arXiv:2601.15498v1 Announce Type: new Abstract: Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification. While recent methods improve draft quality by tightly coupling the drafter with the target model, the verification mechanism itself remains largely unchanged, relying on strict token-level rejection sampling. In practice, modern LLMs frequently operate in low-margin regimes where the target model exhibits weak preference among top candidates. In such cases, rejecting plausible runner-up tokens yields negligible information gain […]

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Extending Text-to-Image (T2I) generation to Text-Guided Video Stylization (T2GVS) presents significant challenges in temporal consistency, style fidelity, and fine-grained control. Naive frame-by-frame T2I application results in severe flickering. We propose VideoStylist, a novel diffusion model extending a pre-trained T2I U-Net to a four-dimensional architecture for high-quality stylized video generation. Key innovations are Temporal Anchor Tokens (TATs) globally anchoring style semantics across frames, mitigating flickering, and an Adaptive Spatio-Temporal Consistency Module (ASTCM) to enhance local coherence and smooth transitions […]

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arXiv:2603.06618v1 Announce Type: new Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific […]

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Advances in wearable computing and AI have increased interest in leveraging PPG for health monitoring over the past decade. One of the biggest challenges in developing health algorithms based on such biosignals is the sparsity of clinical labels, which makes biosignals temporally distant from lab draws less reliable for supervision. To address this problem, we introduce a simple training strategy that learns a biomarker-specific decay of sample weight over the time gap between a segment and its ground […]

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arXiv:2602.05725v1 Announce Type: cross Abstract: Muon updates matrix parameters via the matrix sign of the gradient and has shown strong empirical gains, yet its dynamics and scaling behavior remain unclear in theory. We study Muon in a linear associative memory model with softmax retrieval and a hierarchical frequency spectrum over query-answer pairs, with and without label noise. In this setting, we show that Gradient Descent (GD) learns frequency components at highly imbalanced rates, leading to slow convergence bottlenecked […]

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arXiv:2507.04341v2 Announce Type: replace Abstract: While continuous diffusion models excel in modeling continuous distributions, their application to categorical data has been less effective. Recent work has shown that ratio-matching through score-entropy within a continuous-time discrete Markov chain (CTMC) framework serves as a competitive alternative to autoregressive models in language modeling. To enhance this framework, we first introduce three new theorems concerning the KL divergence between the data and learned distribution. Our results serve as the discrete counterpart to […]

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Table of Links Abstract and 1. Introduction Related Works 2.1 Traditional Index Selection Approaches 2.2 RL-based Index Selection Approaches Index Selection Problem Methodology 4.1 Formulation of the DRL Problem 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection System Framework of IA2 5.1 Preprocessing Phase 5.2 RL Training and Application Phase Experiments 6.1 Experimental Setting 6.2 Experimental Results 6.3 End-to-End Performance Comparison 6.4 Key Insights Conclusion and Future Work, and References 6.3 End-to-End Performance Comparison reveals IA2’s distinctive […]

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arXiv:2601.08643v1 Announce Type: cross Abstract: In this paper, we extend the Riesz representation framework to causal inference under sample selection, where both treatment assignment and outcome observability are non-random. Formulating the problem in terms of a Riesz representer enables stable estimation and a transparent decomposition of omitted variable bias into three interpretable components: a data-identified scale factor, outcome confounding strength, and selection confounding strength. For estimation, we employ the ForestRiesz estimator, which accounts for selective outcome observability while […]

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[Crossposted on lesswrong, see here for prior posts] The following statements seem to be both important for AI safety and are not widely agreed upon. These are my opinions, not those of my employer or colleagues. As is true for anything involving AI, there is significant uncertainty about everything written below. However, for readability, I present these points in their strongest form, without hedges and caveats. That said, it is essential not to be dogmatic, and I am […]

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A busy week. I finished my Aerial Robotics course! The next in the Specialization is Computational Motion Planning, which I am more excited about – mainly because the curriculum goes more towards my areas of expertise. Aerial Robotics was challenging primarily because I was doing a lot of physics/calculus which I had not attempted since a long time. Onto the articles for this week: Colab is now public! Google made Colaboratory, a previously-internal tool public. ‘Colab’ is a […]

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