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Scale-invariance and characteristic length scale for the large-scale vortices in geostrophic convective turbulence with friction
arXiv:2606.02940v1 Announce Type: new Abstract: In geostrophic convective turbulence, large-scale vortices (LSVs) emerge through upscale energy transfer and are commonly regulated by large-scale friction. Yet the role of friction in setting the LSV size remains poorly understood. Here we perform direct numerical simulations of rotating Rayleigh-Benard convection with a linear friction term $\alpha\mathbf{u}$. Contrary to the classical prediction $L_\alpha\sim\alpha^{-3/2}$ obtained from the...
Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design
arXiv:2602.10016v3 Announce Type: replace Abstract: Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-scale recommendation systems. While such laws are established for large language models, they remain challenging for recommendation systems, especially those processing both user history and context features. We identify poor scaling efficiency as the main barrier...
AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning
Announce Type: replace Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery. Accordingly, agricultural multimodal reasoning demands robust cross-scale spatial understanding. However, due to the lack of multi-view agricultural benchmark datasets, existing multimodal large language models (MLLMs) exhibit...
UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling
Announce Type: new Abstract: In real-world deployments of large language models (LLMs), balancing inference quality and computational cost has become a central challenge. Existing approaches tackle this trade-off along two largely independent dimensions: model routing, which switches among models of different scales to match request complexity, and test-time scaling (TTS), which adjusts inference-time compute within a fixed model for fine-grained control. However, this decoupled design...
StoryVideoQA: Scaling Deep Video Understanding with a Large-Scale, Multi-Genre and Auto-Generated Dataset
arXiv:2606.06338v1 Announce Type: new Abstract: Video question answering (VideoQA) aims to answer questions about given videos. While existing approaches excel on factoid VideoQA, they struggle with deep video understanding (DVU), which requires the comprehension of complex storylines. This challenge arises from the inherent long-range video content, multi-faceted question types, and instance-level story elements, all of which constrain the scale and diversity of manually constructed DVU...
Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction
Announce Type: new Abstract: Molecular systems involve interactions across multiple spatial scales, from local coordination and short-range perturbations to long-range electrostatic and solvent-mediated effects. However, most molecular representation learning methods rely on manually predefined scales, and the task-optimal modeling scale may not coincide with these fixed levels. This study introduces a loss-guided adaptive scale refinement framework for molecular force prediction, treating...
Generalizing Multi-Scale Time-Series Modeling with a Single Operator
arXiv:2605.31129v1 Announce Type: new Abstract: Multi-scale modeling has emerged as an effective design principle for time-series forecasting by capturing temporal dynamics at multiple resolutions. As no principled foundation has been established in the literature, we unify existing scaling methods into a scaling operator family, revealing a fundamental limitation of existing approaches: reliance on fixed and discrete scaling. To address this limitation, we propose SiGMA (Single Generalized...
Exploring the Scale and Diversity of Speech Anti-spoofing Datasets: Experiments and Analysis
arXiv:2606.08038v1 Announce Type: new Abstract: The scale of speech anti-spoofing datasets has grown exponentially over the past decade, driven by the assumption that larger data leads to better performance. However, it remains unclear whether indiscriminate scaling commensurately improves model generalization. This study challenges the "scale-first" paradigm by decoupling the impacts of training data scale versus diversity.
SigmaScale: LLM Compression with SVD-based Low-Rank Decomposition and Learned Scaling Matrices
Announce Type: new Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices $S$ to aid truncated Singular Value Decomposition (SVD) based Large Language Model (LLM) compression. Instead of deriving scaling matrices analytically, SigmaScale optimizes two sets of vectors that define diagonal row and column scaling transformations under an activation-aware compression loss. We show that learned scaling lowers the effective intrinsic rank of weight matrices, as reflected...
Breaking the width-scaling limit in high-performance atomically thin 2D nanoribbon transistors
arXiv:2606.04219v1 Announce Type: cross Abstract: State-of-the-art transistors have been successfully scaled the gate lengths and channel thicknesses down to 5 nm for high-performance and energy-efficient information processing. However, reducing channel width below 40-50 nm remains a bottleneck, as dangling bonds, edge disorder, and lateral depletion suppress drive current and degrade device performance. Here, we break this width-scaling wall using ultra-scaled two-dimensional semiconductor...