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New MLIRBench benchmark tests LLM reasoning over compiler IRs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MLIRBench, a new benchmark designed to evaluate how well large language models understand and reason about programs written in the Multi Level Intermediate Representation (MLIR). The benchmark consists of 1,768 validated program pairs, generated using a multi-agent pipeline and categorized as EQUIV, DIVERGE, or ERROR. In evaluations, Gemini 3.1 Pro demonstrated the highest accuracy at 92.65% on hard labels, but its aggregate and behavior scores indicated a gap in predicting detailed execution semantics, highlighting a need for improved LLM reasoning over compiler intermediate representations. AI

IMPACT Highlights a gap in current LLM reasoning over compiler IRs, potentially guiding future research in program repair and semantic classification.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating LLM reasoning capabilities on compiler intermediate representations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MLIRBench benchmark tests LLM reasoning over compiler IRs

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The cluster contains a research paper introducing a new benchmark for evaluating LLM reasoning capabilities on compiler intermediate representations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Pranava Sai Maganti, Meghasyam Peddireddy, Muzakker Hossain, Kaining Gao, Farhan Ahmed, Ali Jannesari ·

    MLIRBench - A Benchmark and Dataset for Compiler and Hardware-Aware Reasoning

    arXiv:2611.14881v1 Announce Type: cross Abstract: In recent years, large language models (LLMs) have become increasingly popular in software engineering and have performed well on different programming tasks. However, it is still difficult to know whether these models truly under…

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