arXiv:2603.16946v1 Announce Type: cross Abstract: Currently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for automatic bin-width optimization of multidimensional histograms has been developed and recently validated on real inelastic neutron-scattering data. However, measuring beyond the equipment resolution leads to inefficient use of valuable beam time. To […]
Learning Permutation Distributions via Reflected Diffusion on Ranks
arXiv:2603.17353v1 Announce Type: cross Abstract: The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and discrete, non-Euclidean structure. Recent permutation diffusion methods define forward noising via shuffle-based random walks (e.g., riffle shuffles) and learn reverse transitions with Plackett-Luce (PL) […]
Alignment Makes Language Models Normative, Not Descriptive
arXiv:2603.17218v1 Announce Type: cross Abstract: Post-training alignment optimizes language models to match human preference signals, but this objective is not equivalent to modeling observed human behavior. We compare 120 base-aligned model pairs on more than 10,000 real human decisions in multi-round strategic games – bargaining, persuasion, negotiation, and repeated matrix games. In these settings, base […]
DANCE: Dynamic 3D CNN Pruning: Joint Frame, Channel, and Feature Adaptation for Energy Efficiency on the Edge
arXiv:2603.17275v1 Announce Type: cross Abstract: Modern convolutional neural networks (CNNs) are workhorses for video and image processing, but fail to adapt to the computational complexity of input samples in a dynamic manner to minimize energy consumption. In this research, we propose DANCE, a fine-grained, input-aware, dynamic pruning framework for 3D CNNs to maximize power efficiency […]
Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression
arXiv:2603.17408v1 Announce Type: cross Abstract: Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates. However, most approaches require training separate diffusion models for each target bitrate, resulting in substantial computational overhead and hindering practical deployment. Meanwhile, recent studies have shown that joint super-resolution can serve as an effective approach for enhancing […]
Machine intelligence supports the full chain of 2D dendrite synthesis
arXiv:2603.16959v1 Announce Type: cross Abstract: Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism […]
Implementation of tangent linear and adjoint models for neural networks based on a compiler library tool
arXiv:2603.16976v1 Announce Type: cross Abstract: This paper presents TorchNWP, a compilation library tool for the efficient coupling of artificial intelligence components and traditional numerical models. It aims to address the issues of poor cross-language compatibility, insufficient coupling flexibility, and low data transfer efficiency between operational numerical models developed in Fortran and Python-based deep learning frameworks. […]
Synchronized DNA sources for unconditionally secure cryptography
arXiv:2603.17149v1 Announce Type: cross Abstract: Secure communication is the cornerstone of modern infrastructures, yet achieving unconditional security -resistant to any computational attack- remains a fundamental challenge. The One-Time Pad (OTP), proven by Shannon to offer perfect secrecy, requires a shared random key as long as the message, used only once. However, distributing large keys over […]
OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation
arXiv:2603.17205v1 Announce Type: cross Abstract: Domain-specific finetuning is essential for dense retrievers, yet not all training pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that exploits this heterogeneity to improve both the effectiveness and efficiency of retrieval model adaptation. We first investigate static pruning (SP), which retains only high-similarity […]
KANtize: Exploring Low-bit Quantization of Kolmogorov-Arnold Networks for Efficient Inference
arXiv:2603.17230v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) have gained attention for their potential to outperform Multi-Layer Perceptrons (MLPs) in terms of parameter efficiency and interpretability. Unlike traditional MLPs, KANs use learnable non-linear activation functions, typically spline functions, expressed as linear combinations of basis splines (B-splines). B-spline coefficients serve as the model’s learnable parameters. However, […]
Beyond bouba/kiki: Multidimensional semantic signals are deeply woven into the fabric of natural language
arXiv:2603.17306v1 Announce Type: cross Abstract: A foundational assumption in linguistics holds that the relationship between a word’s sound and its meaning is arbitrary. Accumulating evidence from sound symbolism challenges this view, yet no study has systematically mapped the multidimensional semantic profile of every phonological unit within a language. Here we show that individual letter-phonemes in […]
Efficient Exploration at Scale
arXiv:2603.17378v1 Announce Type: cross Abstract: We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates reward and language models as choice data is received. The reward model is fit to the choice data, while the language model is updated by a variation […]