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TBSM achieves state-of-the-art FID scores by transforming generative modeling through a novel energy-based interaction mechanism that enhances sample-level generation efficiency.
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.
A novel framework achieves unprecedented dataset distillation speed and accuracy by directly minimizing information loss, setting a new benchmark in the field.
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.
