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arXiv preprints from January 1, 2026 through September 23, 2026 — 15:25:21 EST

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Posted in econ.GN · 2026-07-16 · Magnus Lundgren, Jonas Tallberg

Governing Artificial Intelligence: Public Preferences and Regulatory Options

Artificial intelligence (AI) is rapidly transforming economies, societies, and polities, raising fundamental questions about how it should be regulated. Policymakers face choices over whether to prioritize innovation or safety, rely on public oversight or private self-regulation, and govern nationally or internationally. Yet little is...

💬 0 commentsarXiv:2607.14585v1PDF
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Posted in econ.GN · 2026-07-16 · Youngjin Hong, In Kyung Kim, Frank Verboven

Which Green Technology to Subsidize? Evidence from Electric Vehicles in South Korea

We develop a framework to compare the relative effectiveness of subsidizing alternative emission-reducing technologies. We show that an intermediate technology may reduce emissions more effectively than the cleanest technology if it induces sufficiently greater substitution away from the prevailing high-emission technology. We apply...

💬 0 commentsarXiv:2607.14446v1PDF
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Posted in cs.LG · 2026-07-15 · Mohammad Rashid, Hema Yoganarasimhan

Adaptive Ad Load Design for Sponsored Search Markets: Evidence, Theory, and Deployment

Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes. We study this trade-off using a large-scale randomized field experiment on an Android app store, where over five million users are exposed to one through six...

💬 0 commentsarXiv:2607.14418v1PDF
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Posted in econ.EM · 2026-07-15 · Benjamin Côté, Ruodu Wang

Probability of worthwhile effect of monotone-response treatments

Experiments may, by design, prevent one from observing on a single subject both the response to a treatment and to its absence. Because of this, marginal distributions for both cases may be observable but not their joint distribution, thus obscuring the distribution of the treatment effect. We examine the case where we impose that the...

💬 0 commentsarXiv:2607.14414v1PDF
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Posted in cs.LG · 2026-07-15 · Fengzhuo Zhang, Zhuoran Yang, Dirk Bergemann

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion

Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scarce computational resources that users must share. When should a user invest in expensive Supervised Fine-Tuning (SFT) versus lightweight In-Context Learning (ICL)? How does...

💬 0 commentsarXiv:2607.14371v1PDF
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Posted in cs.GT · 2026-07-15 · Taksch Dube

When Is Delegated Play Truthful? Within-Range Regret and the Trilemma of Aligned Delegation

Advertisers delegate bidding to autobidders; users delegate tasks to language-model agents. A person describes what they want to an automated proxy that acts in a mechanism on their behalf. This is the revelation principle in production, and it forces a question classical theory assumes away: when is it optimal to describe yourself...

💬 0 commentsarXiv:2607.14357v1PDF
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Posted in econ.EM · 2026-07-15 · Likai Chen, Weining Wang

From Vector Autoregressions to AI-based Time Series Forecasting: A Review

Forecasting is a central goal of time-series analysis. This review centers on three major developments in recent AI-based time-series forecasting: transformers, large pretrained models for zero-shot forecasting, and diffusion-based generative forecasters. We connect these methods to the econometric tradition built around the vector...

💬 0 commentsarXiv:2607.14279v1PDF
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Posted in econ.EM · 2026-07-15 · Kamil Makieła

Model Uncertainty under Non-Gaussian Errors: Bayesian Model Averaging and Selection in Stochastic Frontier Models

The paper investigates Bayesian Model Averaging and Selection (BMA/S) under non-standard stochastic assumptions, focusing on stochastic frontier analysis (SFA). We propose fast, reliable procedures for inference in the normal-exponential stochastic frontier model and examine whether accounting for asymmetric disturbances affects model...

💬 0 commentsarXiv:2607.14274v1PDF
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Posted in econ.TH · 2026-07-15 · Paul H. Y. Cheung, Yi-Hsuan Lin, Chung-Hao Sheu

Revealed Attentional Interference

We study the impact of external stimuli on attention in the Attentional Interference Model, capturing two opposing forces in consideration-set formation: proactive and retroactive interference. Proactive interference limits the permeation of external information, while retroactive interference displaces internally generated...

💬 0 commentsarXiv:2607.13974v1PDF
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Posted in econ.EM · 2026-07-15 · Giuseppe Cavaliere, Luca Fanelli, Marco Mazzali

Global factors for local shocks in a data-scarce environment: with an application to regional fiscal multipliers in Italy

We propose a novel econometric methodology for Structural Vector Autoregressions with external instruments (`proxy-SVARs' or `SVAR-IVs') in panel data characterized by strong cross-sectional dependence, dynamic heterogeneity, and limited availability of direct external instruments for the shocks of interest. For each unit, we specify...

💬 0 commentsarXiv:2607.13879v1PDF
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Posted in econ.EM · 2026-07-15 · Arturas Juodis, George Kapetanios, Vasilis Sarafidis

Estimation and Inference for Latent Dual Networks Using High-Dimensional IV Screening

We develop a novel methodology for estimation and inference in high-dimensional panel network models with latent dual structures. The framework allows outcomes to be affected simultaneously by positive and negative interaction channels, accommodating settings in which some interactions reinforce outcomes while others generate...

💬 0 commentsarXiv:2607.13862v1PDF
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Posted in cs.SE · 2026-07-13 · Haotian Lin, Silin Chen, Xiaodong Gu, Yuling Shi, Chengxi Pan, Jiaqi Ge, Mengfan Li, Jianghong Huang, Mengchieh Chuang, Beijun Shen, Haibing Guan

Know Before Fix: QA-Driven Repository Knowledge Acquisition for Software Issue Resolution

LLM-based coding agents have significantly advanced automated software issue resolution, yet they remain highly prone to factual errors caused by insufficient repository understanding. Recent methods attempt to mitigate this limitation through pre-repair repository exploration; however, their fix-driven strategies explore repositories...

💬 0 commentsarXiv:2607.11111v1PDF
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Posted in q-fin.PR · 2026-05-30 · William H. Press, Alex Dannenberg

Multiplicative Langevin Process for Volatilities Produces Observed Q-Variance Regularities

Q-variance (so-called) posits a statistical relationship $\mathbf{E}(σ^2 | z) = σ_0^2 + \tfrac{1}{2}z^2$ between an asset's volatility $σ^2$, as observed in a time interval $T$, and its (suitably scaled) return $z$ in the same interval. We here show that this relationship is {\em exactly equivalent} to to positing an Inverse Gamma...

💬 0 commentsarXiv:2606.00800v2PDF
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Posted in q-fin.RM · 2026-06-20 · Shintaro Mori, Masato Hisakado

Temporal Coarse-Graining of Multi-Sector Default Count Data Generates Posterior-Implied Copulas

Sectoral default dependence is usually described by a static correlation matrix, a static copula, or a small number of common factors. Such representations, when specified separately at each observation horizon, do not by themselves explain why the effective dependence observed in monthly credit data differs from that observed after...

💬 0 commentsarXiv:2606.22162v2PDF
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Posted in physics.hist-ph · 2026-07-10 · Rasmus Jaksland

Spacetime from Entanglement: The Emergence of Metric, Gravity, or Topology

In AdS/CFT, one often finds claims along the lines that ``spacetime emerges from entanglement." This paper argues that behind these general statements hide three distinct emergence claims about, respectively, metric, gravitational dynamics, and topological connectivity. Thus, despite being advertised with the same terminology, these...

💬 1 commentsarXiv:2607.09823v1PDF
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Posted in cs.LG · 2026-07-15 · Lincan Li, Zheng Chen, Yushun Dong

NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis

Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling. While spatial-temporal graph neural networks (STGNNs) have advanced EEG brain network representation learning, the resulting graph structures suffer from low...

💬 1 commentsarXiv:2607.14314v1PDF
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Posted in astro-ph.GA · 2026-07-16 · Kerwann Tep, Douglas C. Heggie

Discrepancies between Chandrasekhar's theory of relaxation and $N$-body simulations

Globular clusters are systems which are known to be particularly well described by two-body relaxation. In recent decades many studies have shown that Chandrasekhar's orbit-averaged theory is able to reproduce many features of numerical simulations. However, it has been claimed that differences between the theory and simulation...

💬 1 commentsarXiv:2607.14844v1PDF
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Posted in astro-ph.GA · 2026-07-13 · Christa DeCoursey, Christian Vassallo, Louis-Gregory Strolger, Justin D. R. Pierel, Eiichi Egami, Seppo Mattila, Armin Rest, David A. Coulter, Andrew J. Bunker, Alex J. Cameron, James M. DerKacy, Daniel J. Eisenstein, Michael Engesser, Ori D. Fox, Sebastian Gomez, Massimo Griggio, Kevin Hainline, Ryan Hausen, Zhiyuan Ji, Benjamin D. Johnson, Roberto Maiolino, Takashi J. Moriya, Brant Robertson, Koji Shukawa, Matthew R. Siebert, Fengwu Sun, Sandro Tacchella, Christina C. Williams, Christopher N. A. Willmer, Yossef Zenati

The JADES Transient Survey II: Volumetric Supernova Rates out to z~5

The JADES Transient Survey (JTS) identified 83 supernova (SN) candidates in the JADES Deep Field, a $\sim$25 arcmin$^2$ region with deep ($\sim$30 mag) multi-band, multi-epoch JWST/NIRCam coverage. We use this sample to derive the first volumetric core-collapse (CC) SN and Type Ia (SN Ia) rates in the $z$$\sim$2-5 range. Many of these...

💬 1 commentsarXiv:2607.12018v1PDF
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Posted in hep-ph · 2026-07-07 · Sayantan Chakraborty, Yash Dadhwal, Arun M. Thalapillil

The Positivity Geometry of Photon--Dark-Photon Effective Field Theories

We derive positivity bounds on the complete dimension-eight effective field theory of photons and a massless dark photon. The mixed gauge sector contains twelve CP-even Wilson coefficients and an enlarged helicity-amplitude structure. Using a modified forward-limit dispersion relation, we analytically obtain non-trivial linear and...

💬 1 commentsarXiv:2607.06658v1PDF
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Posted in cs.MA · 2026-07-16 · Ali Ghoroghi, Yacine Rezgui, Afrouz Ghaemi, Cristina De Nardi, Andrei Hodorog

Multi-Scale Equilibrium under Variable Indicator Dimensionality: Faithful Reduction of Dynamic Attractors in Urban Mobility Systems

Equilibrium analysis of urban mobility systems is formulated in a high-dimensional indicator space, whilst data availability varies sharply across cities and disruption contexts. This paper gives a formal treatment of that mismatch. It presents a dynamic multi-layer equilibrium attractor for disrupted urban mobility, in which a fast...

💬 1 commentsarXiv:2607.14815v1PDF
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Posted in cs.AI · 2026-07-13 · Ivan Bercovich

Good Benchmarks

Good tasks are correct, solvable, verifiable, well-specified, and hard for interesting reasons. The best tasks describe a real problem an experienced practitioner would recognize, in language a practitioner would use, with tests that verify the outcome rather than the approach.

💬 1 commentsarXiv:2607.12217v1PDF
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Posted in cs.RO · 2026-07-14 · Zhilin He, Yorai Shaoul, Jiaoyang Li

Model-Based Diffusion Optimal Control for Multi-Robot Motion Planning

Multi-Robot Motion Planning in continuous environments, where robots must generate dynamically feasible, collision-free trajectories, is challenging due to the combinatorial growth of the joint trajectory space and the difficulty of enforcing dynamic feasibility and hard safety constraints. Recent approaches recast trajectory planning...

💬 1 commentsarXiv:2607.12423v1PDF
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Posted in cs.AI · 2026-07-14 · Kaiwen Zheng, Junchen Fu, Wenhao Deng, Hu Han, Joemon M. Jose, Xuri Ge

Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?

Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc. However, these performance improvements are often accompanied by an increase in model...

💬 1 commentsarXiv:2607.12787v1PDF
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Posted in math.CO · 2026-07-12 · Dariush Kiani, Hanieh Tavakolipour

Properties of the Tropical Characteristic Polynomial of Symmetric Matrices

We investigate the combinatorial structure of the tropical characteristic polynomial of symmetric matrices using the tropical permanents of their principal submatrices. We establish new inequalities for the leading coefficients of the tropical characteristic polynomial, revealing concavity properties of the coefficient sequence and...

💬 1 commentsarXiv:2607.10922v1PDF