Cover Image

High-Throughput SAT Sampling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ardakani, Arash, Kang, Minwoo, He, Kevin, Huang, Qijing, Wawrzynek, John
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Recurrent CircuitSAT Sampling for Sequential Circuits
by: Ardakani, Arash, et al.
Published: (2025)

LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi
by: Ardakani, Mahsa, et al.
Published: (2025)

DEMOTIC: A Differentiable Sampler for Multi-Level Digital Circuits
by: Ardakani, Arash, et al.
Published: (2025)

Self Paced Gaussian Contextual Reinforcement Learning
by: Ardakani, Mohsen Sahraei, et al.
Published: (2026)

Chip Placement with Diffusion Models
by: Lee, Vint, et al.
Published: (2024)

MILP-SAT-GNN: Yet Another Neural SAT Solver
by: Cardillo, Franco Alberto, et al.
Published: (2025)

Graph Neural Thompson Sampling
by: Wu, Shuang, et al.
Published: (2024)

Scalable Option Learning in High-Throughput Environments
by: Henaff, Mikael, et al.
Published: (2025)

TurboAttention: Efficient Attention Approximation For High Throughputs LLMs
by: Kang, Hao, et al.
Published: (2024)

Improving Efficiency of GPU Kernel Optimization Agents using a Domain-Specific Language and Speed-of-Light Guidance
by: Hari, Siva Kumar Sastry, et al.
Published: (2026)

W2SAT: Learning to generate SAT instances from Weighted Literal Incidence Graphs
by: Wen, Weihuang, et al.
Published: (2023)

DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone
by: Singh, Vaibhav, et al.
Published: (2025)

Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity
by: Shrestha, Susav, et al.
Published: (2025)

Neural Approaches to SAT Solving: Design Choices and Interpretability
by: Mojžíšek, David, et al.
Published: (2025)

SATURN: SAT-based Reinforcement Learning to Unleash LLMs Reasoning
by: Liu, Huanyu, et al.
Published: (2025)

HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
by: Li, Yang, et al.
Published: (2023)

Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning
by: Poole, Benjamin, et al.
Published: (2026)

NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
by: Wang, Wenxi, et al.
Published: (2021)

Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
by: Shin, Seunghyeok, et al.
Published: (2026)

GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
by: Zhang, Zhanguang, et al.
Published: (2024)

Distributionally Robust Classification for Multi-source Unsupervised Domain Adaptation
by: Kim, Seonghwi, et al.
Published: (2026)

Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research
by: Lan, Tian, et al.
Published: (2024)

A Comparative Study of Sampling Methods with Cross-Validation in the FedHome Framework
by: Ahmadi, Arash, et al.
Published: (2024)

Actor-Critics Can Achieve Optimal Sample Efficiency
by: Tan, Kevin, et al.
Published: (2025)

Towards Interactive Reinforcement Learning with Intrinsic Feedback
by: Poole, Benjamin, et al.
Published: (2021)

The Road Less Traveled: Enhancing Exploration in LLMs via Sequential Sampling
by: Kang, Shijia, et al.
Published: (2025)

Litespark Technical Report: High-Throughput, Energy-Efficient LLM Training Framework
by: Dade, Nii Osae Osae, et al.
Published: (2025)

Predictive Modeling of Effluent Temperature in SAT Systems Using Ambient Meteorological Data: Implications for Infiltration Management
by: Elkayam, Roy
Published: (2025)

Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions
by: Choi, Jinyoung, et al.
Published: (2025)

The OPS-SAT benchmark for detecting anomalies in satellite telemetry
by: Ruszczak, Bogdan, et al.
Published: (2024)

Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching
by: Dong, Yanhao, et al.
Published: (2025)

On the Sample Complexity of Differentially Private Policy Optimization
by: He, Yi, et al.
Published: (2025)

Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights
by: Huang, Tzu-Heng, et al.
Published: (2025)

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning
by: Lee, Hayeong, et al.
Published: (2026)

Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
by: McClellan, Joshua, et al.
Published: (2024)

Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
by: Narang, Adhyyan, et al.
Published: (2024)

Vertex-Softmax: Tight Transformer Verification via Exact Softmax Optimization
by: Rezazadeh, Navid, et al.
Published: (2026)

Fairness Through Matching
by: Kim, Kunwoong, et al.
Published: (2025)

COSEE: Consistency-Oriented Signal-Based Early Exiting via Calibrated Sample Weighting Mechanism
by: He, Jianing, et al.
Published: (2024)

Simulus: Combining Improvements in Sample-Efficient World Model Agents
by: Cohen, Lior, et al.
Published: (2025)