Improving the performance of high-power electronics
By using a thin layer of diamond to manage excessive heat, researchers can boost the speed and energy-efficiency of next-generation wireless devices.
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LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization
arXiv:2603.02970v2 Announce Type: replace Abstract: We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and gradient-based trust region local refinement through an adaptive competition mechanism for smooth expensive-to-evaluate objective functions with available gradients. At each iteration, global and…
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
arXiv:2605.00369v4 Announce Type: replace Abstract: We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated by recent advances in LLM-based evolutionary search, such as AlphaEvolve, which demonstrates…
Model Recycling Framework for Multi-Source Data-Free Supervised Transfer Learning
arXiv:2508.02039v2 Announce Type: replace Abstract: Increasing concerns for data privacy and other difficulties associated with retrieving source data for model training have created the need for source-free transfer learning, in which one only has access to pre-trained models instead of…
RePo: Language Models with Context Re-Positioning
arXiv:2512.14391v3 Announce Type: replace Abstract: In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices. The rigid position information poses the full burden…
On the conditional equivalence of phase retrieval algorithms
arXiv:2606.07257v1 Announce Type: cross Abstract: Phase retrieval – recovering a complex-valued field from intensity measurements – is typically solved using variants of the Gerchberg-Saxton (GS) algorithm, understood as alternating projections between measurement planes. Meanwhile, modern computational imaging increasingly relies on…
Twin: Tuning Learning Rate and Weight Decay of Deep Homogeneous Classifiers without Validation
arXiv:2403.05532v2 Announce Type: replace Abstract: We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminating the need to hold out data and avoiding the two-step…
Skip a Layer or Loop It? Learning Program-of-Layers in LLMs
arXiv:2606.06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence of training-free, flexible, dynamic program-of-layers (PoLar), where pretrained layers can be packed…
Unmixing ATR-{mu}FTIR spectroscopic images of cross-sections of historical oil paintings
arXiv:2603.06673v2 Announce Type: replace-cross Abstract: Spectroscopic imaging (SI) has become central to heritage science because it enables non-invasive, spatially resolved characterisation of materials in artefacts. In particular, attenuated total reflection Fourier transform infrared microscopy (ATR-$mu$FTIR) is widely used to analyse…
Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression
arXiv:2606.06567v1 Announce Type: new Abstract: Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationships in a dataset. Despite recent advances in the field, a…