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Topology-preserving 3D generation now achieves 98% fewer tokens and 58.5% faster inference by encoding shapes as overlapping slice latents instead of expensive voxels.
Models that appear equally accurate can possess vastly different agentic reasoning capabilities, revealing the hidden complexities of LLM performance.
Task learnability can significantly enhance RL efficiency in LLMs, leading to better performance with less data.
TopoAgent's innovative use of dynamic graph evolution allows for noise-resistant scientific reasoning that outperforms traditional linear models.
Training video generation models to explicitly infer latent physical properties yields more physically plausible videos than simply scaling data and model size.
Text-to-3D generation gets a semantic upgrade: DreamPartGen creates 3D objects with parts that not only look right but also understand their relationships and align with textual descriptions.
