Crowdsourced fact-checking systems have been adopted by major social media companies such as X, Meta, TikTok and Google with the aim of combating misleading information at scale without relying on centralized editorial control. These systems have been developed around a common underlying concept: a bridging mechanism that identifies notes flagging misleading information when they receive support from people with different perspectives rather than simple majority support.
Text driven hand object interaction (HOI) generation is gaining attention for immersive applications and robotics, yet producing physically plausible interactions remains challenging. Even when individual motions appear natural, small contact errors can cause conspicuous artifacts such as floating and interpenetration.
LLM agents are increasingly used to translate natural language into 3D scenes in a procedural way, but existing systems focus on static output. Dynamic 4D scenes from text alone, in which liquids flow, particles emit, rigid bodies cascade, and articulated mechanisms move, remain largely unexplored despite their value as editable content and as physics-grounded training data for video generation and embodied AI.
On-policy exploration is a crucial component for training robust Vision-Language Navigation agents, as it exposes the policy to a broader state distribution. However, such exploration inevitably leads to trajectories that deviate from expert demonstrations, resulting in a semantic mismatch between the executed visual stream and the original language instruction.
arXiv:2607. 00808v1 Announce Type: new Abstract: Pre-training on large-scale videos to improve reinforcement learning efficiency is promising yet remains challenging.
By Jinwen Wang, Youfang Lin, Xiaobo Hu, Shuo Wang, Kai Lv
arXiv:2603. 14354v3 Announce Type: replace-cross Abstract: End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledge transfer across diverse scenarios, and spurious correlations between unobservable confounders and true driving intents.
By Jiayuan Du, Yuebing Song, Yiming Zhao, Xianghui Pan, Jiawei Lian, Yuchu Lu, Liuyi Wang, Chengju Liu, Qijun Chen
arXiv:2607. 00442v1 Announce Type: cross Abstract: Reinforcement learning (RL) for quadruped locomotion commonly depends on fixed, hand-crafted, and Markovian reward functions that limit both interpretability of learned policies and lack explicit control over gait behaviors.
By Merve Atasever, Cagan Bakirci, Alfredo Reina Corona, Keyan Azbijari, Jyotirmoy V. Deshmukh
arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.
By Amirhosein Chahe, Tyler Naes, Jovin D'sa, Faizan M. Tariq, Sangjae Bae, Lifeng Zhou, David Isele
arXiv:2607. 00642v1 Announce Type: new Abstract: Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation models.
By Roberto Capobianco (Sony AI, Zurich, Switzerland), Harm van Seijen (Sony AI, North America, various locations), Nolan D. Bard (Sony AI, North America, various locations), Neil Burch (Sony AI, North America, various locations), Fatima Davelouis (Sony AI, North America, various locations), Josh Davidson (Sony AI, North America, various locations), Alisa Devlic (Sony AI, Zurich, Switzerland), Yunshu Du (Sony AI, North America, various locations), Ishan Durugkar (Sony AI, North America, various locations), Siddhant Gangapurwala (Sony AI, North America, various locations), Daniel Hernandez (Sony AI, North America, various locations), G. Zacharias Holland (Sony AI, North America, various locations), Sahil Jain (Sony AI, North America, various locations), Kenta Kawamoto (Sony AI, Tokyo, Japan), Raksha Kumaraswamy (Sony AI, North America, various locations), Patrick MacAlpine (Sony AI, North America, various locations), Dustin R. Morrill (Sony AI, North America, various locations), Declan Oller (Sony AI, North America, various locations), Francesco Riccio (Sony AI, Zurich, Switzerland), Akanksha Saran (Sony AI, North America, various locations), Craig Sherstan (Sony AI, Tokyo, Japan), Kaushik Subramanian (Sony AI, Zurich, Switzerland), Thomas J. Walsh (Sony AI, North America, various locations), Samuel Barrett (Sony AI, North America, various locations), Kizza N. Frisbee (Sony AI, North America, various locations), Mady Govil (Sony AI, North America, various locations), Johannes G\"unther (Sony AI, North America, various locations), Varun R. Kompella (Sony AI, North America, various locations), James A. MacGlashan (Sony AI, North America, various locations), Maxwell Svetlik (Sony AI, North America, various locations), Michael D. Thomure (Sony AI, North America, various locations), Jaden B. Travnik (Sony AI, North America, various locations), Kevin Waugh (Sony AI, North America, various locations), Elahe Aghapour (Sony AI, North America, various locations), Florian Fuchs (Sony AI, Zurich, Switzerland), Andreanne Lemay (Sony AI, North America, various locations), Shruti Mishra (Sony AI, Zurich, Switzerland), Takuma Seno (Sony AI, Tokyo, Japan), Peter Stone (Sony AI, North America, various locations), Michael Spranger (Sony AI, Tokyo, Japan), Peter R. Wurman (Sony AI, North America, various locations)
arXiv:2607. 00155v1 Announce Type: new Abstract: We study runtime human oversight of an AI agent when private information runs in both directions: the human privately knows her reward function, while the AI privately knows the quality of the action it proposes.
By Yunjin Tong
arXiv:2603. 23405v2 Announce Type: replace-cross Abstract: Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms.
By Zixiang Jiang, Yulun Zhang, Rishi Veerapaneni, Jiaoyang Li
arXiv:2607. 00666v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models often fail to perform the same learned tasks under environmental shifts, such as changes in camera pose and shifts to a different but similar robot (e.
By Taewook Kang, Taeheon Kim, Donghyun Shin, Jonghyun Choi
arXiv:2607. 00457v1 Announce Type: new Abstract: Embodied agents operating in the real world require multi-scale reasoning and knowledge adaptation as conditions change.
By Jinwoo Jang, Daniel J. Rho, Sihyung Yoon, Hyunsuk Cho, Honguk Woo
arXiv:2607. 00025v1 Announce Type: cross Abstract: While deep learning models achieve state-of-the-art performance in complex tasks, they remain brittle when faced with new environments or sensory deprivation.
By Benquan Wang, Jingdao Chen
arXiv:2607. 01111v1 Announce Type: cross Abstract: Robot policies inevitably encounter failures when deployed in real environments.
By Haoran Hao, Shahram Najam Syed, Jeffrey Ichnowski, Jeff Schneider
arXiv:2607. 01025v1 Announce Type: cross Abstract: Radio-frequency (RF) sensing is a central modality for counter-unmanned-aerial-system (counter-UAS) defence because it exploits the control, telemetry, and video links between a drone and its operator.
By David Shulman
arXiv:2607. 00716v1 Announce Type: cross Abstract: Skeleton-based action recognition has achieved remarkable success by exploiting joint coordinates and their topological connections, yet prevailing methods overwhelmingly assume complete and clean skeleton inputs.
By Yingjie Dai, Tianyang Xu, Yanglin Deng, Xiao-Jun Wu, Josef Kittler
arXiv:2607. 00796v1 Announce Type: new Abstract: Visual Reinforcement Learning (VRL) has achieved considerable success in solving control tasks.
By Jinwen Wang, Youfang Lin, Xiaobo Hu, Qian Xu, Shuo Wang, Zhuo Chen, Kai Lv
arXiv:2607. 00514v1 Announce Type: cross Abstract: Automatic understanding of dynamic 4D point clouds, the 3D-point sequences captured over time by depth sensors and LiDAR, is central to robotics and embodied perception.
By Trung Thanh Nguyen, Hai Nguyen-Truong, Tu Vo, Hoang M. Truong, Tuan-Anh Vu
arXiv:2607. 00310v1 Announce Type: cross Abstract: Foundation video diffusion models are increasingly viewed as world simulators for embodied agents, yet their pretraining on internet-scale generic video leaves them poorly aligned with real-world deployment domains.
By Amirreza Rouhi, Rajat Aggarwal, Parikshit Sakurikar, Anoop M. Namboodiri, Sashi P. Reddi