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arXiv preprints from January 1, 2026 through September 16, 2026 — 11:50:24 EST

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Posted in stat.ME · 2026-01-20 · Marlena Bannick, Yuanyuan Bian, Gregory Chen, Liming Li, Yuhan Qian, Daniel Sabanés Bové, Dong Xi, Ting Ye, Yanyao Yi

The RobinCar Family: R Tools for Robust Covariate Adjustment in Randomized Clinical Trials

Purpose: Covariate adjustment is a powerful statistical technique that can increase efficiency in clinical trials. Recent guidance from the U.S. FDA provided recommendations and best practices for using covariate adjustment. However, there has existed a gap between the extensive statistical literature on covariate adjustment and...

💬 0 commentsarXiv:2601.14498v1PDF
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Posted in astro-ph.HE · 2026-01-20 · Noam Soker

The failed failed-supernova scenario of M31-2014-DS1

I examine a recently proposed failed-supernova scenario for the fading of the yellow supergiant event M31-2014-DS1, and find that it requires unlikely fine-tuned parameters to work, if at all. In the failed-supernova scenario, most of the yellow supergiant collapsed to form a black hole. Due to the energy carried by neutrinos from the...

💬 0 commentsarXiv:2601.14497v1PDF
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Posted in cond-mat.str-el · 2026-01-20 · Shi Feng, Nandini Trivedi

Magnetic field induced phenomena in Kitaev spin liquids

Quantum spin liquids (QSLs) host a variety of fractionalized particles. In Kitaev's paradigmatic honeycomb model a spin-$\tfrac{1}{2}$ fractionalizes into $Z_2$ flux due to emergent $Z_2$ gauge field and matter Majorana fermions. Although these excitations have well-defined dynamics in the integrable limit, their direct experimental...

💬 0 commentsarXiv:2601.14496v1PDF
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Posted in cs.LO · 2026-01-20 · Joshua Clune, Haniel Barbosa, Jeremy Avigad

Hint-Based SMT Proof Reconstruction

There are several paradigms for integrating interactive and automated theorem provers, combining the convenience of powerful automation with strong soundness guarantees. We introduce a new approach for reconstructing proofs found by SMT solvers which we intend to be complementary with existing techniques. Rather than verifying or...

💬 0 commentsarXiv:2601.14495v1PDF
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Posted in math.CO · 2026-01-20 · Mohamed Omar

New Perspectives On The Unimodality Of Domination Polynomials

The domination polynomial of a graph $G$ is given by $D(G,x)=\sum_{k=0}^{n} d_k(G)x^k$ where $d_k(G)$ records the number of $k$-element dominating sets in $G$. A conjecture of Alikhani and Peng asserts that these polynomials have unimodal coefficient sequences. We develop three complementary perspectives that strengthen existing tools...

💬 0 commentsarXiv:2601.14494v1PDF
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Posted in math.OC · 2026-01-20 · Kota Katsuki, Duckgyu Shin, Naoya Onizawa, Takahiro Hanyu

Fast Solving Complete 2000-Node Optimization Using Stochastic-Computing Simulated Annealing

In this paper, we evaluate stochastic-computing simulated annealing (SC-SA) for solving large-scale combinatorial optimization problems. SC-SA is designed using stochastic computing, where the computatoin is reazlied using random bitstream, resulting in fast converging to the global minimum energy of the problems. The proposed SC-SA...

💬 0 commentsarXiv:2603.20197v1PDF
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Posted in cond-mat.soft · 2026-01-20 · Matthieu Bourguignon, Gustavo Alberto Rosales-Sosa, Yoshinari Kato, Bruno Bresson, Hikaru Ikeda, Shingo Nakane, Gergely Molnár, Hiroki Yamazaki, Etienne Barthel

Fracture initiation in silicate glasses via a universal shear localization mechanism

Shear bands lie at the root of fracture initiation in bulk metallic glasses and amorphous polymers. For silicate glasses, in contrast, studies have largely emphasized permanent volumetric strain, commonly referred to as densification. Here we systematically investigate indentation-induced fracture in two distinct families of...

💬 0 commentsarXiv:2601.14493v1PDF
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Posted in cs.RO · 2026-01-20 · Malak Mansour, Ali Abouzeid, Zezhou Sun, Qinbo Sun, Dezhen Song, Abdalla Swikir

UNCLE-Grasp: Uncertainty-Aware Grasping of Leaf-Occluded Strawberries

Robotic strawberry harvesting remains challenging under partial occlusion, where leaf interference introduces significant geometric uncertainty and renders grasp decisions based on a single deterministic shape estimate unreliable. From a single partial observation, multiple incompatible 3D shape completions may be plausible, such that...

💬 0 commentsarXiv:2601.14492v2PDF
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Posted in cs.SC · 2026-01-20 · Baran Solmaz, Tulay Ayyildiz

Certified Real Eigenvalue Location

The location of real eigenvalues provides critical insights into the stability and resonance properties of physical systems. This paper presents a hybrid symbolic numeric approach for certified real eigenvalue localization. Our method combines Gershgorin disk analysis with Hermite matrix certification to compute certified intervals...

💬 0 commentsarXiv:2601.14491v1PDF
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Posted in cs.CV · 2026-01-20 · Hunter Heidenreich, Ben Elliott, Olivia Dinica, Yosheb Getachew

GutenOCR: A Grounded Vision-Language Front-End for Documents

GutenOCR is a family of grounded OCR front-ends obtained by fine-tuning Qwen2.5-VL-3B and Qwen2.5-VL-7B. The resulting single-checkpoint vision-language models expose reading, detection, and grounding through a unified, prompt-based interface. Trained on business documents, scientific articles, and synthetic grounding data, the models...

💬 0 commentsarXiv:2601.14490v2PDF
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Posted in econ.GN · 2026-01-20 · Marina Agranov, Federico Echenique, Kota Saito

I Choose For You: an Experimental Study

We investigate whether risk and time preferences differ when individuals make decisions for others compared to making decisions for themselves. We introduce a novel ``skin in the game'' experimental design, where choices for others incur a direct cost to the decision-maker, ensuring a genuine trade-off between self-interest and...

💬 0 commentsarXiv:2601.14489v1PDF
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Posted in cs.LG · 2026-01-20 · Krish Tadigotla

Translational Gaps in Graph Transformers for Longitudinal EHR Prediction: A Critical Appraisal of GT-BEHRT

Transformer-based models have improved predictive modeling on longitudinal electronic health records through large-scale self-supervised pretraining. However, most EHR transformer architectures treat each clinical encounter as an unordered collection of codes, which limits their ability to capture meaningful relationships within a...

💬 0 commentsarXiv:2603.13231v1PDF
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Posted in math.NA · 2026-01-20 · Moustapha Diallo, Zelalem Arega Worku

High-Order Symmetric Positive Interior Quadrature Rules on Two and Three Dimensional Domains

Fully symmetric positive interior (f-SPI) quadrature rules are key building blocks for high-order discretizations of partial differential equations, yet high-degree rules with few nodes remain scarce on reference elements commonly used in mesh generation. We construct new f-SPI rules on the square, cube, prism, and pyramid by coupling...

💬 0 commentsarXiv:2601.14488v1PDF
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Posted in cs.LG · 2026-01-20 · Mrigank Dhingra, Omer San

Stabilizing autoregressive forecasts in chaotic systems via multi-rate latent recurrence

Long-horizon autoregressive forecasting of chaotic dynamical systems remains challenging due to rapid error amplification and distribution shift: small one-step inaccuracies compound into physically inconsistent rollouts and collapse of large-scale statistics. We introduce MSR-HINE, a hierarchical implicit forecaster that augments...

💬 0 commentsarXiv:2601.14487v1PDF
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Posted in cs.AI · 2026-01-20 · Yuan Tian, Yi Mei, Mengjie Zhang

Scalable Knee-Point Guided Activity Group Selection in Multi-Tree Genetic Programming for Dynamic Multi-Mode Project Scheduling

The dynamic multi-mode resource-constrained project scheduling problem is a challenging scheduling problem that requires making decisions on both the execution order of activities and their corresponding execution modes. Genetic programming has been widely applied as a hyper-heuristic to evolve priority rules that guide the selection...

💬 0 commentsarXiv:2601.14485v1PDF
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Posted in astro-ph.CO · 2026-01-20 · N. Weaverdyck, M. Rodríguez-Monroy, J. Elvin-Poole, I. Sevilla-Noarbe, A. Porredon, S. Avila, S. Lee, W. Riquelme, M. Tabbutt, D. Huterer, J. Prat, J. De Vicente, J. Mena-Fernández, M. Crocce, C. Sánchez, G. M. Bernstein, E. Henning, R. Cawthon, A. J. Ross, T. M. C. Abbott, M. Aguena, S. S. Allam, O. Alves, F. Andrade-Oliveira, D. Bacon, K. Bechtol, E. Bertin, J. Blazek, S. Bocquet, D. Brooks, R. Camilleri, A. Carnero Rosell, J. Carretero, F. J. Castander, A. Choi, L. N. da Costa, M. E. da Silva Pereira, T. M. Davis, H. T. Diehl, C. Doux, A. Drlica-Wagner, T. Eifler, S. Everett, A. Evrard, B. Flaugher, J. García-Bellido, M. Gatti, E. Gaztañaga, G. Giannini, D. Gruen, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, B. Jain, T. Kacprzak, K. Kuehn, O. Lahav, J. L. Marshall, F. Menanteau, R. Miquel, J. J. Mohr, J. Muir, J. Myles, R. Nichol, R. L. C. Ogando, A. Palmese, M. Paterno, W. J. Percival, A. A. Plazas Malagón, R. Rosenfeld, E. Rykoff, S. Samuroff, E. Sanchez, D. Sanchez Cid, E. Sheldon, N. Sherman, M. Smith, M. Soares-Santos, E. Suchyta, M. E. C. Swanson, T. Gregory, D. Thomas, C. To, D. L. Tucker, V. Vikram, M. Yamamoto, B. Yanny

Dark Energy Survey Year 6 Results: MagLim++ Lens Sample Selection and Measurements of Galaxy Clustering

Galaxy clustering is a sensitive probe of the expansion history and growth of structure of the universe, and key degeneracies can be broken by combining these data with measurements of cosmic shear and galaxy-galaxy lensing (a so-called 3$\times$2pt analysis). The largest and least biased statistical samples of galaxies for use in...

💬 0 commentsarXiv:2601.14484v1PDF
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Posted in astro-ph.HE · 2026-01-20 · Andrew Mummery

Supermassive black hole mass inference with the optical flares of tidal disruption events

Tidal disruption events (TDEs) represent a truly unique, and potentially very powerful, probe of the quiescent supermassive black hole (SMBH) population. Given current observational survey capabilities the vast majority of the TDEs discovered in the next decade will be observed only across optical-UV wavelengths. A set of questions of...

💬 0 commentsarXiv:2601.14483v1PDF
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Posted in math.CO · 2026-01-20 · Daniela Egas Santander, Matteo Santoro, Jason P. Smith

Linear extensions and directed clique counts via modular partitions

Counting linear extensions is a fundamental problem in poset theory. It is known to be #P-complete, with polynomial-time formulas available in special cases. In this work, we develop new recursive formulas for counting linear extensions of posets whose modular partitions have particular structure. Specifically, we focus on posets...

💬 0 commentsarXiv:2601.14482v1PDF
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Posted in astro-ph.HE · 2026-01-20 · Ramandeep Gill, Jiang He, Jonathan Granot, Jian-Chao Sun, Shuang-Nan Zhang, Yuan-Hao Wang, Johannes Hulsman, Nicolas Produit, Shao-Lin Xiong

Prospects of Prompt Gamma-Ray Burst Polarimetry with POLAR-2

The dominant radiation mechanism that powers the prompt $γ$-ray emission in gamma-ray bursts (GRBs) remains poorly understood. High quality, time- and energy-resolved linear polarization measurements of prompt $γ$-ray photons can distinguish between synchrotron and inverse-Compton processes and provide crucial constraints on the...

💬 0 commentsarXiv:2601.14481v2PDF
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Posted in cs.NI · 2026-01-20 · Egemen Erbayat, Gustavo B. Figueiredo, Shih-Chun Lin, Motoharu Matsuura, Hiroshi Hasegawa, Suresh Subramaniam

A benchmarking framework for PON-based fronthaul network design

As mobile networks transition toward 5G and 6G RAN architectures, Passive Optical Networks (PONs) offer a critical solution for cost-effective fronthaul transport. However, the lack of standardized evaluation models in current literature makes an objective comparison of diverse optimization strategies difficult. This paper addresses...

💬 0 commentsarXiv:2601.14480v2PDF
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Posted in cs.CL · 2026-01-20 · Crish Nagarkar, Leonid Bogachev, Serge Sharoff

Can LLM Reasoning Be Trusted? A Comparative Study: Using Human Benchmarking on Statistical Tasks

This paper investigates the ability of large language models (LLMs) to solve statistical tasks, as well as their capacity to assess the quality of reasoning. While state-of-the-art LLMs have demonstrated remarkable performance in a range of NLP tasks, their competence in addressing even moderately complex statistical challenges is not...

💬 0 commentsarXiv:2601.14479v1PDF
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Posted in cs.CL · 2026-01-20 · Sasha Ronaghi, Emma-Louise Aveling, Maria Levis, Rachel Lauren Ross, Emily Alsentzer, Sara Singer

Large Language Models for Large-Scale, Rigorous Qualitative Analysis in Applied Health Services Research

Large language models (LLMs) show promise for improving the efficiency of qualitative analysis in large, multi-site health-services research. Yet methodological guidance for LLM integration into qualitative analysis and evidence of their impact on real-world research methods and outcomes remain limited. We developed a model- and...

💬 0 commentsarXiv:2601.14478v1PDF
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Posted in cs.CV · 2026-01-20 · Frank Bieder, Hendrik Königshof, Haohao Hu, Fabian Immel, Yinzhe Shen, Jan-Hendrik Pauls, Christoph Stiller

XD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data Generation

Until open-world foundation models match the performance of specialized approaches, deep learning systems remain dependent on task- and sensor-specific data availability. To bridge the gap between available datasets and deployment domains, domain adaptation strategies are widely used. In this work, we propose XD-MAP, a novel approach...

💬 0 commentsarXiv:2601.14477v2PDF
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Posted in cs.LG · 2026-01-20 · Naoya Onizawa, Takahiro Hanyu

GPU-accelerated simulated annealing based on p-bits with real-world device-variability modeling

Probabilistic computing using probabilistic bits (p-bits) presents an efficient alternative to traditional CMOS logic for complex problem-solving, including simulated annealing and machine learning. Realizing p-bits with emerging devices such as magnetic tunnel junctions (MTJs) introduces device variability, which was expected to...

💬 0 commentsarXiv:2601.14476v1PDF