AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single reward signal captures the full range of human preference. In this work, we develop a simple and effective post-training recipe for open-domain text-to-image gen...
One-Forcing is proposed, a simple yet effective approach that augments the DMD objective with an auxiliary GAN loss for high-quality and efficient one-step video generation, and finds that framewise autoregression stabilizes adversarial training, enabling higher-quality generation with substantially fewer training iter...
SlackDrive is proposed, a pre-inference compute allocator that reuses realized latency to select the compute budget of each control step before model execution, complementing existing profiling and resource scheduling while preserving the driving backbone and its compute actuator.
Improvements are shown that ReCAST yields improvements that generalize beyond the training rewards and support its core principle: assigning each reward greater weight at the denoising timesteps where its feedback is most informative.
This work replaces the static critic with a feed-forward 4D reconstruction reward that explicitly models scene dynamics, allowing coherent motion to receive high consistency rewards, and adds a motion prior that rewards natural scene-flow magnitude while penalizing jitter and non-rigid artifacts.
Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models. Existing faithfulness benchmarks, however, rely on simple atomic instructions, on which top-tier systems already achieve near-perfect scores. As T2I models enter cre...
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