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https://arxiv.org/html/2607.24970v2
That total number of applications mirrors the classical coupon-collector problem: if each breakfast cereal box contains one of nn equally likely coupons, how many cereal boxes do I need to buy to collect all nn coupons
https://arxiv.org/list/cs.LG/2022-12
2001-2004
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[1] arXiv:2212
https://arxiv.org/html/2608.02909v1
In practice, most researchers use the generated variables “as is” on the right-hand side of the regression, as if they captured the true, latent variable of interest
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
https://arxiv.org/html/2407.08254v2
We prove that our approach guarantees that the average joint action of all agents converges to a Nash equilibrium (NE) if every agent applies the same RM procedure in any cooperative game with a submodular utility function
https://arxiv.org/html/2407.06328v2
In such dynamic game settings, if agents have limited capabilities for observing the game’s state, they must estimate it and use this estimate to infer expected payoffs and select one of available strategies
https://arxiv.org/html/2407.20187v1
If there are more blue nodes than red nodes then we say blue is the majority
https://arxiv.org/html/2309.08431v3
Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP’s range of liquidity
https://arxiv.org/html/2407.05763v1
A system is said to be finite-time stable if it is Lyapunov stable and its states reach zeros at a finite instant of time (which may depend on the initial state of the system)
https://arxiv.org/html/2508.16279v1
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arXiv:2508
https://arxiv.org/html/2608.02942v1
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arXiv:2608
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
https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/abs/2407.16741v1
16741v1] OpenDevin: An Open Platform for AI Software Developers as Generalist Agents
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https://arxiv.org/abs/2407.16741v3
16741v3] OpenHands: An Open Platform for AI Software Developers as Generalist Agents
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arXiv is now an independent nonprofit
https://arxiv.org/abs/2407.16741
16741] OpenHands: An Open Platform for AI Software Developers as Generalist Agents
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arXiv is now an independent nonprofit
https://arxiv.org/html/2608.03038v1
(2025), and even advanced models can produce errors in nuanced statistical reasoning, code generation, and interpretation Herklotz et al
https://arxiv.org/html/2401.02063v2
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https://arxiv.org/list/cs.CG/new
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New submissions (showing 1 of 1 entries)
[1] arXiv:2608
https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3
https://arxiv.org/list/cs.AR/new
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[1] arXiv:2608
https://arxiv.org/list/cs.SY/new
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[1] arXiv:2608
https://arxiv.org/html/2608.02851v1
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arXiv:2608
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
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2508.20101v1
Volatility in Financial Networks
1 Introduction
2 Heterogeneous Spatiotemporal GARCH model
2.1 Model Assumptions
2.2 Prediction at Unobserved Locations
2.3 Estimation of Parameter Surface
2.4 Properties of the Estimator
3 Monte Carlo
https://arxiv.org/html/2607.28294v1
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https://arxiv.org/html/2607.27099v1
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arXiv:2607
https://arxiv.org/html/2407.20441v1
I Introduction
II System Model and Problem Formulation
III Main result
IV Proof of the main result
V Simulations
VI Conclusion and Future Work
Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
Nicolò Dal Fabbro, Arman Adibi
https://arxiv.org/html/2407.15077v2
2 Problem Definition
3 Method
3.1 Monotonic Improvement of B2MAPO scheme
3.2 B2MAPO Framework
3.2.1 Batch Sequence Generation Layer
3.2.2 Batch by Batch Policy Optimization Layer
4 DAG-based B2MAPO Algorithm
4.1 Hierarchical DAG