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Mohamed bin Zayed University of Artificial Intelligence
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A simple training framework boosts pixel-level tampering detection performance in VLMs by over 26%, showcasing the power of balanced sampling and late-injection strategies.
Mixed-authorship documents can be harder to detect than purely human or AI-generated texts, challenging existing assumptions about AI-text detection.
Uncover a model's "digital DNA" – its pretraining data mixture – from its outputs alone, even without access to the training data.
Claude Code's architecture reveals a surprising amount of complexity outside the core LLM loop, with most code dedicated to safety, context management, and extensibility.
