arXiv AI

Regression Language Models for Code

arXiv:2509. 26476v3 Announce Type: replace-cross Abstract: We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages.

arXiv AI

Aug 13

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

arXiv:2608. 12004v1 Announce Type: cross Abstract: In modern AI frameworks, GPU kernels are key to overall system performance.

By Jinjun Huang, Zhongzhen Wen, Tongtong Xu, Meng Yan, Xin Xia, Zhongxin Liu

llms benchmarks

arXiv AI

Jun 4

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

arXiv:2606. 04023v1 Announce Type: cross Abstract: While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.

By Jie Li, Wenzhao Wu, Junqi Hu, Qinrui Zheng, Bowen Wu, Juepeng Zheng, Yutong Lu, Haohuan Fu

llms benchmarks

arXiv AI

Aug 10

Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors

arXiv:2608. 06723v1 Announce Type: cross Abstract: The rapid scaling of Large Language Models (LLMs) has significantly increased computational cost, energy consumption, and inference latency, making accurate estimation essential for sustainable artificial intelligence deployment and hardware-aware design.

By Saeid Shokoufa, Mohammad Erfan Sadeghi, Mehdi Kamal, Massoud Pedram

llms efficiency

arXiv AI

Jul 1

Benchmarking Large Language Models on Floating-Point Error Classification

arXiv:2606. 31308v1 Announce Type: new Abstract: This paper investigates the capability of Large Language Models (LLMs) to detect and classify floating-point errors statically in software code.

By Lisa Taldir (LI-PaRAD), Muhammad Ahmad Saeed (LI-PaRAD), David Defour (LI-PaRAD), Pablo de Oliveira Castro (LI-PaRAD), Eric Petit

llms benchmarks

arXiv AI

Aug 28

Exploring the Role of LLMs in HPC Programming: A Survey

The survey reviews how Large Language Models (LLMs) are being used in High‑Performance Computing (HPC) programming, covering code generation, parallelization, frameworks, evaluation, and broader challenges. It finds that general‑purpose LLMs perform adequately on serial and OpenMP‑style tasks but struggle with distributed MPI workloads, while domain‑specialized models achieve higher accuracy yet are limited in scope and evaluation. The authors argue that LLMs will not replace HPC experts soon but can act as powerful collaborators, provided richer datasets, integration with performance tools, rigorous evaluation, and governance are developed.

By Strahinja Ljaljevic, Josep Jorba, Sergio Iserte

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jun 3

KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators

arXiv:2606. 02963v1 Announce Type: new Abstract: Production inference increasingly targets a heterogeneous mix of accelerators.

By Taras Sereda, Burak Bartan, Ankita Nayak, Tom St. John, Natalie Serrino, Zain Asgar

llms agents efficiency benchmarks

arXiv AI

Aug 11

SuperCoder: Assembly Program Superoptimization with Large Language Models

arXiv:2505. 11480v4 Announce Type: replace-cross Abstract: Superoptimization is the task of transforming a program into a faster one, and ideally the very fastest possible one, while preserving its input-output behavior.

By Anjiang Wei, Tarun Suresh, Huanmi Tan, Yinglun Xu, Gagandeep Singh, Ke Wang, Alex Aiken

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Sep 14

Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs

The paper investigates whether reinforcement‑learning post‑training of code‑generating large language models can be done entirely offline using existing datasets, avoiding costly online code generation and GPU‑CPU communication. Experiments show that a few hours of offline RL can substantially boost zero‑shot code generation performance across models from 0.5 B to 7 B parameters, though the magnitude of improvement differs by model family.

By Abhinav Anand, Sanjana Reddy Pachika, Shweta Verma, Mira Mezini

llms reinforcement-learning

arXiv AI

Jun 16

Mojo: A Promising Tool for Scalable Financial AI Efficiency

arXiv:2606. 16059v1 Announce Type: cross Abstract: For thirty years, quantitative finance has paid a costly two-language tax: models researched in Python are rewritten in C++ for production, often introducing numerical discrepancies.

By Henry Han

llms benchmarks

arXiv AI

Jul 21

KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?

arXiv:2607. 16241v1 Announce Type: cross Abstract: Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench.

By Yunxiang Zhang (Xiangjun), Ping Yu (Xiangjun), Jianyu Wang (Xiangjun), Max (Xiangjun), Fan, Julian Reed, Azalia Mirhoseini, Will Su

llms benchmarks

arXiv Machine Learning

Jun 8

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs

arXiv:2606. 06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers.

By Ziyue Li, Yang Li, Tianyi Zhou

llms benchmarks

arXiv AI

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning benchmarks

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