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LLM watermarks can now survive fine-tuning, quantization, and distillation thanks to a new method that embeds them in a stable functional subspace.
By enforcing graph isomorphism across counterfactual inputs, UGID reveals that debiasing LLMs can be achieved by directly manipulating internal representations and attention mechanisms.
By unifying layout-to-image generation and image grounding with a novel cycle-consistent learning approach, EchoGen achieves state-of-the-art results in both tasks, proving that solving two problems at once can be better than solving them separately.
Autoregressive video generation gets a 1.8x speed boost and avoids temporal drift by denoising all blocks hierarchically at the same noise level.
Iteratively prompting a graph neural network at test time to amplify out-of-distribution signals dramatically improves OOD detection accuracy.
