Frontier models that generate extended reasoning traces inadvertently produce token sequences that can facilitate model distillation. Recognizing this vulnerability, model owners may seek sampling strategies that limit the effectiveness of distillation without compromising model performance. Antidistillation sampling provides exactly this capability.

Can neural networks systematically capture discrete, compositional task structure despite their continuous, distributed nature? The impressive capabilities of largescale neural networks suggest that the answer to this question is yes. However, even for the most capable models, there are still frequent failure cases that raise doubts about their compositionality. Here, we seek to understand what it takes for a standard neural network to generalize over tasks that share compositional structure. We find that simply scaling data and model size leads to compositional generalization. We show that this holds across different task encodings as long as the training distribution sufficiently covers the task space. In line with this finding, we prove that standard multilayer perceptrons can approximate a general class of compositional task families to arbitrary precision using only a linear number of neurons with respect to the number of task modules. Finally, we uncover that if networks successfully compositionally generalize, the constituents of a task can be linearly decoded from their hidden activations. We show that this metric correlates with failures of text-to-image generation models to compose known concepts.

Environment Animals Wildlife'Two-headed snake' confuses predators More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The reed snake is only about eight inches long. Breakthroughs, discoveries, and DIY tips sent six days a week. Only around 600 of the nearly 4,000 known snake species are venomous. The recently discovered Guangxi reed snake () in China is not one of those species, but its alternative defense mechanism is strange enough to keep most predators at bay.

Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection? Existing methods either seek salient features according to statistics of different orders or learn to predict keypoints that are invariant to transformation. Nevertheless, the idea of incorporating shape reconstruction into 3D keypoint detection is under-explored. We argue that this is restricted by former problem formulations. To this end, a novel unsupervised paradigm named SNAKE is proposed, which is short for shape-aware neural 3D keypoint field. Similar to recent coordinate-based radiance or distance field, our network takes 3D coordinates as inputs and predicts implicit shape indicators and keypoint saliency simultaneously, thus naturally entangling 3D keypoint detection and shape reconstruction. We achieve superior performance on various public benchmarks, including standalone object datasets ModelNet40, KeypointNet, SMPL meshes and scene-level datasets 3DMatch and Redwood. Intrinsic shape awareness brings several advantages as follows.

Environment Animals Wildlife Endangered Species Longest snake ever measured is over 23.5 feet long Nicknamed the'Baroness,' this python is longer than two great white sharks. The Baroness may be as much as 10 percent longer than initially measured. Breakthroughs, discoveries, and DIY tips sent six days a week. A snake in southwest Indonesia has shattered the Guinness World Record for the longest serpent ever spotted in the wild. Nicknamed "Ibu Baron" (the Baroness), the giant female reticulated python () discovered in late 2025 measures 23-feet-and-8-inches from head to tail--about the same length as a regulation soccer goal.

Probabilistic modelling lies at the heart of machine learning.

While the ImageNet dataset has been driving computer vision research over the past decade, significant label noise and ambiguity have made top-1 accuracy an insufficient measure of further progress.

Weconsidertheproblem 9 Figure 5: V ofa SnAK Table 1: the multiplied 103.