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ParticleSplat is presented, a self-supervised object-centric learning method that decomposes scenes into a set of latent particles representing semantic entities through feedforward 3D Gaussian Splatting, and it is established that the learned 3D representation improves downstream performance on robotic manipulation tasks.
Context-Fractured Decomposition attacks expose a critical vulnerability in LLM agents, where harmless actions can lead to harmful outcomes through fragmented artifact interactions.
