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Robótica & RL

Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.

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EU AI Act Article 50 transparency rules enter force
Blog Robótica & RL

EU AI Act Article 50 transparency rules enter force

Article 50 of the EU AI Act has entered into force, setting transparency obligations for AI providers and deployers operating across the bloc. Enterprises running generative AI tools now have to comply with Article 50, which requires providers and deployers of certain AI systems to tell people when they’re interacting with a machine, and to […] The post EU AI Act Article 50 transparency rules enter force appeared first on AI News .

03.08.2026
Blog Robótica & RL

Cogent AI Team Releases VR-1: A Frontier Cyber Reasoning Model That Composes and Verifies Enterprise Attack Paths

Cogent AI team released Cogent VR-1, a reasoning model post-trained specifically for cybersecurity rather than picking up cyber capability as a side effect of general coding strength. It ships with two companions: IntrusionBench, a benchmark that scores agents on completed enterprise intrusions, and the Cogent AI Harness, a governed runtime for security agents. The launch […] The post Cogent AI Team Releases VR-1: A Frontier Cyber Reasoning Model That Composes and Verifies Enterprise Attack Pa...

03.08.2026
Blog Robótica & RL

Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning

arXiv:2607.28769v1 Announce Type: new Abstract: Security-critical biometric and forensic applications require accurate predictions and reliable confidence estimates, particularly under distribution shift. This challenge is especially acute for deepfake detection, where foundation-model-based detectors often exhibit overconfident predictions on out-of-distribution manipulations, which limits their suitability for operational deployment. We propose an uncertainty-aware deepfake detection framework...

03.08.2026
Blog Robótica & RL

Best Friends, Not Forever: Evaluating Long-Horizon Persona Collapse and Behavioral Drift in AI Companions

arXiv:2607.28818v1 Announce Type: new Abstract: As AI companions increasingly mediate repeated social interaction, users may rely on a stable role and shared history, yet locally acceptable replies do not ensure that either persists. We study two observable long-horizon failures: 'persona collapse', the loss of a deployed role, boundaries, values, or style, and 'behavioral drift', the gradual or recurrent erosion of those properties. We introduce ANCHOR, a controlled synthetic audit that separat...

03.08.2026
Blog LLMs & Texto

Can Zero-Shot LLMs Predict Child Malnutrition? A Fairness and Temporal Robustness Study

arXiv:2607.29082v1 Announce Type: new Abstract: Child malnutrition remains a major public health challenge in low- and middle-income countries, particularly in South Asia, where early identification of vulnerable children is critical for timely intervention and resource allocation. This study aims to evaluate the feasibility, fairness, and temporal robustness of using a pretrained large language model (LLM) in a zero-shot setting for child stunting prediction using population health survey data....

03.08.2026
Blog LLMs & Texto

TextCloak: Thwarting Unauthorized LLM Exploitation via RL-Driven Unlearnable Text

arXiv:2607.28862v1 Announce Type: new Abstract: The rapid development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously raising growing concerns about unauthorized data exploitation and privacy leakage. Unlearnable examples (UEs) offer a promising defense by introducing carefully designed perturbations into data such that models trained on them exhibit degraded utility. However, existing methods for text protection are pri...

03.08.2026
Blog Dados & Embeddings

Retrieval-Driven Training-Free AI-Generated Video Attribution

arXiv:2607.28955v1 Announce Type: new Abstract: AI-generated videos are becoming increasingly realistic and difficult to distinguish from authentic ones, which facilitates malicious misuse and poses growing threats to cybersecurity and social governance. Attributing AI-generated videos to their specific generative sources is therefore of critical importance for forensic investigation and legal regulation. However, most existing visual attribution methods focus on images and particularly rely on ...

03.08.2026
Blog Robótica & RL

Latent Lie-Poisson Neural Networks (LLPNNs): Discovering the motion of Lie-Poisson systems through observable data and latent dynamics

arXiv:2607.28939v1 Announce Type: new Abstract: Structure-preserving neural networks are essential for the long-term prediction of Hamiltonian systems from data. Many important Hamiltonian systems in mechanics and control admit symmetry reduction to Lie--Poisson systems, including rigid bodies, underwater vehicles, fluids, plasmas, and optimal control problems. A fundamental challenge in learning such systems is that their dynamics evolve in momentum variables that are typically unobservable, wh...

03.08.2026
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