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We give the distribution of outcomes across runs, report which kinds of self-proposed question the system converts into accepted results, examine one accepted result in depth, report the axiom audit that screens each accepted result, and trace the library feedback loop closing on a concrete lemma
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Report GitHub Issue

https://arxiv.org/html/2607.22511v2
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Condensed Matter

https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
A core calculus gives an effect-confinement lemma and six safety properties

Approximating Choice Data by Discrete Choice Models

https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset M Additional Empirical Application: Fishing-site Choice M

I INTRODUCTION

https://arxiv.org/html/2407.10811v1
Despite all efforts in this field, there is no real-world deployment of an actual RL-based TSC to date

Stochastic Choice with Advertising

https://arxiv.org/html/2608.03504v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/pdf/2006.02318
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www

Report GitHub Issue

https://arxiv.org/html/2608.02609v1
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Machine Learning Dec 2022

https://arxiv.org/list/cs.LG/2022-12
2001-2004 Showing up to 50 entries per page: fewer | more | all [1] arXiv:2212

Mitigating Distribution Shift in Stock Price Data via Return-Volatility

https://arxiv.org/html/2508.20108v2
Normalization for Accurate Prediction 1 Introduction 2 Related Works 2.1 Geometric Brownian Motion 2.2 Deep Learning-Based Stock Price Prediction 2.3 Methods for Addressing Distribution Shift 3 Proposed Method 3.1 Overview 3.2 Return

You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4
This version: I would like to thank Xu Cheng, Frank Diebold, Wayne Gao, George Mailath, and seminar participants at Chicago Booth, Paris Econometrics Seminar, Toulouse School of Economics, and the University of Pennsylvania for valuable discussions and comments that substantially improved this article

Raising Rivals’ Costs on Hybrid Platforms: The Complementarity of Fees

https://arxiv.org/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan

Multi-Currency AMMs for Decentralized FOREX Markets: Feasibility

https://arxiv.org/html/2607.26405v1
& Optimal Design Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report

Existence of Richter–Peleg representation for general preferences

https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license arXiv:2508

Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
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Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.

Microstructural Foundations of Rough Noise1footnote

https://arxiv.org/html/2607.29442v2
1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise. We benefitted from comments by and discussions with Kim Christensen, Mikko Pakkanen, Bent Jesper Christensen, and Mikkel

Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
Probing five architectures, twelve trained models each, under lemma-disjoint cross-validation with controls and surface baselines, we show that the models encode the L-shaped class itself, not just its visible alternations

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing) D

On the Complexity of Learning to Cooperate with Populations of Socially

https://arxiv.org/html/2407.00419v1
Two natural assumptions in such settings are that: 1) all agents in the population are individually rational learners, and 2) when any two members of the population are paired together, with high-probability they will achieve at least the same utility as they would under some Pareto efficient equilibrium strategy

United We Stand: Decentralized Multi-Agent Planning With Attrition

https://arxiv.org/html/2407.08254v2
Thus, attention has turned towards algorithms based on Monte Carlo tree search (MCTS), due to their ability to effectively explore long planning horizons, their anytime nature [25], and their excellent performance in decentralized settings [11, 7, 15], effectively overcoming the limitations of other approaches

Rainfall is rough

https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license arXiv:2607

SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of

https://arxiv.org/html/2608.03573v1
org perpetual non-exclusive license arXiv:2608

Learning to Control Unknown Strongly Monotone Games

https://arxiv.org/html/2407.00575v3
This would require the manager to collect this information from the players, and then solve a large-scale optimization problem

Bandwidth-Free Inference for Recursive Nonlinear Impulse Response

https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license arXiv:2608

Maximizing utility in multi-agent environments by anticipating the

https://arxiv.org/html/2407.04889v1
(2019b); however, in general settings it is not known how to best play against mean-based learners, even if the agent knows everything about the learners, and can predict with certainty what actions they would take as a function of the history of play

Stochastic Games with Minimally Bounded Action Costs

https://arxiv.org/html/2407.18010v2
Since acting at each available opportunity would accumulate prohibitively large costs, the resulting decision problem is one in which players must make strategic decisions about when to execute actions in addition to their choice of action

Computational Geometry

https://arxiv.org/list/cs.CG/new
more | all New submissions (showing 1 of 1 entries) [1] arXiv:2608

Optimal testing in a class of nonregular models

https://arxiv.org/html/2403.16413v3
org perpetual non-exclusive license arXiv:2403

Mathematical Finance Apr 2021

https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3