arXiv:2606. 10716v1 Announce Type: cross Abstract: Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich contextualized representations.
By Roberto Mart\'inez-Cruz, Alvaro J. L\'opez-L\'opez, Jos\'e Portela
EEG-Captioner introduces a new EEG-to-language framework that grounds language generation in measurable EEG signal properties. The authors built a large dataset of 160,781 EEG segments annotated with question‑answer pairs targeting spectral, spatial, temporal, and lateral properties, and paired each segment with a feature‑grounded caption. Their Dual‑Domain EEG Encoder jointly models temporal waveforms and spectral‑spatial representations, achieving a macro‑averaged balanced accuracy of 88.81% on signal‑level QA and improving caption factual consistency from 82.06% to 83.46% compared to caption‑only models.
By Leyuan Jin, Jiahao Mei, Yixuan Yuan, Shimeng Li, Mengyue Wu, Yue Ding
arXiv:2610.28502v1 Announce Type: new
Abstract: Smart glasses can help users understand their activities and surroundings, but small vision-language models must preserve useful evidence within limite...
By Trong-Thang Pham, Anh Nguyen, Hien Van Nguyen, Ngan Le
The study examines how reading times differ between human-written texts and rewrites generated by large language models (LLMs). Using a RoBERTa-RT model trained on eye-tracking data, the authors compare predicted reading times for 496 paired articles and find that LLM rewrites are shorter overall but have higher predicted reading times per word. These differences correlate with linguistic changes such as longer, less frequent words, greater lexical diversity, and higher contextual predictability.
By Kamer Cekini, Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti
arXiv:2603. 03312v3 Announce Type: replace-cross Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task.
By Yuchen Wang, Haonan Wang, Yu Guo, Honglong Yang, Xiaomeng Li
arXiv:2607. 08152v1 Announce Type: cross Abstract: On the recent EyeBench benchmark, predicting reading comprehension from eye movements exposes a stark gap: text-aware models using pretrained language models reach 56--63% AUROC, while gaze-only models operate at chance.
By Sumin Lee, Kyeonghun Kim, Subeen Lee, Jiwon Yang, Tien Nguyen, Ken Ying-Kai Liao, Nam-Joon Kim