IRIS-14B is the first LLM trained explicitly for GIMPLE-to-LLVM IR translation and outperforms much larger models by up to 44 percentage points on real-world C code.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 2polarities
background 2representative citing papers
Rover uses a new Multi-layer Code Property Graph and clustering to supply LLMs with dependency-aware contexts, outperforming standalone LLMs, MergeGen, and WizardMerge on similarity to ground-truth conflict resolutions.
SPEC CPU2026 increases instruction volume and memory footprint while shifting pressure to instruction-cache bottlenecks; 4-5 workload subsets per group preserve 96.4-99.9% of full-suite behavior and show complementary traits to DCPerf and MLPerf.
citing papers explorer
-
LLM Translation of Compiler Intermediate Representation
IRIS-14B is the first LLM trained explicitly for GIMPLE-to-LLVM IR translation and outperforms much larger models by up to 44 percentage points on real-world C code.
-
Rover: Context-aware Conflict Resolution with LLM
Rover uses a new Multi-layer Code Property Graph and clustering to supply LLMs with dependency-aware contexts, outperforming standalone LLMs, MergeGen, and WizardMerge on similarity to ground-truth conflict resolutions.
-
SPEC CPU2026: Characterization, Representativeness, and Cross-Suite Comparison
SPEC CPU2026 increases instruction volume and memory footprint while shifting pressure to instruction-cache bottlenecks; 4-5 workload subsets per group preserve 96.4-99.9% of full-suite behavior and show complementary traits to DCPerf and MLPerf.