The paper investigates how EEG embeddings from signal processing and foundation models encode trait and state information. Using a hierarchy metric and kNN purity on a BCI dataset, it finds that most embeddings form a macro‑trait hierarchy with micro‑states inside, which hampers state discrimination. Normalizing embeddings reduces trait leakage and improves state classification accuracy by about 10%, and fine‑tuning shifts the hierarchy toward macro‑state organization, enhancing decoding performance.
By Anusha Madapura, Aditya Kommineni, Shrikanth Narayanan
Alexa+ devices use animated icons to reinforce spoken content, but the original hand‑drawn icons were too slow to scale. A new automated pipeline now generates vectorised animated icons from text, breaking the process into image generation, semantic decomposition, vectorisation, styling, and animation. The key improvement is a reinforcement‑learning‑based fine‑tuning of the image generation stage using objective verifiers, boosting the pass rate from 15.7% to 58.7% under an independent VLM judge.
By Rohan Sequeira, Matthew Rosenberg, Amin Mahmoudabadbozchelou, Oleg Poliannikov, Jonathan Rubin
LegalEvolver is a query‑optimization framework for legal judgment prediction that generates legally informative queries to improve retrieval quality. It uses heuristic query seeding, summarizes rollout experiences into rubrics, and applies rubric‑guided reinforcement learning for iterative self‑evolution. Experiments on large benchmarks show that evolving rubrics outperform fixed ones and that rubric guidance boosts performance beyond direct supervised fine‑tuning plus RL.
By Xinyu Yang, Jiejun Tan, Chenlong Deng, Chenghao Zhang, Tongyu Wen, Zhicheng Dou
SUMONE is a lightweight summarizer designed to compress long interaction histories of language‑model agents. It is trained via masked reconstruction, where a frozen reconstructor predicts randomly masked parts of the original trajectory; reconstruction quality plus a length penalty yields a task‑agnostic reward for optimizing the summarizer with GRPO. At inference, only the summarizer remains, enabling plug‑and‑play context compression that reduces inference cost by roughly 78 % on DeepSearchQA and 75 % on SWE‑bench without affecting the underlying agent.
By Jizhe Zhang, Ang Li, Chenheng Zhang, Yisen Wang
CellCanon is an agentic cellular world model that integrates single‑cell expression prediction, evidence‑grounded explanation, and observation‑driven updating. Its predictive core reuses condition‑level biological evidence to generate gene‑specific readouts, while a shared world state links predictions, measurements, and source‑linked claims to support explanation and revision. On 339 held‑out SciPlex3 conditions across three cell lines, CellCanon outperforms baselines in differential‑expression AUPRC, Macro‑F1, and direction Macro‑F1, and its explanation module correctly answers all answerable questions with supporting proofs.
By Zhaokang Liang, Shuyang Zhuang, Xiaoran Jiao, Hao Chen
The paper explores training a large language model solely on textual video descriptions to perform zero‑shot video temporal localization, eliminating the need for paired video‑text data during training. Using a 134K‑example corpus and adapting Qwen3‑8B with LoRA on CLIP or LLM2CLIP embeddings, the authors evaluate on Charades‑STA and QV‑Highlights, achieving modest performance when directly replacing text with frame embeddings. They introduce Nearest Entity Replacement (NER), a training‑free mapping that represents each video frame by the mean of its nearest text‑entity embeddings, which significantly improves results and narrows the modality gap.
By Divyanshu Mishra, Sabine Sternig, Rakshith Shetty, Erhan Gundogdu
arXiv:2602.18874v2 Announce Type: replace-cross
Abstract: Few-shot Chinese font generation aims to synthesize new characters in a target style using only a handful of reference images. Achieving accu...
By Jie Li, Suorong Yang, Jian Zhao, Furao Shen
arXiv:2611.04108v2 Announce Type: replace-cross
Abstract: Model merging combines multiple fine-tuned models without additional training, and arithmetic methods have emerged as a simple yet effective...
By Shiting Wang, Yingjie Zou, Ke Li
arXiv:2611.05062v2 Announce Type: replace-cross
Abstract: Pretrained video generation models distill internet-scale motion priors into robot policies, but full video is a redundant signal for action...
By Jeremy Collins, Namra Patel, Ayush Agarwal, Shitij Govil, Animesh Garg
arXiv:2611.06356v1 Announce Type: new
Abstract: Foundation models for electroencephalography (EEG) are pretrained on tens of thousands of hours of recordings, and their accuracy grows with that amoun...
By Kuber Shahi, Bruno Aristimunha, Arnaud Delorme
arXiv:2611.05592v1 Announce Type: cross
Abstract: Gated and ungated optimizations evaluated in GraphCast and Pangu-Weather. The first panel (upper-left) shows the mean over the 36 dates; remaining pa...
By P. Trent Vonich, Gregory J. Hakim
arXiv:2611.06789v1 Announce Type: cross
Abstract: A fast acceptance classifier could select revisions for a slower evaluator. We ask whether agreement on existing papers supports that use. We compare...
By Shaoliang Yang, Jun Wang
arXiv:2611.02102v3 Announce Type: replace
Abstract: Scaling computer-use agents from individual actions to complex software work requires experience that connects decisions to complete outcomes. We p...
By Chaoyun Zhang, Bingxuan Li, Pinxue Guo, Yangfan Gao, Zikun Cai, Tianci Xie, Hui Li, Yuqi Huang, Yifei Cao, Bo Mao, Tian Tang, Qichang Zheng, Tongtong Liu, Xiaoxing Wang, Jinxiu Liu, Lizhi Lin, Zhou Shao, Maosen Zhou, Miao Zheng, Shuo Zhang, Yangji He, Kepeng Lei, Ning Zhao, Wei Shen, Zhenjiang Jin, Xinyue Zhang, Peng Sun, Zhenhua Han, Rui Zheng, Hang Yan
arXiv:2611.03633v2 Announce Type: replace
Abstract: Muon has recently emerged as a promising optimizer for pretraining large language models, offering a speedup over AdamW in minimizing pretraining l...
By Catherine Li, Jingchu Gai, Andrej Risteski, Jacob Mitchell Springer, Aditi Raghunathan
arXiv:2611.06090v1 Announce Type: new
Abstract: This paper presents an overview of \emph{SciClaimEval}, a pilot task at NTCIR-19 on verifying scientific claims against the tables and figures of the p...
By Xanh Ho, Andr\'{e} Greiner-Petter, Florian Boudin, Yun-Ang Wu, Sunisth Kumar, Tian Cheng Xia, Hiba Akbi, Tim Schopf, Akiko Aizawa
arXiv:2611.06371v1 Announce Type: new
Abstract: Summarization with a fixed character length is important for tasks such as headline and article preview generation, where text must fit an exact space....
By Steffen Castle, Nils Feldhus, Christopher Ebert, Leonhard Hennig, Sebastian M\"oller
arXiv:2611.06517v1 Announce Type: new
Abstract: The massive permeability of intelligent systems in every sphere of human activities have greatly enhanced clarity, ease of work, and increased general...
By Etuh E., Eli. A. Jiya
arXiv:2611.05403v1 Announce Type: new
Abstract: In Bangladesh, much localized misinformation spreads as photocards: single-image news summaries that imitate the branding of well-known news organizati...
By Anindya Kundu, Kabya Mithun Saha, Md. Muhaiminul Islam, Fayek Ahmed, Aminul Kader Bulbul, Muhammad Ibrahim
arXiv:2611.05408v1 Announce Type: new
Abstract: Low-textured sketch images are widely used in scientific applications, including wildlife conservation, archaeological illustration, and forensic compo...
By Zhiqiang Wang, Sheri L. Barton, Jennifer H. Johnson, Yi He, Xingquan Zhu
arXiv:2611.05755v1 Announce Type: new
Abstract: Classifier-Free Guidance is a widely used inference-time guidance rule for conditional image generation with diffusion models, which exposes a guidance...
By Wuyang Zhang, Xiaolin Xu