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Ditch the complex diffusion sampling methods: a simple Adam optimizer can give you state-of-the-art image generation and restoration results.
Ditch the anchors and NMS: AutoReg3D reimagines 3D object detection as a sequence generation problem, opening the door for language-model techniques in 3D perception.
Forget expensive human annotations or hallucination-prone LLM-generated data: rule-generated synthetic data can teach LLMs to compose knowledge and significantly boost multi-hop reasoning on real-world tasks.
By pausing to "think" with latent diffusion, STAR-LDM achieves superior language understanding, narrative coherence, and controllable generation compared to standard autoregressive models of similar size.
