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Pulse · note.com

note.com · Oct 24

Muse Arrives in Japan! I Tried Out Meta's AI Agent: The Convenience and Fear I Felt with Instagram and Threads Integration. [Generative AI / AI Side Hustle / AI Illustration]

Good morning! This is Kinu.Thank you for coming to visit! 😊✨The evolution of AI agents has been incredible lately.I am just amazed by the daily progress.Today, since Meta's new AI agent 'Muse' became available in Japan on October 23,

Pulse · bankinfosecurity.com

bankinfosecurity.com · Oct 23

Why AI Labs, HR Giants Are Emerging as Cybersecurity Buyers

Workday's buy of C1.ai, Cohere's purchase of Aisle and OpenAI's reported talks with Trail of Bits highlight how AI providers and enterprise software companies are expanding into cybersecurity, creating new startup exit opportunities while raising questions about competition and partner independence.

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arXiv

Oct 22

A Unified Framework for Image-to-3D Part Generation via Variable Granularity

Controllable 3D part generation is pivotal for digital asset creation, yet existing methods lack the flexibility to accommodate diverse user workflows requiring varying levels of control. In this paper, we introduce FlexPart, a unified image-to-3D part generation framework that supports variable-granularity geometric conditions within a single model. By seamlessly integrating heterogeneous inputs, ranging from sparse points to dense bounding boxes and masks, via gated adaptive modulation, FlexPart unlocks a powerful cross-modal synergy. We also propose an asymmetric geometric guidance mechanism that leverages strong spatial priors to align representations across granularities, improving geometric consistency and facilitating inter-task synergy. Under mask conditioning on PartVerseXL, FlexPart reduces Part CD by 16.6% relative to TripoSG + SAM3D and by 10.5% relative to OmniPart, while maintaining competitive whole-mesh quality. The code can be found at https://github.com/Gucci233/FlexPart

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arXiv

Sep 24

ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the test distribution shifts. Prompt learning adapts only a small set of parameters while keeping the backbone frozen, yet many…

arXiv

Sep 24

Improving Molecular-Morphology Contrastive Pretraining using Deep-Learning-based Morphology Profiles

Recent advancements in image-based profiling techniques have enabled the collection of high-volume cell morphology data, allowing new molecular embedding models to learn from the experimental phenotypic perturbations of a molecule in a cell. Previously, we developed…

arXiv

Sep 24

Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language

Foundation models provide robots with the ability to interpret natural language and reason about environmental context, yet most language-conditioned policies assume that goals are well-specified and that task-relevant information is provided upfront via a prior map. Operating…

arXiv

Sep 24

Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation

Disinformation research has produced increasingly accurate automated fake-news detectors, but many systems remain difficult to interpret and are weakly connected to established theories of persuasion, credibility, and human judgment. In this paper, we develop a theory-informed…

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