arXiv:2206.02088v3 Announce Type: replace Abstract: Feature importance inference is critical for the interpretability and reliability of machine learning models. There has been increasing interest in developing model-agnostic approaches to interpret any predictive model, often in the form of feature occlusion or leave-one-covariate-out (LOCO) inference. Existing methods typically make limiting distributional assumptions, modeling assumptions, and require data splitting. In this work, we develop a novel, mostly model-agnostic, and distribution-free inference framework for feature importance in regression or classification tasks […]

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Training a single network with multiple objectives often leads to conflicting gradients that degrade shared representations, forcing them into a compromised state that is suboptimal for any single task–a problem we term latent representation collapse. We introduce Domain Expansion, a framework that prevents these conflicts by restructuring the latent space itself. Our framework uses a novel orthogonal pooling mechanism to construct a latent space where each objective is assigned to a mutually orthogonal subspace. We validate our approach […]

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arXiv:2511.15120v2 Announce Type: replace Abstract: In deep learning, a central issue is to understand how neural networks efficiently learn high-dimensional features. To this end, we explore the gradient descent learning of a general Gaussian Multi-index model $f(boldsymbol{x})=g(boldsymbol{U}boldsymbol{x})$ with hidden subspace $boldsymbol{U}in mathbb{R}^{rtimes d}$, which is the canonical setup to study representation learning. We prove that under generic non-degenerate assumptions on the link function, a standard two-layer neural network trained via layer-wise gradient descent can agnostically learn the target […]

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arXiv:2602.03907v1 Announce Type: new Abstract: While recent advances in neural representations and generative models have revolutionized 3D content creation, the field remains constrained by significant data processing bottlenecks. To address this, we introduce HY3D-Bench, an open-source ecosystem designed to establish a unified, high-quality foundation for 3D generation. Our contributions are threefold: (1) We curate a library of 250k high-fidelity 3D objects distilled from large-scale repositories, employing a rigorous pipeline to deliver training-ready artifacts, including watertight meshes and multi-view […]

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Recent progress in text-to-music generation has enabled high-quality audio synthesis from natural language prompts. However, such models are at risk of unintended replication, raising concerns regarding originality and intellectual property. While training-time mitigation strategies can address this issue, they typically require retraining or curated datasets, limiting their practicality for largescale systems. Inference-time methods provide a more lightweight alternative but often involve a trade-off between fidelity and memorization risk. This work introduces Repulsive Guidance (RG), a systematic inference-time mitigation […]

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arXiv:2601.23208v1 Announce Type: new Abstract: In the era of transformer models, masked self-supervised learning (SSL) has become a foundational training paradigm. A defining feature of masked SSL is that training aggregates predictions across many masking patterns, giving rise to a joint, matrix-valued predictor rather than a single vector-valued estimator. This object encodes how coordinates condition on one another and poses new analytical challenges. We develop a precise high-dimensional analysis of masked modeling objectives in the proportional regime where […]

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arXiv:2601.09765v1 Announce Type: new Abstract: Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use […]

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arXiv:2602.07992v1 Announce Type: cross Abstract: The emergence of compositional reasoning in large language models through reinforcement learning with verifiable rewards (RLVR) has been a key driver of recent empirical successes. Despite this progress, it remains unclear which compositional problems are learnable in this setting using outcome-level feedback alone. In this work, we theoretically study the learnability of compositional problems in autoregressive models under RLVR training. We identify a quantity that we call the task-advantage ratio, a joint property […]

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arXiv:2509.24544v3 Announce Type: replace Abstract: In this paper, we study the quantitative convergence of shallow neural networks trained via gradient descent to their associated Gaussian processes in the infinite-width limit. While previous work has established qualitative convergence under broad settings, precise, finite-width estimates remain limited, particularly during training. We provide explicit upper bounds on the quadratic Wasserstein distance between the network output and its Gaussian approximation at any training time $t ge 0$, demonstrating polynomial decay with network […]

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arXiv:2601.19903v1 Announce Type: new Abstract: Formal Verification (FV) relies on high-quality SystemVerilog Assertions (SVAs), but the manual writing process is slow and error-prone. Existing LLM-based approaches either generate assertions from scratch or ignore structural patterns in hardware designs and expert-crafted assertions. This paper presents STELLAR, the first framework that guides LLM-based SVA generation with structural similarity. STELLAR represents RTL blocks as AST structural fingerprints, retrieves structurally relevant (RTL, SVA) pairs from a knowledge base, and integrates them into […]

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