arXiv AI June 2, 2026 By Yafan Huang, Sheng Di, Guanpeng Li

Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference

arXiv:2606. 02430v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through diverse perspectives such as code generation and domain-specific decision-making.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Sep 24

Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark

The paper introduces SWE-Flux, a repository‑level benchmark designed to test large language models’ ability to reason about runtime behavior. It contains 480 execution‑grounded instances from 12 real Python repositories, with gold answers automatically harvested from instrumented test executions. Evaluation of five LLMs shows the task remains difficult, with the best model achieving only 37% accuracy, and the benchmark can generate challenging variants through input perturbation.

By Hamed Taherkhani, Mohammad Abdollahi, Melika Sepidband, Hridya Dhulipala, Tien N. Nguyen, Hadi Hemmati

llms benchmarks

arXiv AI

Jul 2

LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution

arXiv:2607. 00700v1 Announce Type: cross Abstract: LLVM is a widely used compiler infrastructure whose scale and complexity make issue resolution labor-intensive and challenging.

By Zhao Tian, Yingquan Zhao, Chenyao Suo, Meng Wang, Junjie Chen

llms agents benchmarks

arXiv AI

Jul 14

SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks

arXiv:2507. 11059v3 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset.

By Pavel Adamenko, Mikhail Ivanov, Aidar Valeev, Rodion Levichev, Pavel Zadorozhny, Ivan Lopatin, Dmitry Babaev, Alena Fenogenova, Valentin Malykh

llms agents benchmarks

arXiv AI

Aug 11

PluginEval: A Diagnostic Benchmark for Fine-Grained Error Attribution in Function Calling

arXiv:2608. 08700v1 Announce Type: new Abstract: Reliable evaluation of tool routing is critical as Large Language Models increasingly operate as autonomous agents.

By Dongjie Xu, Julius, Hanchi Dong, Minghua Tang, Yuxuan Sun, Ziwei Nie, Zicheng Liu, Dujun Qing, Jiajie Xu

llms agents benchmarks safety

arXiv AI

Aug 13

Program Semantic Inequivalence Game with Large Language Models

arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.

By Antonio Valerio Miceli-Barone, Vaishak Belle, Ali Payani

llms agents fine-tuning benchmarks

arXiv AI

Jun 24

VeriPilot: An LLM-Powered Verilog Debugging Framework

arXiv:2606. 23759v1 Announce Type: cross Abstract: Verilog debugging remains one of the most time-consuming stages in digital circuit design.

By Yihan Wang, Cheng Liu, Jiazheng Zhang, Lei Zhang, Long Cheng, Xiaowei Li, Huawei Li

llms benchmarks

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Related stories

Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark

LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution

SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks

PluginEval: A Diagnostic Benchmark for Fine-Grained Error Attribution in Function Calling

Program Semantic Inequivalence Game with Large Language Models

VeriPilot: An LLM-Powered Verilog Debugging Framework