arXiv:2404.09113v4 Announce Type: replace Abstract: Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models. In this paper, we propose a novel VI method that extends the naive mean field via entropic regularization, referred to as $Xi$-variational inference ($Xi$-VI). $Xi$-VI has a close connection to the entropic optimal transport problem and benefits from the computationally efficient Sinkhorn algorithm. We show that $Xi$-variational posteriors effectively recover the true posterior dependency, where the dependence […]

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arXiv:2602.06218v1 Announce Type: new Abstract: Vision-language models (VLMs) align images and text with remarkable success, yet the geometry of their shared embedding space remains poorly understood. To probe this geometry, we begin from the Iso-Energy Assumption, which exploits cross-modal redundancy: a concept that is truly shared should exhibit the same average energy across modalities. We operationalize this assumption with an Aligned Sparse Autoencoder (SAE) that encourages energy consistency during training while preserving reconstruction. We find that this inductive […]

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arXiv:2603.05551v1 Announce Type: new Abstract: Information-intensive Document Question Answering (DocQA) is often constrained by long contexts and information overload, which hinders Vision-Language Models (VLMs) from performing precise direct reasoning. Although multimodal GraphRAG has achieved preliminary breakthroughs, existing frameworks still face dual challenges: (1) the necessity of large-scale models for handling queries of diverse complexities and (2) the inherent reasoning bottlenecks of end-to-end VLMs. To address these issues, we propose AutoThinkRAG, a framework that enhances the understanding of complex […]

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arXiv:2602.12337v1 Announce Type: new Abstract: In this paper, we develop an asymptotic-preserving dynamical low-rank method for the multiscale linear kinetic transport equation. The proposed scheme is unconditionally stable in the diffusive regime while preserving the correct asymptotic behavior, and can achieve significant reductions in computational cost through a low-rank representation and large time step stability. A low-rank formulation consistent with the discrete energy is introduced under the discrete ordinates discretization, and energy stability of the resulting scheme is […]

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arXiv:2601.09805v1 Announce Type: new Abstract: Modern logical reasoning with LLMs primarily relies on employing complex interactive frameworks that decompose the reasoning process into subtasks solved through carefully designed prompts or requiring external resources (e.g., symbolic solvers) to exploit their strong logical structures. While interactive approaches introduce additional overhead, hybrid approaches depend on external components, which limit their scalability. A non-interactive, end-to-end framework enables reasoning to emerge within the model itself — improving generalization while preserving analyzability without any […]

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:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter X Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter X By Oscar Wilde ::: When his servant entered, he looked at him steadfastly and wondered if he had thought of peering behind the screen. The man was quite impassive […]

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arXiv:2602.18489v1 Announce Type: new Abstract: Personalized federated learning (PFL) creates client-specific models to handle data heterogeneity. Previously, PFL has been shown to be naturally resistant to backdoor attack propagation across clients. In this work, we reveal that PFL remains vulnerable to backdoor attacks through a novel frequency-domain approach. We propose DCInject, an adaptive frequency-domain backdoor attack for PFL, which removes portions of the zero-frequency (DC) component and replaces them with Gaussian-distributed samples in the frequency domain. Our attack […]

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This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectural blueprints. Instead of following fixed plans, construction tasks are defined through targets and obstacles, allowing the system to adapt more flexibly to environmental uncertainty and variations during the building process. A reinforcement learning (RL) policy, trained using deep Q-learning with successor features, serves as the decision-making component. As a proof of concept, we evaluate the approach on a benchmark of […]

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