The paper introduces the Residual Patch Adapter (RPA), a lightweight module that aligns EEG and MEG signals with all patch tokens from an intermediate layer of a Vision Transformer, rather than a single global embedding. Ablation studies show that retaining the full set of patch tokens is crucial for effective alignment, while the CLS token adds little value. Using RPA, the authors achieve state‑of‑the‑art Top‑1 accuracies on THINGS‑EEG2 and THINGS‑MEG datasets, and demonstrate that the approach generalizes to other brain encoders and can be swapped with various neural network backbones.
By Yuhui Jin, Yonghao Song, Bingchuan Liu
The paper investigates how to recover low‑dimensional latent geometry from a random geometric graph built from noisy, high‑dimensional data. It analyzes a spectral embedding algorithm on the Signal+Noise Graph Model, where vertices are perturbed points and edges are added when their inner product exceeds a threshold. In the regime where both the number of points and ambient dimension grow, the authors prove that, under a spectral gap condition, the leading eigenvectors and eigenvalues of the adjacency matrix can approximately recover the original point cloud up to an orthogonal transformation, and demonstrate the method on nested spheres and high‑dimensional sinusoid curves.
By Tatiana Brailovskaya, Nicholas A. Cook, Sofia Poinelli
RA-MoWE introduces workflow‑affinity embeddings to cluster queries and guide the creation of reusable expert workflows for large language models. Each embedding captures how well a set of reference workflows solves a query, revealing common reasoning strategies. The framework uses cluster embeddings to initialize and refine specialized workflows, and an encoder predicts embeddings from query text, enabling efficient expert selection without executing reference workflows.
By Qi Cheng, Shengyu Chen, Wei Cheng, Yiqun Xie, Haoyu Wang, Haifeng Chen, Xiaowei Jia
WorkflowOps is a multi‑agent workflow orchestration framework that learns collaboration priors from historical workflows. It uses a transition probability matrix to guide DAG construction, a sufficiency‑driven loop to create new agents on demand via an LLM, and a layered semantic matching strategy to reduce LLM calls. Experiments show that WorkflowOps improves end‑to‑end pass rates, especially on structured, decomposable tasks.
By Qi Cheng, Shengyu Chen, Wei Cheng, Zhengzhang Chen, Xiaowei Jia, Haoyu Wang, Haifeng Chen
arXiv:2610.08165v1 Announce Type: new
Abstract: The mechanical properties of a metallic alloy are set by its microstructure and texture: the size and shape of its grains and the orientation of their...
By Mahish K. Guru, Jan Bohlen, Louam Lemjid, Marius Tacke, Roland Aydin, Noomane Ben Khalifa
arXiv:2610.08214v1 Announce Type: new
Abstract: We report on an approach to teaching logic to mixed groups of computer science, mathematics, and philosophy students, based on the logico-pluralistic L...
By Christoph Benzm\"uller, David Fuenmayor, Luca Pasetto
arXiv:2610.07125v1 Announce Type: cross
Abstract: While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important co...
By Abhinav Sudhakar Dubey (University of California Santa Cruz), Scott Sirri (University of California Santa Cruz), Vaggos Chatziafratis (University of California Santa Cruz), C. Seshadhri (University of California Santa Cruz)
arXiv:2610.07706v1 Announce Type: cross
Abstract: Autoregressive large language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and...
By Byeonghu Na, Donghyeok Shin, Yeongmin Kim, Mina Kang, Il-Chul Moon
arXiv:2610.07764v1 Announce Type: cross
Abstract: Natural language processing (NLP) models can detect depression-related language in text written near the time symptoms are measured, but whether pret...
By Daniel Kua, Emrul Hasan, John-Jose Nunez, Frances Chen
arXiv:2610.07792v1 Announce Type: cross
Abstract: Large language model agents are increasingly deployed to perform complex tasks in real-world environments. However, the knowledge required for correc...
By Haizhong Zheng, Yizhuo Di, Ranajoy Sadhukhan, Shuowei Jin, Beidi Chen
arXiv:2610.07996v1 Announce Type: cross
Abstract: Tabular foundation models (TFMs) achieve strong predictive performance by conditioning on labeled demonstrations provided in context, without any par...
By Yacine Benihaddadene, Milan Bhan, Eliot Dugelay, Mohammed Jawhar, Benjamin Wong, Nicolas Chesneau, Duong Nguyen
arXiv:2610.08571v1 Announce Type: cross
Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a be...
By Niveen O. Jaffal, Ahmet Yuksel, David Mohaisen
arXiv:2511.21075v4 Announce Type: replace-cross
Abstract: Engineering LLMs to accelerate life sciences research requires a robust alignment with biomedical knowledge. We observe that biomedical text...
By Zhenchao Tang, Fang Wang, Haohuai He, Jiale Zhou, Tianxu Lv, Jun Zhu, Shouzhi Chen, Minghao Yang, Yu Wang, Jiayang Wu, Yidong Song, Yaokun Li, Jiehui Huang, Jun Zhou, Bing He, Jianhua Yao
arXiv:2610.01936v2 Announce Type: replace
Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by...
By Meghana Sunil, Shravya V, Shravan Venkatraman, Joe Dhanith PR
arXiv:2610.06855v1 Announce Type: new
Abstract: Scalable driver identification requires embedding models that maintain discriminative performance as fleet size grows, yet existing triplet-loss formul...
By Kyle Musgrove, Dylan B. Lewis, Sarah Powers, Emma J. Reid, Hector Santos-Villalobos
arXiv:2610.06974v1 Announce Type: new
Abstract: Knowledge graph embedding (KGE) methods represent entities and predicates in continuous vector spaces to infer missing knowledge. Despite strong benchm...
By Yuqicheng Zhu
arXiv:2610.07006v1 Announce Type: new
Abstract: Learning effective representations helps characterize the structure and dynamics of equity markets from financial data with a low signal-to-noise ratio...
By Yizhi Luo, Jiahe Yi, Jianhui Zhang, Shuo Sun
arXiv:2610.07540v1 Announce Type: new
Abstract: Robotic systems often exhibit unstable modes, along which small perturbations and disturbances can cause unbounded growth unless corrected through feed...
By Leonardo F. Toso, Yann LeCun, James Anderson, Oumayma Bounou
arXiv:2610.07834v1 Announce Type: new
Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster...
By Chao He, Jianyu Xu, Xinyi Guo, Ruiqi Liu, Haobin Ding, Ruiqi He, Dongqing Song
arXiv:2610.05518v1 Announce Type: cross
Abstract: The Fr\'echet distance is a de facto standard for evaluating generative models across domains, appearing as FID for images and FVD for videos. It sum...
By Yunghee Lee, Jaeyeon Kim