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[2608.01651] Bole: Efficient Tree Speculation for Hybrid-Attention

https://arxiv.org/abs/2608.01651
Tree speculative decoding offers an attractive acceleration path, but existing tree-speculation systems are designed around the key--value caches of full-attention models

Computer Science > Computation and Language

https://arxiv.org/abs/2609.14858
The driver of this process is effective exploration, however, managing and improving exploration strategies remains a major bottleneck

Physics > Computational Physics

https://arxiv.org/abs/1202.3424
, dust particles), but may also be 1-d or 2-d periodic arrays of "target unit cells", which can be used to study absorption, scattering, and electric fields around arrays of nanostructures

Computer Science > Programming Languages

https://arxiv.org/abs/2504.02246
02246 (cs) Title:C*: Unifying Programming and Verification in C Abstract:Ensuring the correct functionality of systems software, given its safety-critical and low-level nature, is a primary focus in formal verification research and applications

Statistics Theory

https://arxiv.org/list/math.ST/new
| all New submissions (showing 6 of 6 entries) [1] arXiv:2608

Distributed, Parallel, and Cluster Computing

https://arxiv.org/list/cs.DC/new
SE) Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early

A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for

https://arxiv.org/html/2508.20101v1
The model combines classical GARCH(pp, qq) dynamics with spatially correlated innovations and spatially varying parameters, estimated using local likelihood methods

Operating Systems

https://arxiv.org/list/cs.OS/new
A major source of this nondeterminism is batch-dependent GPU execution: dynamic input shapes change kernel tiling and floating-point reduction orders

Reviving Micro Real Rigidities: The Importance of Demand ShocksThe views

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

Hardware Architecture

https://arxiv.org/list/cs.AR/new
Our analysis shows that, despite its limited write performance, HBF can significantly improve the batch size, throughput, and flexibility of LLM-serving systems while reducing the minimum GPU requirements, but realizing these benefits critically depends on sustaining HBM-comparable read bandwidth and requires

Electrical Engineering and Systems Science

https://arxiv.org/list/eess/new
To establish their well-posedness, the kernel domain is partitioned along characteristic lines into a finite number of triangular subregions, and the kernel solution is constructed successively in the preceding subregion to the next one

Machine Learning Dec 2022

https://arxiv.org/list/cs.LG/2022-12
Aakur, Priyadharsini Ramamurthy, Arunkumar Bagavathi, Vishalini Ramnath, Akhilesh Ramachandran Comments: Expanded version of arXiv:2111

Machine Learning Dec 2024

https://arxiv.org/list/cs.LG/2024-12
Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Machine Learning Authors and titles for December 2024 Total of 3298 entries : 1-50 51-100 101-150 151-200 ... 3251-3298 Showing up

1 Introduction

https://arxiv.org/html/2407.02518v2
We observed that our approach can provide an advanced level of critiques of both safety and helpfulness analysis, significantly improving the quality of output codes (+10%percent10+10\%+ 10 % absolute improvements in all models)

Instrumentation and Methods for Astrophysics

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

Economics

https://arxiv.org/list/econ/new
The paper develops a mechanism-design model showing that the same delegated intermediation structure can generate both Stiglitz-Weiss underinvestment and De Meza-Webb overinvestment distortions

Numerical Analysis

https://arxiv.org/list/cs.NA/new
all New submissions (showing 18 of 18 entries) [1] arXiv:2608

Solar and Stellar Astrophysics

https://arxiv.org/list/astro-ph.SR/new
per page: fewer | more | all New submissions (showing 19 of 19 entries) [1] arXiv:2608

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

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

Cryptography and Security

https://arxiv.org/list/cs.CR/new
fewer | more | all New submissions (showing 32 of 32 entries) [1] arXiv:2608

AgentScope 1.0: A Developer-Centric Framework for Building Agentic

https://arxiv.org/html/2508.16279v1
Such advancements have laid a robust foundation for developing powerful LLM-based agent applications that can effectively interface with the world through a variety of tools, to perform diverse and complex tasks with increased autonomy and precision (Qin et al

Quantum Physics

https://arxiv.org/list/quant-ph/new
isolated systems), and by (iii) clearly distinguishing the state of a quantum system from the knowledge of conscious observers about this state, we suggest a modified Copenhagen interpretation of quantum mechanics that resolves Wigner's Friend and extended Wigner's Friend paradoxes

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

Stochastic Games with Minimally Bounded Action Costs

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

Homogeneous Distributed Observers for Quasilinear Systems

https://arxiv.org/html/2407.05763v1
fr Inria, Centrale Lille, Université de Lille, CNRS, CRISTAL, France

Mathematics

https://arxiv.org/list/math/new
Their finite iterates can develop rapidly increasing numbers of linear pieces, making them a natural test case for the expressive power of deep neural networks