Non-log-concave sampling from an unnormalized density is fundamental in machine learning and statistics. As datasets grow larger, computational efficiency becomes increasingly important, particularly in reducing adaptive complexity, namely the number of sequential rounds required for sampling algorithms. In this work, we initiate the study of the adaptive complexity of non-log-concave sampling within the framework of relative Fisher information introduced by Balasubramanian et al. in 2022. To obtain a relative Fisher information of at most ε2 from the target distribution, we propose a novel algorithm that reduces the adaptive complexity from O(d2/ε4) to O(d/ε2) by leveraging parallelism. Furthermore, we show our algorithm is optimal for a specific regime of large ε. Our algorithm builds on a diagonally parallelized Picard iteration, while the lower bound is based on a reduction from the problem of finding stationary points.

Large-scale, high-quality data is essential for advancing the reasoning capabilities of large language models (LLMs). As publicly available Internet data becomes increasingly scarce, synthetic data has emerged as a crucial research direction. However, existing data synthesis methods often suffer from limited scalability, insufficient sample diversity, and a tendency to overfit to seed data, which constrains their practical utility.

Understanding perspective is fundamental to human visual perception, yet the extent to which multimodal large language models (MLLMs) internalize perspective geometry remains unclear. We introduce MMPerspective, the first benchmark specifically designed to systematically evaluate MLLMs' understanding of perspective through 10 carefully crafted tasks across three complementary dimensions: Perspective Perception, Reasoning, and Robustness.

Hand-Object Interaction (HOI) generation has significant application potential. However, current 3DHOI motion generation approaches heavily rely on predefined 3D object models and lab-captured motion data, limiting generalization capabilities.

Effective denoising is critical for managing complex visual inputs contaminated with noisy distractors in model-based reinforcement learning (RL). Current methods often oversimplify the decomposition of observations by neglecting the conditional dependence between task-relevant and task-irrelevant components given an observation. To address this limitation, we introduce CsDreamer, a modelbased RL approach built upon the world model of Collider-structure Recurrent State-Space Model (CsRSSM). CsRSSM incorporates colliders to comprehensively model the denoising inference process and explicitly capture the conditional dependence. Furthermore, it employs a decoupling regularization to balance the influence of this conditional dependence. By accurately inferring a task-relevant state space, CsDreamer improves learning efficiency during rollouts. Experimental results demonstrate the effectiveness of CsRSSM in extracting task-relevant information, leading to CsDreamer outperforming existing approaches in environments characterized by complex noise interference.

Transformers have demonstrated impressive in-context learning (ICL) capabilities, raising the question of whether they can serve as metalearners that adapt to new tasks using only a small number of in-context examples, without any further training. While recent theoretical work has studied transformers' ability to perform ICL, most of these analyses do not address the formal metalearning setting, where the objective is to solve a collection of related tasks more efficiently than would be possible by solving each task individually. In this paper, we provide the first theoretical analysis showing that a simplified transformer architecture trained via gradient descent can act as a near-optimal metalearner in a linear classification setting. We consider a natural family of tasks where each task corresponds to a class-conditional Gaussian mixture model, with the mean vectors lying in a shared k-dimensional subspace of Rd. After training on a sufficient number of such tasks, we show that the transformer can generalize to a new task using only eO(k/eR4) in-context examples, where eR denotes the signal strength at test time. This performance (almost) matches that of an optimal learner that knows exactly the shared subspace and significantly outperforms any learner that only has access to the in-context data, which requires Ω(d/eR4) examples to generalize. Importantly, our bounds on the number of training tasks and examples per task needed to achieve this result are independent of the ambient dimension d.

Neural Combinatorial Optimization (NCO) has emerged as a promising learningbased paradigm for addressing Vehicle Routing Problems (VRPs) by minimizing the need for extensive manual engineering. While existing NCO methods, trained on small-scale instances (e.g., 100 nodes), have demonstrated considerable success on problems of similar scale, their performance significantly degrades when applied to large-scale scenarios. This degradation arises from the distributional shift between training and testing data, rendering policies learned on small instances ineffective for larger problems. To overcome this limitation, we introduce a novel learning framework driven by Large Language Models (LLMs). This framework learns a projection between the training and testing distributions, which is then deployed to enhance the scalability of the NCO model. Notably, unlike prevailing techniques that necessitate joint training with the neural network, our approach operates exclusively during the inference phase, obviating the need for model retraining. Extensive experiments demonstrate that our method enables a backbone model (trained on 100-node instances) to achieve superior performance on large-scale Traveling Salesman Problems (TSPs) and Capacitated Vehicle Routing Problems (CVRPs) with up to 100K nodes from diverse distributions.

This paper proposes a new algorithm for learning gradient boosted decision trees while ensuring the existence of recourse actions. Algorithmic recourse aims to provide a recourse action for altering the undesired prediction result given by a model. While existing studies often focus on extracting valid and executable actions from a given learned model, such reasonable actions do not always exist for models optimized solely for predictive accuracy. To address this issue, recent studies proposed a framework for learning a model while guaranteeing the existence of reasonable actions with high probability. However, these methods can not be applied to gradient boosted decision trees, which are renowned as one of the most popular models for tabular datasets. We propose an efficient gradient boosting algorithm that takes recourse guarantee into account, while maintaining the same time complexity as the standard ones. We also propose a post-processing method for refining a learned model under the constraint of a recourse guarantee and provide a PAC-style analysis of the refined model. Experimental results demonstrated that our method successfully provided reasonable actions to more instances than the baselines without significantly degrading accuracy and computational efficiency.

Graph Neural Networks learn on graph-structured data by iteratively aggregating local neighborhood information. While this local message passing paradigm imparts a powerful inductive bias and exploits graph sparsity, it also yields three key challenges: (i) oversquashing of long-range information, (ii) oversmoothing of node representations, and (iii) limited expressive power. In this work we inject randomized global embeddings of node features, which we term Sketched Random Features, into standard GNNs, enabling them to efficiently capture long-range dependencies. The embeddings are unique, distance-sensitive, and topology-agnostic--properties which we analytically and empirically show alleviate the aforementioned limitations when injected into GNNs. Experimental results on real-world graph learning tasks confirm that this strategy consistently improves performance over baseline GNNs, offering both a standalone solution and a complementary enhancement to existing techniques such as graph positional encodings.

Recent advances in multimodal models have raised questions about whether visionand-language models (VLMs) integrate cross-modal information in ways that reflect human cognition. One well-studied test case in this domain is the boubakiki effect, where humans reliably associate pseudowords like'bouba' with round shapes and'kiki' with jagged ones. Given the mixed evidence found in prior studies for this effect in VLMs, we present a comprehensive re-evaluation focused on two variants of CLIP, ResNet and Vision Transformer (ViT), given their centrality in many state-of-the-art VLMs. We apply two complementary methods closely modelled after human experiments: a prompt-based evaluation that uses probabilities as a measure of model preference, and we use Grad-CAM as a novel approach to interpret visual attention in shape-word matching tasks. Our findings show that these model variants do not consistently exhibit the bouba-kiki effect. While ResNet shows a preference for round shapes, overall performance across both model variants lacks the expected associations. Moreover, direct comparison with prior human data on the same task shows that the models' responses fall markedly short of the robust, modality-integrated behaviour characteristic of human cognition. These results contribute to the ongoing debate about the extent to which VLMs truly understand cross-modal concepts, highlighting limitations in their internal representations and alignment with human intuitions.