations
Penelope is introduced, an efficient latent-reasoning framework for pretrained decoder-only Transformers that localizes recurrent computation to a selected decoder interval and attains competitive accuracy relative to established latent-reasoning models while reducing measured inference latency.
Yutong Chen, Shouqian Shi, Xin-Ran Liu et al.· arXiv.org· 0 citations
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