The paper presents a vision‑language navigation system that transfers from simulation to a real Ackermann‑steered mobile robot without relying on navigation graphs or panoramic views. It uses a Cross‑Modal Attention architecture trained on simulated data and fine‑tuned with limited real‑world episodes, leveraging linear photometric adjustments and a camera‑LiDAR sensor suite. Evaluation with SPL and nDTW metrics shows robust, adaptable navigation in continuous environments.
By Chalindu Abeywansa, Sahan Gunasekara, Devindi De Silva, Seniru Dissanayake, Ranga Rodrigo, Peshala Jayasekara
OTel is an open telecom AI resource that provides derived datasets for retrieval, reranking, instruction tuning, and safety/abstention, along with 30 full‑parameter post‑trained baselines covering 10 embedding models, 3 rerankers, and 17 language models. The project has seen significant community engagement, with over 16 million model downloads and more than 157 media mentions by May 2026. Post‑training on OTel data improves performance across all model families, achieving 93.1% NDCG@10 for embeddings, 0.947 MRR@10 for rerankers, and 87.8% correctness for language models.
By Farbod Tavakkoli, Gregory Diamos, Kenneth Church, David Kanter, Mark Austin, Imtiaz Karim, Mirza Masfiqur Rahman, Merouane Abdelkader Debbah, Zeinab Nezami, Ali Maatouk, Leandros Tassiulas, Rex Ying, Nick Sorros, Louis Powell, Nikolaos Vasiloglou, Ashish Vaswani, Somanshu Singla, Adarsh Chaluvaraju
OOPMAS introduces a training‑free framework that generates both the agent set and the coordination workflow at the granularity of individual queries. Agents are defined as object‑oriented class definitions with dedicated roles, tools, and persistent state, while workflows are expressed as executable main functions over these agent objects. A dynamic skill library accumulates structured lessons from execution feedback across optimization rounds, enabling in‑context improvement without any gradient updates or fine‑tuning, and achieves 89.6% accuracy on a mixed‑task benchmark, outperforming the strongest baseline by 18.1 percentage points.
By Qi Cheng, Shengyu Chen, Wei Cheng, Yiqun Xie, Xiaowei Jia, Haoyu Wang, Haifeng Chen
arXiv:2610.08312v1 Announce Type: new
Abstract: Continual learning (CL) is essential for Large Language Models (LLMs) to sequentially adapt to evolving tasks. To mitigate catastrophic forgetting, rec...
By Maoqi Liu, Quan Fang, Yufei He
arXiv:2610.08761v1 Announce Type: new
Abstract: Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges const...
By Zewei Zhou, Rachel Luo, Yulong Cao, Chaowei Xiao, Chensheng Peng, Boyi Li, Thomas Tian, Zheng Lian, Yan Wang, Jiaqi Ma, Boris Ivanovic, Marco Pavone, Wenhao Ding
arXiv:2610.06993v1 Announce Type: cross
Abstract: Evolution Strategies (ES) enable memory efficient full parameter fine-tuning of large language models (LLMs) using only forward computation. However,...
By Zhishen Sun, Hongzhan Wang, Sizhe Dang, Guang Dai, Haishan Ye
arXiv:2610.07384v1 Announce Type: cross
Abstract: Individual animal re-identification from camera-trap imagery is an instance retrieval problem central to non-invasive wildlife monitoring: a query im...
By Turhan Can Kargin, Piotr Kubaty, Ekaterina Rostovskaya, Izabela Wierzbowska, Bartosz Zieli\'nski, Marcin Przewi\k{e}\'zlikowski
arXiv:2610.07885v1 Announce Type: cross
Abstract: Electrocardiogram (ECG) delineation, the identification of waveform boundaries, is a foundational step that translates raw ECG signals into clinicall...
By Jeonghwa Lim, Minje Park, Yeongyeon Na, Yujin Eom, Soyeon Lim, Young Ho Lee, Yu Jeong Kim, Sunghoon Joo, Ki Hong Lee
arXiv:2610.07940v1 Announce Type: cross
Abstract: Looped language models apply the same stack of layers T times to each token, which deepens the model without adding parameters but multiplies its key...
By Yuhan Chen, Siyuan Zhang, Nan Wang, Feiyang Kang, Ruoxi Jia
arXiv:2610.08093v1 Announce Type: cross
Abstract: Developing reliable models for clinical tasks, such as Medical Question Answering (QA), is severely constrained by the limited availability of high-q...
By Chuan Li, Chengyu Wang, Cen Chen, Ye Lyu, Mingyuan Fan, Ming Gao
arXiv:2610.08400v1 Announce Type: cross
Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to ge...
By Kasper Helverskov Petersen, Rasmus Hannibal Tirsgaard, Fran\c{c}ois R J Cornet, Mikkel Jordahn, Mikkel N. Schmidt
The paper presents a new multi‑turn benchmark of 423 conversations with 1,661 labeled turn‑states to study when language models should refrain from answering. It shows that probes for unanswerability transfer well across datasets that share the same underlying signal, but fail to generalise to other forms of epistemic uncertainty. While a calibrated probe can identify underspecified turns more accurately than chance, it does not consistently improve overall generation quality compared to standard methods.
By Jerzy Kami\'nski, Ilya Galyukshev, Artem Kuznetsov, Danil Fedorov, Kirill Redko, Sergey Chuprin, Aidar Shumbalov, Stanislav Chumakov, Anna Kalyuzhnaya
arXiv:2610.08669v1 Announce Type: cross
Abstract: On-device learning is necessary when the model encounters user-,sensor-, or environment-specific shifts after deployment. Although parameter-efficien...
By Mehmet Emre Akbulut, Johannes Geier, Ulf Schlichtmann
arXiv:2507.07426v4 Announce Type: replace
Abstract: Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their...
By Zerui Yang, Yuwei Wan, Siyu Yan, Yudai Matsuda, Tong Xie, Linqi Song
arXiv:2610.04206v2 Announce Type: replace
Abstract: Spatial intelligence is a fundamental skill in multiple domains, such as Science, Technology, Engineering, and Mathematics (STEM), Medicine, Archit...
By Uttamasha Monjoree, Wei Yan
arXiv:2610.05828v2 Announce Type: replace
Abstract: Large language models (LLMs) offer new opportunities for public opinion research by enabling early prediction of survey responses, potentially redu...
By Dongryeol Lee, Weronika {\L}ajewska, Leonardo Perelli, Saab Mansour
arXiv:2503.22764v3 Announce Type: replace-cross
Abstract: The large language model (LLM) is typically integrated into the mainstream optimization protocol. However, it remains underexplored whether m...
By Mingyuan Zhang, Yue Bai, Huan Wang, Yizhou Wang, Qihua Dong, Yitian Zhang, Yun Fu
arXiv:2508.10020v2 Announce Type: replace-cross
Abstract: Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especiall...
By Chuan Li, Qianyi Zhao, Fengran Mo, Cen Chen
arXiv:2605.20740v2 Announce Type: replace-cross
Abstract: Large language models (LLMs) have emerged as flexible regressors capable of predicting real-valued quantities from heterogeneous inputs. Yet...
By Jungsoo Park, Hyungjoo Chae, Ethan Mendes, Jay DeYoung, Varsha Kishore, Wei Xu, Alan Ritter
arXiv:2607.02092v4 Announce Type: replace-cross
Abstract: Reinforcement learning can improve vision-language-action (VLA) policies beyond supervised fine-tuning, although this typically involves furt...
By Liuhaichen Yang, Zhuang Jiang, Chenchao Sheng, Ningwei Bai, Qichen Yin, Hanbo Ma, Junkai Liu, Junkai Sun, Dongcheng Lyu, Yi Dong, Zezhi Tang