The paper introduces a configuration-first framework called LOCALIZE that streamlines machine learning experimentation by declaring experiments in human-readable configuration files and orchestrating isolated processes for each workflow stage. It version‑controls code, data, configurations, and artifacts, enabling reproducible runs. A qualitative comparison with five platforms and quantitative tests against Jupyter and Kedro show that LOCALIZE reduces code edits while keeping time and memory usage comparable, and scales sublinearly with dataset size.
By Tim Strnad (Jo\v{z}ef Stefan Institute, Slovenia), Bla\v{z} Bertalani\v{c} (Jo\v{z}ef Stefan Institute, Slovenia), Carolina Fortuna (Jo\v{z}ef Stefan Institute, Slovenia)
HoliBench is a modular benchmarking and deployment toolkit that jointly measures accuracy, latency, and energy for foundation models across a wide range of devices, from single-board computers to GPU servers. It provides a platform abstraction layer that calibrates cross-device measurements and supports multiple model modalities, inference engines, and quantization levels. Using HoliBench, the authors evaluated 20 models on 7 device types, revealing tradeoffs such as limited latency gains from quantization on low‑precision hardware and diminishing accuracy returns relative to energy consumption, while also showing that single-model profiles can predict multi-model pipeline performance within a few percent.
By Inesh Chakrabarti, Zejun Xiong, Pragya Sharma, Mani Srivastava
arXiv:2607. 16617v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate data-processing workflows, yet coding agents typically produce scripts that are not automatically materialized as persistent, editable platform artifacts.
By Runming He, Zhen Hao Wong, Hao Liang, Zimo Meng, Chengyu Shen, Xiaochen Ma, Wentao Zhang
arXiv:2601. 19568v2 Announce Type: replace Abstract: Code localization constitutes a key bottleneck in automated software development pipelines.
By Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
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
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
arXiv:2606. 17461v1 Announce Type: cross Abstract: Fine-grain clock gating (FGCG) is among the most effective techniques for reducing dynamic power, yet current FGCG optimization flows remain largely manual.
By Yiting Wang, Chenhui Deng, Chia-Tung Ho, Yanqing Zhang, Zhuo Feng, Cunxi Yu, Ang Li, Gang Qu, Brucek Khailany
arXiv:2511. 15503v3 Announce Type: replace-cross Abstract: High-performance Host processors can integrate Processing-In-Memory (PIM) devices, which can accelerate memory-intensive kernels of Machine Learning (ML) models, including Large Language Models (LLMs), by leveraging the large memory bandwidth available at PIM cores.
By Peiming Yang, Sankeerth Durvasula, Ivan Fernandez, Mohammad Sadrosadati, Onur Mutlu, Gennady Pekhimenko, Christina Giannoula
arXiv:2606. 00162v1 Announce Type: cross Abstract: Robotic systems generate large volumes of multimodal sensor data, but converting ROS bag recordings into machine learning datasets is often handled by ad hoc sequential scripts, creating engineering overhead and slow iteration cycles.
By Leon Pohl, Lukas Beer, George Sebastian, Mirko Maehlisch
arXiv:2607. 22595v1 Announce Type: new Abstract: Mechanistic interpretability (MI) has emerged as a powerful approach for analyzing and intervening in inference computations, with a growing number of applications such as jailbreak attempt detection, truthfulness evaluation, and hallucination detection.
By Michael Blum, Mark Silberstein, Yaniv David
arXiv:2606. 03852v1 Announce Type: cross Abstract: Large language models often generate code with bugs.
By Yinsheng Yao, Hongxiang Zhang, Weixi Tong, Tianyi Zhang
The paper proposes DCO, a dynamic cache orchestration scheme for multi-core AI accelerators that uses application-aware policies and dataflow information to guide cache replacement, bypass decisions, and thrashing mitigation. Using a cycle-accurate simulator, the authors demonstrate up to 1.80× speedup over conventional cache architectures and validate the approach with an analytical model and RTL implementation. The design occupies 0.064 mm² on a 15 nm process and operates at 2 GHz, showing that a shared system-level cache can simplify programming while boosting performance for large language model workloads.
By Zhongchun Zhou, Chengtao Lai, Yuhang Gu, Wei Zhang