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Murdered charity worker left behind ‘rich legacy of love’, heartbroken father says
Murdered charity worker left behind ‘rich legacy of love’, heartbroken father says Ms Rook was the co-founder of the charity MamaSuze, which she ran to help refugee and migrant women - Bookmark A former judge and father of murdered charity worker Annabel Rook said she should be remembered for her “rich legacy of love and support for the vulnerable”, after her abusive partner was found guilty of stabbing her to death. Murdered charity worker Annabel Rook should be remembered for her “rich...
TextFake: Benchmarking AI-Generated Image Detection on Text-Rich Images
arXiv:2606.01050v1 Announce Type: new Abstract: Recent AI-generated image (AIGI) detectors perform well on natural-image benchmarks, but their behavior on text-rich forgeries, such as fabricated screenshots, documents, and news pages prevalent in misinformation, remains untested. We introduce TextFake, a 20,000-image benchmark for text-rich AIGI detection spanning 28 languages, 4 topic categories, and 2 scene modalities.
Space station dust maps slash climate uncertainty over iron-rich particles
Space station dust maps slash climate uncertainty over iron-rich particles Lisa Lock Scientific Editor Robert Egan Associate Editor New research from a team of scientists led by Cornell is transforming how researchers understand one of the atmosphere's most abundant and least understood constituents: mineral dust. Mineral dust, composed of tiny particles lifted from arid regions including the Sahara, Middle East and East Asia, plays a complex role in Earth's climate system. These particles...
FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation
Announce Type: new Abstract: Force signals provide critical interaction cues for contact-rich robotic manipulation. However, existing methods mostly use force as an additional observation modality, without fully exploiting its role in modeling future interaction dynamics or guiding execution-time feedback correction.
TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation
arXiv:2606.09337v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a powerful framework for robotic manipulation, and recent studies have introduced tactile or force feedback into VLAs to address contact-rich tasks. However, these models are typically deployed as offline policies. When contact conditions shift from the training distribution, the policy cannot perform online adaptation, leading to problems such as inappropriate contact forces and inefficient retries.
Label tree semantic losses for rich multi-class medical image segmentation
arXiv:2507.15777v4 Announce Type: replace Abstract: Rich and accurate medical image segmentation is poised to underpin the next generation of AI-defined clinical practice by delineating critical anatomy for pre-operative planning, guiding real-time intra-operative navigation, and supporting precise post-operative assessment. However, commonly used learning methods for medical and surgical imaging segmentation tasks penalise all errors equivalently and thus fail to exploit any inter-class...
Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
arXiv:2606.08737v1 Announce Type: new Abstract: World action models inherit the predictive capability of world models, enabling action generation to be guided by anticipated future observations. However, they rely primarily on vision and often fail in contact-rich manipulation, where critical cues arise from physical interaction. In this paper, we propose Dream-Tac, a unified Tactile-World Action Model that jointly models actions, future visual observations, and tactile dynamics.
Multimodal Approaches for Visually-Rich Document Type Classification: A Comparative Analysis
arXiv:2606.02162v1 Announce Type: new Abstract: Document type classification in visually rich documents remains challenging, as relevant information is distributed across textual, visual, and layout modalities. To capture this complexity, current approaches rely on diverse multimodal modeling strategies, resulting in heterogeneous architectures that complicate systematic comparison. This variability is also reflected in existing comparative studies, which often rely on heterogeneous...
Reinforcement Learning from Rich Feedback with Distributional DAgger
Announce Type: new Abstract: Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward each with a single bit indicating whether the final answer is correct. Yet many settings provide rich feedback, including execution traces, tool outputs, expert corrections, and model self-evaluations. We study how to use such feedback through a distributional variant of the classic...
Reinforcement Learning from Rich Feedback with Distributional DAgger
arXiv:2606.05152v2 Announce Type: replace Abstract: Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward each with a single bit indicating whether the final answer is correct. Yet many settings provide rich feedback, including execution traces, tool outputs, expert corrections, and model self-evaluations. We study how to use such feedback through a distributional...