arXiv:2508. 05002v2 Announce Type: replace-cross Abstract: Existing unstructured data analytics systems rely on experts to write code and manage complex analysis workflows, making them both expensive and time-consuming.
By Ji Sun, Guoliang Li, Peiyao Zhou, Yihui Ma, Jingzhe Xu, Yuan Li
arXiv:2607. 15053v1 Announce Type: cross Abstract: The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!
By Mu Yuan, Jinke Song, Zhaomeng Zhou, Lan Zhang
arXiv:2607. 14989v1 Announce Type: cross Abstract: Large language models are increasingly evolving from text generators into general agents capable of understanding user requests, invoking external tools, and completing complex tasks through interaction.
By Chengyu Shen, Yujie Fu, Gangtao Xin, Yanheng Hou, Wenlong Fei, Guojie Zhu, Jiawei Li, Hongcheng Gao, Runming He, Zhen Hao Wong, Meiyi Qiang, Hao Liang, Zhao Cao, Hao Jiang, Chong Chen, Wentao Zhang
arXiv:2607. 14386v1 Announce Type: new Abstract: Data science tasks span from closed-ended information extraction to open-ended analysis, presenting significant challenges for automation.
By Maxime Heuillet, Sharadind Peddiraju
arXiv:2607. 14387v1 Announce Type: new Abstract: Validating autonomous driving systems requires diverse, regulation-compliant test scenarios.
By Yuan Gao, Wenting Miao, Mattia Piccinini, Haoyu Wang, Qunying Song, Johannes Betz
arXiv:2607. 14182v1 Announce Type: cross Abstract: Recent advances in humanoid robotics and reinforcement learning have enabled the acquisition of highly expressive whole-body motion policies.
By J. M. A. Marcelo, M. Brienza, E. Bugli, L. Comito, D. Nardi, D. D. Bloisi, V. Suriani
arXiv:2508. 00923v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to answer health-related questions and support healthcare workflows, yet evidence for their safety still relies heavily on static benchmarks that can rapidly become obsolete or be optimized against.
By Jiazhen Pan (Cherise), Bailiang Jian (Cherise), Paul Hager (Cherise), Yundi Zhang (Cherise), Che Liu (Cherise), Friederike Jungmann (Cherise), Hongwei Bran Li (Cherise), Julian Canisius (Cherise), Chenyu You (Cherise), Junde Wu (Cherise), Jiayuan Zhu (Cherise), Fenglin Liu (Cherise), Yuyuan Liu (Cherise), Niklas Bubeck (Cherise), Moritz Knolle (Cherise), Chen (Cherise), Chen (Cherise), Christian Wachinger, Zhenyu Gong, Cheng Ouyang, Georgios Kaissis, Benedikt Wiestler, Daniel Rueckert
arXiv:2607. 14110v1 Announce Type: cross Abstract: Human dialogue involves more than exchanging information; it also expresses beliefs, emotions, and subjective cognitive styles.
By Molood Arman, Cl\'ement Bonnafous
arXiv:2607. 14185v1 Announce Type: cross Abstract: Feedback-driven loops support iterative improvement in large language models, reinforcement learning, and autonomous discovery, yet their gains often diminish under repeated internal feedback.
By Xuening Wu, Shan Yu, Shenqin Yin
arXiv:2607. 15257v1 Announce Type: new Abstract: Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents.
By Yuyao Zhang, Junjie Gao, Zhengxian Wu, Jiaming Fan, Jin Zhang, Shihan Ma, Yao Yao, Weiran Qi, Chuyan Jin, Guiyu Ma, Xingzhong Xu, Kai Yang, Ji-Rong Wen, Zhicheng Dou
arXiv:2607. 14187v1 Announce Type: new Abstract: Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved.
By Haotian Liang, Mingkang Chen, Yufei Huang, Yuchun Guo, Xiaomeng Zhu, Xiangli Shi, Kaixuan Wang, Yunxuan Mao, Weijie Zhou, Ling Chen, Shirong Zeng, Yueyu Long, Yuchen Si, Yajuan Zhu, Xingyu Zhou, Minghui Wang, Wanjia He, Xin Yang, Lingzhu Xiang, Zhiqing Liu, Bohan Ma, Xiran Huang, Tianshuo Yang, Zhiheng Liu, Xuantang Xiong, Zisheng Lu, Ping Luo, Yao Mu, Han Hu, Zhengyou Zhang
arXiv:2607. 14285v1 Announce Type: cross Abstract: Safety alignment in LLMs aims to align models with human values, but which values take precedence when they conflict?
By Aryan Keluskar, Amrita Bhattacharjee, Huan Liu
arXiv:2607. 14721v1 Announce Type: cross Abstract: Cross-modal learning, i.
By Kunal Pratap Singh, Ali Garjani, Rishubh Singh, Muhammad Uzair Khattak, Efe Tarhan, Jason Toskov, Andrei Atanov, O\u{g}uzhan Fatih Kar, Amir Zamir
How to define recurring work, give AI the right context, explain what high-quality work looks like, and decide where human judgment is still needed. The post Prepare These 5 Assets Before Your AI Agents Take On More Work appeared first on Towards Data Science .
By Weiwei Hu
RoboCup has always been a scenario to develop systems that solve real-world problems. Driven by the main goal of playing against the 2050 FIFA World Cup champions, the RoboCup Soccer leagues need to constantly measure how the research community is progressing.
Cross-modal learning, i. e.
arXiv:2607. 13624v1 Announce Type: cross Abstract: Natural language interaction provides an intuitive way for non-expert users to communicate with robotic platforms.
By Jose Mart\'inez-Fajardo, Pablo Pueyo, Fernando Caballero, Luis Merino
arXiv:2607. 13716v1 Announce Type: new Abstract: Agentic AI systems increasingly act through heterogeneous runtimes: local coding hooks, SDK tools, browser automation, managed-agent traces, API gateways, and workflow engines.
By Zexun Wang
arXiv:2507. 19593v3 Announce Type: replace Abstract: Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs - assumptions that are often violated in real-world MAS characterized by uncertainty, misaligned perceptions, and nested beliefs.
By Vince Trencsenyi, Agnieszka Mensfelt, Kostas Stathis
arXiv:2510. 10002v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in sensitive everyday contexts -- offering personal advice, mental health support, and moral guidance -- understanding their behavior in navigating complex moral reasoning is essential.
By Pratik S. Sachdeva, Tom van Nuenen