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arXiv preprints from January 1, 2026 through September 21, 2026 — 19:02:04 EST

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Posted in cs.CV · 2026-09-09 · Siddharth Gupta, Jitin Singla

Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation

In real-time colonoscopy, ground-truth annotations are unavailable at inference, so polyp segmentation models can fail silently. We propose Referee-Based Quality Estimation (RBQE), a reference-free framework measuring agreement between a primary segmentation model and an independently trained referee on the same image. RBQE is...

💬 0 commentsarXiv:2609.10495v1PDF
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Posted in cs.CL · 2026-09-09 · Blake Stenstrom, Charangan Vasantharajan, Brian Sathianathan

IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier

Enterprises deploy systems, not checkpoints. Usable capability depends jointly on weights, serving route, precision, output contract, and harness, yet all 18 audited benchmarks score advertised model identifiers. We treat this as measurement error and give a protocol that makes it reportable. It has three parts. A gold-blind...

💬 0 commentsarXiv:2609.10494v1PDF
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Posted in cs.GT · 2026-09-09 · Xiaohui Bei, Zehan Lin, Shengxin Liu, Rong Luan, Biaoshuai Tao

Non-Existence of PMMS Allocations and a $4/3$-PMMS Guarantee for Additive Chores

We study pairwise maximin share (PMMS) fairness for indivisible items with additive preferences. We give a polynomial-time reduction from chores to goods that preserves the existence of a PMMS allocation. Together with known nonexistence results for chores, this yields nonexistence for additive goods. In addition, we show that...

💬 0 commentsarXiv:2609.10493v1PDF
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Posted in cs.LG · 2026-09-09 · Saurabh Sihag, Andrea Cavallo, Elvin Isufi, Gonzalo Mateos, Alejandro Ribeiro

Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs

This feature article provides an overview of the theoretical foundations for coVariance neural networks (VNNs), i.e., graph neural networks (GNNs) operating on covariance matrices as graphs. Covariance matrices are ubiquitous across domains, and hence, the deployment of GNNs often leverages graphs of pairwise statistical dependencies....

💬 0 commentsarXiv:2609.10490v1PDF
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Posted in q-fin.TR · 2026-09-09 · Magnus Hansson

dexamine: A Python package for Uniswap event data on Ethereum

Decentralized exchanges record trading and liquidity provision on public blockchains, but empirical analysis requires interpreting these records and linking them to execution metadata. dexamine is a Python package that parses Uniswap v2 and v3 events on Ethereum. It converts transaction receipt logs into observations of trades and...

💬 0 commentsarXiv:2609.10407v1PDF
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Posted in cond-mat.mes-hall · 2026-09-09 · Isaac Tesfaye, Giandomenico Palumbo

Symplectic Hopf Insulator: Delicate Topology in Bosonic Bogoliubov-de Gennes Systems

Recent advances in topological phases have highlighted the role of symplectic (Krein-space) topology in the classification of bosonic Bogoliubov-de Gennes (BBdG) systems. In this work, we construct a BBdG realization of Hopf topology, which we dub the symplectic Hopf insulator, starting from a microscopic Bose-Hubbard generalization...

💬 0 commentsarXiv:2609.10541v1PDF
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Posted in cs.CV · 2026-09-09 · Zheng-Hui Huang, Guixu Lin, Jiacheng Lin, Yi-Chuan Huang, Ruihan Yu, Muyao Niu, Siqi Yang, Yu-Lun Liu, Yung-Yu Chuang, Kaipeng Zhang, Zhixiang Wang

Programmable World Model

Recent video world models generate increasingly realistic and interactive visual experiences, yet lack reliable mechanisms for maintaining persistent world state and enforcing programmable rules over extended interactions. We introduce Programmable World Model, a framework that decouples world-state evolution from visual observation...

💬 0 commentsarXiv:2609.10540v1PDF
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Posted in cs.CL · 2026-09-09 · Yiling Ma, Yilun Zhao, Sihong Wu, Manasi Patwardhan, Arman Cohan

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

A research idea may be novel, coherent, and scientifically plausible, yet its proposed method may remain insufficiently specified for faithful implementation. We study the codification readiness of implementation-facing research-method specifications, defined by whether they provide sufficient methodological information for a...

💬 0 commentsarXiv:2609.10539v1PDF
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Posted in math.DS · 2026-09-09 · Subhasish Mukherjee

Exact dimensionality of stationary measures for nonuniformly conformally contracting random diffeomorphisms

We prove exact dimensionality of ergodic stationary measures for random $C^1$ diffeomorphisms in the single negative Lyapunov scale setting. Let $ν$ be a Borel probability measure on $\mathrm{Diff}^1(M)$ satisfying a logarithmic $C^1$ moment condition, and let $μ$ be a $ν$-stationary ergodic probability measure. If $λ_{\mathrm{top}} =...

💬 0 commentsarXiv:2609.10538v1PDF
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Posted in cs.CR · 2026-09-09 · Yiwei Fang, Yichen Liu, Ze Jin, Haoqiang Wang, Qixu Liu, Luyi Xing

Towards Tackling Application Logic Flaws through Autonomous Formal-Logic Modeling and Automated Reasoning

Logic flaws pose significant challenges in the design and implementation of modern, semantically rich systems and applications, impacting security, privacy, and trust. These flaws are inherently tied to business-specific semantics and threat models, making their discovery and reasoning difficult and hard to scale. Real-world systems...

💬 0 commentsarXiv:2609.10537v1PDF
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Posted in astro-ph.IM · 2026-09-09 · Sam J. Potier, Arlene J. Aleman, Justin R. Crepp, Stanimir Letchev

Performance Comparison of the Nonlinear Curvature and Shack-Hartmann Wavefront Sensors in Strong Turbulence

Strong turbulence induces spatial variations in beam intensity that hinder the reconstruction process of many commonly deployed adaptive optics (AO) systems that use gradient-based wavefront sensors (WFS), such as the Shack-Hartmann wavefront sensor (SHWFS) and pyramid wavefront sensor. The nonlinear curvature WFS (nlCWFS) uses...

💬 0 commentsarXiv:2609.10536v1PDF
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Posted in hep-th · 2026-09-09 · Daniel Green, Kshitij Gupta, Qiya Zhang

For Whom Does Bell Hold?

Violations of Bell's inequalities offer a definitive signal of non-classical (quantum) behavior in local deterministic systems. Yet, in many physical settings where quantum mechanics is expected to play an important role, one cannot construct a Bell-type test using the available observables. Cosmology offers one concrete example,...

💬 0 commentsarXiv:2609.10535v1PDF
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Posted in stat.ME · 2026-09-09 · Phil Assheton

Likelihood-free inference with nuisance parameters through normalizing flows

We present a simple decomposition of a neural-network-based normalizing flow that naturally uncovers a pivotal statistic (or something close) in the presence of nuisance parameters, based only on a sample generator from the distribution of interest. We show that the statistic is near-pivotal in the sense of minimum average...

💬 0 commentsarXiv:2609.10534v1PDF
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Posted in quant-ph · 2026-09-09 · Ali Akil, M. Hamed Mohammady, Zihan Wang, Oscar Dahlsten

Noether Symmetries Generate Deterministic Energy-Harvesting Protocols

We consider the general principles for when deterministic energy harvesting (DEH) is possible. DEH means absorbing energy from a fluctuating source without entropy being absorbed. We show that the symmetry structure of the source--harvester dynamics gives a general route beyond existing examples to identify DEH capable source states....

💬 0 commentsarXiv:2609.10533v1PDF
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Posted in astro-ph.GA · 2026-09-09 · Manish Kataria, Kanak Saha, Bruce Elmegreen

The kinematics of tadpole galaxies at intermediate redshift $z \sim 0.4 - 1.5$

Galaxy morphology and kinematics encode complementary information about the assembly history of galaxies, but the extent to which they evolve in tandem remains unclear. Tadpole galaxies, characterized by their distinct head-tail morphology and pronounced asymmetry, provide an ideal laboratory for investigating whether strongly...

💬 0 commentsarXiv:2609.10532v1PDF
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Posted in cs.CV · 2026-09-09 · Jerred Chen, Simon Weber, Ronald Clark

Guiding Image-to-3D Generation with Test-Time Partial Observations

Image-to-3D models can generate visually compelling 3D assets from a single RGB image, but their geometry is often only loosely constrained by the available observations, limiting their use in applications that require geometric fidelity. In many real-world settings, however, partial geometric observations of the object may be...

💬 0 commentsarXiv:2609.10531v1PDF
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Posted in math.PR · 2026-09-09 · Xinxin Chen, Michel Pain

Fluctuations of additive martingale limits of branching Brownian motion

Consider a one-dimensional branching Brownian motion. Let $W_\infty(β)$ denote the limit of the additive martingale in the subcritical regime $\lvert β\rvert < β_c$ and $Z_\infty$ be the limit of the derivative martingale at criticality. Madaule (Stochastic Process. Appl. 126 (2016), no. 2, 470--502) established the following...

💬 0 commentsarXiv:2609.10530v1PDF
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Posted in cs.LG · 2026-09-09 · P. M. Aronow, Nathan Kallus, Patrick Lopatto

A positive resolution of the gap-entropy conjecture

We prove the gap-entropy conjecture for fixed-confidence best-arm identification with independent unit-variance Gaussian arms, means in $[0,1]$, and a unique optimal arm. For each suboptimal arm $i$, let $Δ_i=μ_*-μ_i$ be its gap from the optimal mean, and write $H=\sum_{i\ne *}Δ_i^{-2}$. Let $p_r$ be the fraction of $H$ contributed by...

💬 0 commentsarXiv:2609.10529v1PDF
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Posted in math.PR · 2026-09-09 · Fu-Hsuan Ho

The Ding--Song--Sun inequality via a maximum principle

We prove the Ding--Song--Sun inequality for continuous spin models on finite ferromagnetic graphs whose even single-site potentials have convex derivatives on the positive half of their domain. This class, introduced by Ellis, Monroe and Newman, includes the $\varphi^4$ and sinh-Gordon potentials. The proof uses a rank-one...

💬 0 commentsarXiv:2609.10528v1PDF
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Posted in math.DG · 2026-09-09 · Wangzhe Wu

A sharp threshold for mixed $Q$-curvature rigidity

Let $I_a(g)=Q_g+aσ_2(A_g)$, where $A_g$ is the Schouten tensor and $Q_g$ is Branson's $Q$-curvature. On a closed connected manifold of dimension $n\ge4$ with a positive Einstein metric $g_0$, we prove that every smooth metric conformal to $g_0$ with nonnegative scalar curvature and constant $I_a(g)$ is Einstein for $a\ge-4$. This...

💬 0 commentsarXiv:2609.10527v1PDF
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Posted in cs.FL · 2026-09-09 · Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao

Characterizing Language Generation in the Limit: Finite Witnesses and a Separation-Width Hierarch

Language generation in the limit asks for valid unseen elements from every exhaustive positive presentation of an unknown infinite language. We characterize this task for arbitrary families over a countable universe. Generation is possible exactly when each target can be assigned a finite positive witness so that the targets activated...

💬 0 commentsarXiv:2609.10525v1PDF
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Posted in cs.CV · 2026-09-09 · Md. Masudul Islam, Galib Muhammad Shahriar Himel, Md. Golam Moazzam, Mohammad Shorif Uddin

Precision in Rice Variety Classification using Stacking-Based Ensemble Learning

Rice, a staple food for a significant portion of the global population, exhibits remarkable diversity in its varieties, presenting substantial challenges for accurate identification by consumers, traders, and farmers. This complexity often facilitates fraudulent practices, such as the unauthorized mixing of rice types, which...

💬 0 commentsarXiv:2609.10524v1PDF
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Posted in cs.RO · 2026-09-09 · Yanzhe Chen, Zechen Bai, Zhijun Cao, Wenzheng Zeng, Kevin Qinghong Lin, Yiqi Lin, Guoqiang Liang, Kevin Yuchen Ma, Qiming Huang, Mike Zheng Shou

Show-Harness: Just a VLM Agent Can Play Robots

Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action. Show-Harness exposes discrete...

💬 0 commentsarXiv:2609.10522v1PDF