arXiv AI
Aug 3
Agentic Harness for Real-World Compilers
arXiv:2603. 20075v2 Announce Type: replace-cross Abstract: Compilers are critical to modern computing, yet fixing compiler bugs is difficult.
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.
Related stories
arXiv AI
Aug 5
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
arXiv:2607. 28587v2 Announce Type: replace-cross Abstract: SWE-bench-like benchmarks are widely used for evaluating LLM's issue resolution capability.
arXiv AI
Jul 2
SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests
arXiv:2607. 00990v1 Announce Type: cross Abstract: Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories.
arXiv AI
Jul 3
Understanding Agent-Based Patching of Compiler Missed Optimizations
arXiv:2607. 02370v1 Announce Type: cross Abstract: Compiler missed optimizations refer to cases in which compilers failed to optimize certain code.
arXiv AI
Jun 6
Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation
arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.
arXiv AI
Jun 9
RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models
arXiv:2606. 08976v1 Announce Type: new Abstract: LLM-based RTL generation and reasoning is a promising direction for hardware design automation.
arXiv AI
Jun 30
Diff-Based Code Corruption using LLMs for Large-Scale Bugfix Benchmarking
arXiv:2606. 29088v1 Announce Type: cross Abstract: There are various benchmarks to evaluate bugfixing capabilities of Large Language Models.
arXiv AI
Jun 9
Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs
arXiv:2606. 08840v1 Announce Type: new Abstract: Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance varies across programming languages, problem families, and failure modes.
arXiv AI
Aug 5
Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?
arXiv:2608. 03983v1 Announce Type: cross Abstract: Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed program representation.
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.
arXiv AI
1d ago
T-LLM Compiler: Trusted LLM-based Code Optimization and Verification Framework
arXiv:2608. 14953v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened opportunities to apply high-level code transformations to the field of code optimization, and it has since emerged as one of the most fundamental tasks for LLMs to perform; however, at present, LLMs struggle to apply wide-ranging code optimization tasks due to both the complexity of the code and the inability to independently verify the correctness of the transformations.
arXiv AI
Jun 2
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.