NeurIPS 2026 论文列表

Systematicity is the capacity to generalize compositionally. Existing benchmarks primarily test behavioral systematicity, while representation-level analyses often leave open whether recovered signals are merely decodable or causally used during generation. We therefore introduce $\mathrm{Bounded\mbox{-}RS}$, an evaluation artifact and protocol for adjudicating these possibilities under bounded hypotheses: unsupported, present-only, causal-use, or underdetermined. The artifact is a controlled semantic-parsing setting with matched recombination tests, exact intervention pairs, and evidence routines for recoverability, interchange, necessity, and repair during generation. We apply $\mathrm{Bounded\mbox{-}RS}$ to the two prominent representation-level explanations of systematic generalization, binding and geometry. Within the family instantiations evaluated here, and under the same behavioral anchor and state semantics, the binding family satisfies the criteria for $\mathit{Causal\mbox{-}use}$, whereas the geometry family is adjudicated as $\mathit{Unsupported}$ under its frozen closure basis. Detector-truthfulness tests do not validate a geometry detector, so the geometry conclusion remains a claim about the tested family rather than about all possible geometric mechanisms. $\mathrm{Bounded\mbox{-}RS}$ thus provides a reusable way to make disciplined representational claims from behavioral systematicity tests.

Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic sets while preserving training efficacy. However, existing studies mainly focus on image classification, leaving dense prediction tasks such as semantic segmentation largely underexplored. In this work, we identify three key challenges for segmentation DD: (*i*) long-tailed class imbalance, (*ii*) the need for strict pixel-wise alignment between images and dense labels, and (*iii*) the high computational cost of optimizing high-resolution data with complex models. To address these challenges, we propose $\mathbf{D^{3}S^{2}}$, a **D**iffusion-guided **D**ataset **D**istillation framework for **S**emantic **S**egmentation. Our method adopts a two-stage design. In **Class-Balanced Mask Selection**, we construct a representative mask set via a greedy strategy that prioritizes underrepresented classes. In **Diffusion-Guided Image Synthesis**, we employ a pretrained layout-to-image diffusion model to generate images conditioned on the selected masks, naturally ensuring spatial alignment. To further enhance the training utility of synthesized data, we introduce guided diffusion sampling with two complementary objectives: a segmentation-consistency loss for pixel-level alignment, and a class-wise feature matching loss for aligning per-class feature statistics across layers. Extensive experiments demonstrate the superiority of $D^{3}S^{2}$. Notably, at an extremely compression rate of 1\%, our method achieves 24.99\% and 35.49\% mIoU on ADE20K and COCO-Stuff with Mask2Former (Swin-S), outperforming random selection by 9.34\% and 5.70\%, respectively.