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.
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.
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arXiv AI
Aug 11
Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis
arXiv:2509. 21629v4 Announce Type: replace-cross Abstract: Program verification relies on loop invariants, yet automatically discovering strong invariants remains a long-standing challenge.
arXiv Machine Learning
Sep 17
CompileRover: Revolutionizing Virtual Machine Compiler Optimization with a Tri-Role LLM-Driven Framework
CompileRover is a new optimization framework for virtual machine compilers that uses a tri‑role LLM‑driven collaboration mechanism involving a referee, an advisor, and an operator. It tackles common issues such as redundant computations, inefficient loops, and suboptimal function implementations by applying control‑flow analysis, code‑structure transformations, and dynamic execution pattern recognition. Benchmark evaluations show that CompileRover consistently outperforms existing virtual machine compilers, reducing execution overhead, improving data‑flow consistency, and enhancing overall compiler performance.
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.
arXiv AI
Jun 19
AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning
arXiv:2606. 20373v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for code compilation tasks, but applying them to runtime performance tuning is difficult due to complex microarchitectural effects and noisy runtime measurements.
arXiv AI
3d ago
Self-Spec Verifiable Code Generation
arXiv:2609.39568v1 Announce Type: cross Abstract: Large language models (LLMs) may generate unreliable code on corner cases missed by testing, while formal verification can provide machine-checkable...
arXiv Machine Learning
Sep 10
Robustness of LLM-Generated SystemVerilog Assertions to Semantics-Preserving RTL Transformations
The paper evaluates how robust large language models are at generating SystemVerilog Assertions (SVA) when the underlying RTL code undergoes semantics‑preserving transformations such as operand reordering, identifier renaming, and redundant parenthesization. Using a curated dataset and two open‑source models (Qwen2.5‑Coder‑7B and DeepSeek‑Coder‑V2‑Lite), the authors find that 9.7%–27.0% of behaviors that were correct on the original RTL become incorrect after transformation, revealing significant instability that aggregate accuracy metrics can hide.
arXiv AI
Sep 15
Efficiency Hallucination: Formalizing and Measuring Behavioral Calibration in LLM-Based Code Optimization
arXiv:2609.14839v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into automated code optimization introduces a critical reliability risk we term the Efficiency Halluc...
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 16
DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents
arXiv:2511. 20709v2 Announce Type: replace-cross Abstract: Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and secure remains a challenge.
arXiv AI
Aug 11
Effect of Abstractions and Prompting Strategies on LLM-Guided High-Performance Optimizations
arXiv:2608. 08085v1 Announce Type: cross Abstract: Code performance optimization is a vital aspect of modern software development, as it enables faster response times and reduced resource usage.
arXiv AI
Jul 28
ParBench: A Benchmark for Reliable Evaluation of LLM Parallel Code Translation
arXiv:2607. 22588v1 Announce Type: new Abstract: Modern compute-intensive software must migrate across a changing ecosystem of accelerators, programming APIs, compiler stacks, and portability layers, including CUDA, OpenMP, OpenCL, and OpenMP target offload.