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LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability

arXiv:2605.31167v1 Announce Type: new Abstract: Assessing whether Large Language Models outputs are factually grounded, epistemically calibrated, and methodologically reproducible is a prerequisite for responsible AI deployment. Yet auditing LLMs remains inaccessible to non-technical practitioners: existing tools require programming expertise and non-trivial environment setup, and cloud-hosted platforms transmit evaluation data to external services, creating barriers for domain experts and...

arXiv CS 9d ago

Ethical Hyper-Velocity (EHV): A Hardware-Rooted Zero-Trust Runtime Enforcement Architecture for Agentic AI Systems

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The ways we contain Claude across products

Get the developer newsletter Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox. Twelve months ago, we'd have rejected out of hand the idea of granting Claude access sufficient to take down an internal Anthropic service.

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Apple's AI Can Now Change Your Passwords. What Could Possibly Go Wrong?

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