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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/src/2405.20232
…qa-��ýy>‚râ@©¥¥5ÇDEÝøÃeè_à_à"_ýD”“ƒxmðCôÜOÁ²âÄ—‘
https://arxiv.org/src/2104.08691
t7x‡eq›Ž9 GËßCS·æÀ‘£rhÏÑIÖ0ÏP‘¹Ì¬if#iš¹õq¬¥¾ÒŽÆz“ÊXë"]FcÁìçfï:ßÝbáT�ãNô»˜g·ÄÎLLaŽí©"§tÑ�
https://arxiv.org/html/2608.02942v1
Across four mathematical reasoning and code-generation benchmarks, OPTD consistently improves the quality–efficiency trade-off and attains the strongest overall quality-constrained AUP among the evaluated few-step baselines
https://arxiv.org/html/2403.16413v3
For testing a simple null hypothesis against a simple alternative one, Neyman-Pearson’s fundamental lemma yields an optimal power property of the likelihood ratio test even for the case of parameter-dependent support
https://arxiv.org/html/2608.03452v1
de
Abstract
When a transformer learns an irregular morphological pattern, what has it learned
https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3
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/html/2608.03067v1
7Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
https://arxiv.org/html/2508.20101v1
However, in real-world financial systems, individual assets are not isolated
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/2508.08980v3
A relation admits such a representation if and only if it is strongly acyclic and its transitive closure is separable, where separability means embeddability in a preorder possessing a countable separating stratification—a countable family of pairwise disjoint subsets, none containing a strictly ranked pair, that
https://arxiv.org/html/2407.00575v3
Even worse, the manager would need to collect the parameters periodically if they are time-varying, and then solve the large-scale optimization problem again
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/2607.28294v1
A second scheme preserves the realized count and sets the number of events in the bootstrap world as n∗=n​(T)n^{\ast}=n(T)
https://arxiv.org/html/2607.27099v1
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https://arxiv.org/html/2407.20441v1
However, in real-world multi-agent systems, inter-node communication imposes strong constraints, such as limited link capacity and transmission delays [4, 5, 6]
https://arxiv.org/html/2604.05327v4
For these problems, it is therefore always optimal for the decision-maker to proceed as if the likelihood were correctly specified and select the resulting optimal decision rule
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/2407.15077v2
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
https://arxiv.org/html/2407.18010v2
1 Introduction
2 Formulation
3 Existence of a Value and Saddle Point Equilibrium
4 Learning in Unknown Environments
4.1 Convergence using Linear Function Approximators
5 Intervention Budget Constraints
6 Discussion on Subcases
7 Related
https://arxiv.org/list/cs.CV/new
02713 [pdf, html, other]
Title: Quo Vadis, World Modeling
https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon
https://arxiv.org/pdf/2407.01722
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https://arxiv.org/list/cs.CG/new
PR)
The Johnson--Lindenstrauss lemma asserts that every set of $n$ points in $d$-dimensional Euclidean space embeds into $O(\varepsilon^{-2}\log n)$-dimensional Euclidean space with distortion at most $1+\varepsilon$
https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/list/cs.AR/new
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