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Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early
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https://arxiv.org/list/cs.DC/new
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Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early
https://arxiv.org/html/2511.06451v1
Spectral safety and discrete Green kernel
https://arxiv.org/list/cs.CV/new
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[2] arXiv:2608
https://arxiv.org/list/cs.AI/new
We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults
https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.03089v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/list/cs.NA/new
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New submissions (showing 18 of 18 entries)
[1] arXiv:2608
https://arxiv.org/html/2508.20101v1
In an empirical application, we demonstrate how the model can be applied to financial stock networks
https://arxiv.org/html/2407.20441v1
I Introduction
Training reinforcement learning (RL) algorithms is well-known to be a critically time consuming task that can require several data samples to achieve a desired level of accuracy
https://arxiv.org/html/2608.02817v1
seminar participants at the NBER 2024 SI (ME), UC Berkeley, Boston College, the University of Maryland, the Federal Reserve Board of Governors, the Federal Reserve Banks of Atlanta, Boston, Chicago, and Richmond, ITAM, IMF, Banque de France, BIS, Bocconi, EIEF, CREI, the University of Iowa, Georgetown University, PUC
https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.03118v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.00426v1
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arXiv:2608
https://arxiv.org/html/2608.03545v1
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arXiv:2608
https://arxiv.org/html/2608.03655v1
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arXiv:2608
https://arxiv.org/html/2608.02999v1
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arXiv:2608
https://arxiv.org/html/2608.03153v1
This paper uses South Korea as a hard test of whether large gender gaps originate at labor market entry
https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2407.04889v1
Can such an optimal strategy be computed efficiently
https://arxiv.org/html/2608.03703v1
Dispersing the rebalance through the session, Korea’s chosen remedy, moves the order but not the reference that sizes it and can raise the toll
https://arxiv.org/list/astro-ph.IM/new
In particular, the maximal light deflection of a given Solar System body can be easily estimated for a given observer
https://arxiv.org/html/2607.29442v2
In an asymptotic framework inspired by Rosenbaum2018, we show that the rescaled price process converges to a Bachelier-type semimartingale plus a Gaussian moving average whose kernel is determined explicitly by the trawl function
https://arxiv.org/html/2407.18010v2
We prove the game respects a dynamic programming principle and that the Markov perfect equilibrium can be computed as a limit point of a sequence of Bellman operations
https://arxiv.org/html/2608.02625v1
1-flash model serves as both drafter and refiner, testing whether an existing model can improve its own block autoregressive output through global refinement
https://arxiv.org/html/2608.02909v1
Through simulations, we show that this approach recovers estimates close to the true values, even in relatively small samples, while the standard approach can produce substantial bias in practice
https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2608.00648v1
mdgam uses an extended graph attention mechanism to jointly update node and edge features, and employs multiple decoders to generate task-selection decisions and communication messages