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arXiv preprints from January 1, 2026 through September 8, 2026 — 03:39:07 EST

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Posted in cs.CV · 2026-08-26 · Riga Wu, Walter Witschey, Yicheng Li, Felix Barajas Ordonez, Keno K. Bressem, Lisa C. Adams, Gary E. Weissman, Li Shen, Christos Davatzikos, Eduardo Barbosa, Daniel Truhn, Tianyu Han

Auditable CT Phenotyping Through Report-derived Radiological Observations

Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read disease-specific findings or shortcuts that correlate with the diagnosis. We tested this in 221 electronic-health-record (EHR) phenotypes using Auditable CT phenotyping (ACT), built on...

💬 0 commentsarXiv:2608.25948v1PDF
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Posted in math.AG · 2026-08-26 · Scott Hiatt

Cyclic covers via mixed Hodge modules

Suppose we are given an arbitrary line bundle $\mathcal{L}$ on a complex algebraic variety $X$, not necessarily smooth. For a positive integer $N$, suppose there exists a global section $s \in Γ(X, \mathcal{L}^{N})$ that defines an effective Cartier divisor $D$. If we denote $π: Y \rightarrow X$ to be the $N$-fold cyclic covering of...

💬 0 commentsarXiv:2608.25947v1PDF
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Posted in astro-ph.HE · 2026-08-26 · A. Acharyya, F. Aharonian, H. Ashkar, M. Backes, R. Batzofin, Y. Becherini, D. Berge, K. Bernlöhr, M. Böttcher, C. Boisson, J. Bolmont, F. Brun, C. Burger-Scheidlin, T. Bylund, S. Casanova, D. Cecchin Momesso, M. Cerruti, M. Chakraborty, A. Chen, M. Chernyakova, J. O. Chibueze, O. Chibueze, T. Collins, B. Cornejo, G. Cotter, G. Cozzolongo, J. de Assis Scarpin, M. de Naurois, E. de Ona Wilhelmi, J. Devin, A. Djannati-Ataï, A. Dmytriiev, K. Egberts, K. Egg, C. Escanuela Nieves, P. Fauverge, S. Fegan, K. Feijen, M. D. Filipovic, G. Fontaine, S. Funk, S. Gabici, Y. A. Gallant, M. Genaro, J. F. Glicenstein, J. Glombitza, P. Goswami, M. -H. Grondin, L. Heckmann, B. Heß, W. Hofmann, T. L. Holch, M. Holler, M. Jamrozy, F. Jankowsky, A. Jardin-Blicq, I. Jaroschewski, D. Jimeno, I. Jung-Richardt, K. Katarzynski, D. Kerszberg, B. Khélifi, N. Komin, K. Kosack, D. Kostunin, R. G. Lang, S. Lazarevic, A. Lemière, M. Lemoine-Goumard, J. -P. Lenain, P. Liniewicz, A. Luashvili, J. Mackey, D. Maheso, D. Malyshev, D. Malyshev, V. Marandon, M. G. F. Mayer, A. Mehta, A. M. W. Mitchell, R. Moderski, L. Mohrmann, H. Ndiyavala, J. Niemiec, P. O'Brien, L. Olivera-Nieto, M. O. Moghadam, S. Panny, M. Panter, R. D. Parsons, P. Pichard, T. Preis, G. Pühlhofer, M. Punch, A. Quirrenbach, A. Reimer, O. Reimer, Q. Remy, H. X. Ren, B. Reville, F. Rieger, G. Roellinghoff, G. Rowell, B. Rudak, K. Sabri, V. Sahakian, A. Santangelo, M. Sasaki, F. Schüssler, J. N. S. Shapopi, I. Shebalkova, W. Si Said, H. Sol, L. Stawarz, R. Steenkamp, S. Steinmassl, T. Tanaka, A. M. Taylor, G. L. Taylor, R. Terrier, Y. Tian, M. Tsirou, N. Tsuji, T. Unbehaun, C. van Eldik, C. Venter, J. Vink, V. Voitsekhovskyi, T. Wach, S. J. Wagner, A. Wierzcholska, M. Zacharias, A. Zech, W. Zhong

Evidence for a spectral steepening of the gamma-ray emission from the Galactic Center ridge

Very-high-energy (VHE) $γ$-ray emission detected from the central molecular zone (CMZ) hints at diffusive propagation of cosmic rays (CRs) injected continuously near the Galactic center (GC). Using H.E.S.S. VHE $γ$-ray observations, we aim to construct a multi-component description of the region in order to derive the spatial and...

💬 0 commentsarXiv:2608.25946v1PDF
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Posted in physics.plasm-ph · 2026-08-26 · Anna Niemelä, Daniel Jordan, Aaro Järvinen, Amanda Bruncrona, Adam Kit, Lorenzo Frassinetti, David Hatch, Leonhard Leppin, Samuli Saarelma, the MAST Upgrade team, EUROfusion Tokamak Exploitation team

Machine learning methods for modelling local, linear gyrokinetic simulations of MAST-U pedestal turbulence

Gyrokinetic (GK) stability strongly influences the performance of high-confinement-mode pedestals in spherical tokamak plasmas. High-fidelity gyrokinetic codes such as GENE can model microinstability-driven transport, but the computational cost limits their routine use in integrated pedestal modeling workflows. Instead, present...

💬 0 commentsarXiv:2608.25945v1PDF
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Posted in cs.CL · 2026-08-26 · Haitong Luo, Xuying Meng, Weiyao Zhang, Wenji Zou, Shengfeng Lou, Xuefeng Jiang, Chungang Lin, Yujun Zhang

Unveiling Spectral Mechanisms in Training-Free LLM Text Detection

The rapid advancement of Large Language Models (LLMs) makes it increasingly difficult to distinguish human writing from machine-generated text. Training-free detection offers a scalable solution, yet common confidence-based metrics mainly measure average token probabilities and often miss the signal fluctuations that characterize...

💬 0 commentsarXiv:2608.25944v1PDF
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Posted in cond-mat.supr-con · 2026-08-26 · Adarsh Karekkat, Gabriele Montefusco, Marco Antonelli

Fluxtube Bouquets and Type-1.5 Clustering in Superfluid Neutron Star Cores

We study mesoscopic configurations of a neutron superfluid coupled to a proton superconductor in the outer core of a neutron star. The condensates are described by a two-component Ginzburg-Landau free energy with local couplings, neglecting genuine phase-gradient entrainment. In two spatial dimensions, we minimize the free energy...

💬 0 commentsarXiv:2608.25943v1PDF
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Posted in cs.HC · 2026-08-26 · Elena Koung, Xinning Gui, Yubo Kou

Gaming Together on Discord: Teen Gamer's Cross-Platform Practices

Discord is one of the most popular communication platforms among gamers. While prior research has highlighted its role in community building, relatively little attention has been paid to its original gaming context-how it shapes gameplay and social experiences. To address this gap, we conducted semi-structured interviews with 16...

💬 0 commentsarXiv:2608.25942v1PDF
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Posted in cs.LG · 2026-08-26 · Suchit Gupte, Xueru Zhang, Mohammad Mahdi Khalili

When Pruning Meets Interpretability: Preserving Sparse Autoencoder Robustness in LLMs

Sparse autoencoders (SAEs) are widely used to interpret the internal representations of large language models (LLMs), yet their reliability under post-hoc model compression remains poorly understood. We present a systematic study of how pruning affects SAE behavior and theoretically show that, for a fixed SAE, its impact is governed...

💬 0 commentsarXiv:2608.25941v1PDF
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Posted in cs.RO · 2026-08-26 · Zaruhi Navasardyan, Hrant Davtyan

A Statistical Audit of Physical AI Benchmark Redundancy

Physical AI models are evaluated on suites of benchmarks that differ across model reports, leaving the model-by-benchmark matrix sparse and the relationship between benchmarks unmeasured. We construct a matrix of 51 models on 12 physical AI benchmarks, selected from a registry of 51 benchmarks and 152 models by reporting density,...

💬 0 commentsarXiv:2608.25940v1PDF
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Posted in cs.SE · 2026-08-26 · Dung Le Quang, Dong Cao Van, Nam Le Hai, Linh Ngo Van, Anh M. T. Bui, Phuong T. Nguyen

XREPOTEST: Benchmarking Multilingual Repository-Level Unit Test Generation for Large Language Models

Large language models (LLMs) have shown promise for automated unit test generation, but existing evaluations largely rely on standalone settings and a narrow set of programming languages, overestimating real-world readiness. We introduce XREPOTEST, a multilingual repository-level benchmark for unit test generation spanning five...

💬 0 commentsarXiv:2608.25939v1PDF
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Posted in cs.AI · 2026-08-26 · Jia-Hao Ji, Sijie Li, Jiabei Cheng, Zixi She, Jin-Tai Yu, Zhiyuan Yuan

Candidate supply and answer selection shape the value of LLM judging in multi-agent systems

Multi-agent systems (MAS) sometimes already have the potential to answer correctly, but still report a wrong answer. Explaining this outcome is difficult because generation, communication and final answer-selection rules usually change simultaneously. We conceptualize multi-agent reasoning as an evolutionary pipeline of candidate...

💬 0 commentsarXiv:2608.25937v1PDF
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Posted in cs.LG · 2026-08-26 · Justin Robert, Raheel Qader

One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

On-policy distillation trains a language model on its own generations while a teacher scores them token by token. It combines the dense supervision of imitation learning with the on-policy sampling of reinforcement learning. But it requires a second, larger model to act as teacher. On-Policy Self-Distillation (OPSD) removes that cost....

💬 0 commentsarXiv:2608.25936v1PDF
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Posted in cs.CV · 2026-08-26 · Yuqiang Lin, Yan Shi, Sam Lockyer, Harish Tayyar Madabushi, Adrian Evans, Wenbin Li, Yinhai Wang, Nic Zhang

TAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding

Traffic Anomaly Understanding (TAU) requires models and systems to detect, reason about, and explain anomalous events in transportation videos. To address this challenge, we propose TAU-Agent, an agentic retrieval-augmented framework for traffic anomaly understanding. Given a task query, a central retrieval agent orchestrates two...

💬 0 commentsarXiv:2608.25935v1PDF
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Posted in cs.AI · 2026-08-26 · Aida Usmanova, Zangir Iklassov, Markus Leippold, Ricardo Usbeck

How Robust Are Automated Fact-Checking Systems? A Cross-Benchmark Evaluation

Automated fact-checking (AFC) systems retrieve evidence and predict claim veracity, yet evaluations omit simple baselines, systems are developed for a single benchmark and cannot be trusted to generalise across domains. No prior work cross-evaluates the full two-stage retrieve-then-verify pipeline across diverse datasets,...

💬 0 commentsarXiv:2608.25934v1PDF
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Posted in cs.CV · 2026-08-26 · Ruoqi Hu, Chulin Zhao, Jiashuo Chang, Ramon Ruiz-Dolz, Hanhe Lin

When Composition Doesn't Add Up: Humans Identifying Defects in AI-Generated Images

*Chulin Zhao and Ruoqi Hu contributed equally to this work. State-of-the-art text-to-image (T2I) models exhibit pronounced and systematic defects when prompts involve intricate compositional factors such as multiple entities and multiple attributes. In this paper, we investigate how humans identify such defects. Specifically, we...

💬 0 commentsarXiv:2608.25933v1PDF
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Posted in physics.geo-ph · 2026-08-26 · Jaehong Chung, Andrew Lockwood, Jef Caers

Continually learning neural-operator surrogate for three-dimensional airborne electromagnetic Bayesian inversion

Three-dimensional probabilistic inversion of time-domain airborne electromagnetic (AEM) data is limited by the cost of the forward solve. Even though one simulation takes only tens of seconds, a Bayesian inversion of a survey of millions of soundings requires of order $10^{10}$ forward evaluations. To address this, we develop a...

💬 0 commentsarXiv:2608.25932v1PDF
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Posted in cond-mat.mes-hall · 2026-08-26 · Marcus N. Kanestrøm, Antonio L. R. Manesco, D. O. Oriekhov

Size and Impurity Effects on Scattering of Valley Hall Modes in Gate-Defined Bilayer Graphene Superlattices

In the present paper we perform a tight-binding simulation of gate-defined islands in Bernal bilayer graphene (BLG). The inversion of the gap sign on the boundaries of the islands creates topologically-protected valley Hall modes. We focus on the specific questions of whether the valley Hall modes around such islands could serve as a...

💬 0 commentsarXiv:2608.25931v1PDF
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Posted in stat.ME · 2026-08-26 · Manuel Pfeuffer, Roshan Prakash Rane, Kerstin Ritter, Sonja Greven

Controlling for Omitted Variable Bias in Deep Neural Networks

Control variables are widely used in statistical modelling to account for omitted variable bias of known confounders. However, they have largely been underexplored in deep learning. This is surprising, given that deep learning models encode image-inferable covariates, such as demographic variables, into their predictions when these...

💬 0 commentsarXiv:2608.25930v1PDF
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Posted in quant-ph · 2026-08-26 · Eleonora Lamma, Tim Bauer, Marcello Dalmonte, Mario Collura

Magic of Kitaev spin liquids

Quantum spin liquids (QSLs) are long-range entangled phases of matter and a natural setting for exploring how quantum correlations generate quantum complexity. Motivated by the emergence of magic, or nonstabilizerness, as a diagnostic of many-body complexity beyond entanglement, we study magic of QSLs by computing the stabilizer Rényi...

💬 0 commentsarXiv:2608.25929v1PDF
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Posted in cs.MA · 2026-08-26 · Peter Pak, Victor Alvarado, Amir Barati Farimani

AI Agentic Selective Laser Sintering Process Optimization

Agentic systems enable the intelligent automation of complex workflows, specific to additive manufacturing this is applicable for complex tasks such as process parameter optimization for mechanical properties. This work investigates the AI enabled agentic process optimization within Selective Laser Sintering (SLS) to iteratively...

💬 0 commentsarXiv:2608.25928v1PDF
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Posted in cs.CV · 2026-08-26 · Yiwen Chen, Guosheng Lin, Chi Zhang

Code World Model: Coding Agent as World Brain

World models aim to simulate how complex environments evolve under actions and events, yet existing video-based world models primarily learn dynamics from visual observations, which reveal outcomes rather than the underlying knowledge, rules, and mechanisms governing world evolution. This makes it difficult to maintain persistent...

💬 0 commentsarXiv:2608.25927v1PDF
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Posted in cs.AI · 2026-08-26 · Roberto Luvini, Giacomo Longo, Alessandro Armando, Enrico Russo

Formal, Executable and Explainable Runtime Monitoring of Spoken Air Traffic Control Operational Procedures

Air traffic control procedures are executed through spoken exchanges between controllers and pilots. These interactions are essential to the safety of air transportation: failures in their execution can create severe operational hazards, as evidenced by past fatal accidents. Assessing whether an instruction has been followed requires...

💬 0 commentsarXiv:2608.25926v1PDF
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Posted in math.LO · 2026-08-26 · Miara Sung

Jump Closure and Limit Uniformization in the Ideal Completion of the Turing Degrees

The Turing jump has no fixed point on the Turing degrees: $\mathbf a <_T \mathbf a'$ for every degree $\mathbf a$. After passing to the ideal completion, however, a natural fixed-point phenomenon appears. We study the Scott-continuous lifting $Γ:\operatorname{Idl}(\mathbf D_T)\to\operatorname{Idl}(\mathbf D_T)$, given by...

💬 0 commentsarXiv:2608.25925v1PDF
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Posted in astro-ph.EP · 2026-08-25 · Prabal Saxena, Stefano Bertone, Heather V. Graham, Natalie M. Curran, Aaron B. Regberg, Andrew Needham, D. E., Pugel, Noah E. Petro

Potential survivable niches for microbial life on the lunar south pole

Most lunar surface conditions are incredibly harsh for microbial survival. High ultraviolet radiation, temperatures, and energetic particle radiation limit survival over most unprotected lunar surfaces, particularly in equatorial regions where all previous crewed exploration occurred. However, whether these harsh conditions are...

💬 0 commentsarXiv:2608.24751v1PDF