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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.
https://arxiv.org/html/2608.03577v1
org/standards/popular/iso-9000-family specifically ISO 9001:2015 Applying the same logic to translation production workflows means that MT and AI must be incorporated sensibly to achieve manufacturing-grade reliability, risk guarantees, and robustness
https://arxiv.org/html/2401.02063v2
org that the field of high-frequency GWs will go from the discovery of the first stellar-mass binary black hole merger in 2015 [9]
https://arxiv.org/list/cs.CG/new
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New submissions (showing 1 of 1 entries)
[1] arXiv:2608
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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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/2407.20441v1
In an effort to reduce the sample-complexity of RL, recent research findings in multi-agent RL (MARL) have theoretically shown that, despite temporal correlations in each agent’s observations, multi-agent cooperation provides collaborative performance gains [1, 2]
https://arxiv.org/list/cs.DS/new
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New submissions (showing 7 of 7 entries)
[1] arXiv:2608
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/2407.04974v1
The primary reason for focusing on this MARL setting is that off-policy learning [6, 7, 8, 9] provides a more general setting compared to its on-policy counterpart, as it allows the behavioral policy to differ from the estimated target policy
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/2607.27099v1
org perpetual non-exclusive license
arXiv:2607
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/abs/2607.28847v1
We develop an effort-centric framework that measures an applicant's effort as the minimum weighted cost of feasible changes required to cross the approval boundary
https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release)
Synthetic risk–neutral generator
https://arxiv.org/list/econ/new
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New submissions (showing 21 of 21 entries)
[1] arXiv:2608
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/list/math/new
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New submissions (showing 219 of 219 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/list/cs.IT/new
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New submissions (showing 11 of 11 entries)
[1] 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/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/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/pdf/1811.12628
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https://arxiv.org/list/cs.GT/new
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New submissions (showing 6 of 6 entries)
[1] arXiv:2608