Prépublication
· 2026 arXiv
EN
Ground-based cosmic microwave background (CMB) experiments filter their time-ordered data to remove $1/f$ noise and ground pickup, which removes or mixes the large angular scales where primordial $B$ modes peak. A common remedy is a pseudo-$C_\ell$ estimator w...
Giacomo Galloni
#astro-ph.CO
Prépublication
· 2026 arXiv
EN
A hypothetical origin as lunar ejecta for Earth's quasi-satellites (QS) has gained initial support in recent years, but there is scarce photometric and/or spectroscopic data to support the premise due to the rare observational windows to obtain such data for t...
Grigori Fedorets, Zuri Gray et al.
#astro-ph.EP
Prépublication
· 2026 arXiv
EN
Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models. Concept-based attribution methods interpret models using human-understandable concepts. Existing concept-based methods can only interpret classifi...
Bhanu Tokas, Rahul Nair et al.
#cs.CV
Prépublication
· 2026 arXiv
EN
Vision-Language Models (VLMs) increasingly analyze and reason about images, yet it remains unclear whether their internal representations faithfully encode the structured relations required for such reasoning. We introduce EICS (Evaluation-Invariant Concept St...
Shubh Garg, Chen Shani
#cs.CL
Prépublication
· 2026 arXiv
EN
While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an understudied degree of freedom. Several lines of research offer alternatives to the standard Me...
Danil Rudenko, Georgii Bulgakov et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
Neural semantic parsers generate meaning representations token by token, yet well-formedness depends on references, scope, and graph relations that unfold during generation. Constrained decoding prevents locally invalid tokens but does not guarantee that a val...
Xiao Zhang
#cs.CL
Prépublication
· 2026 arXiv
EN
As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior. Existing methods optimize semantic relevance but largely ignore tool availability during execution. To address this gap, we propose RATED (Reliability-Awar...
Weiliang Chen, Song Liao
#cs.AI
Prépublication
· 2026 arXiv
EN
Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact across services, unintended or harmful outcomes can arise when agents fulfill benign user requests. Proacti...
Yeonsung Jung, Zhiyuan Wang et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks. A saved memory or successful final action does not by itself establish tha...
Jingwei Song, Zhisheng Chen et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Recent advances in automatic pronunciation assessment (APA) have driven a shift from scripted to free-form assessment. However, existing methods often rely on multi-stage pipelines with forced alignment and separate feature extractors, limiting their practical...
Fu-An Chao, Bi-Cheng Yan et al.
#eess.AS
Prépublication
· 2026 arXiv
EN
In this report, 15 variants of nine parametric models for non-directional ocean wave spectra are reviewed. They are compared against observation-based spectra created by taking spectra measured by moored buoys, binning by wind speed, and averaging. Surface Sto...
W. Erick Rogers, Yalin Fan et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Legacy Fortran physics packages in atmospheric models such as CESM, WRF, and E3SM pose a barrier to GPU modernization, while manual porting requires accelerator-programming expertise. We present LAFT (LLM-Assisted Fortran Translation), an agentic framework for...
Iris Linck, John Dennis et al.
#physics.ao-ph