AICORE: AI-Native Compilation through Compiler–Agent Co-Design

LLM-based systems for optimizing compiled C/C++ programs typically operate at either the source level or the compiler level. Treating these decisions separately overlooks their coupling: a promising source rewrite can change backend behavior, while compiler-only search cannot introduce transformations that require high-level algorithmic reasoning. Existing compiler interfaces further complicate integration by scattering optimization feedback across low-level diagnostics, intermediate representations, and configuration options. We present AICore, an agent–compiler co-design framework built around an AI-native compiler interface. The interface identifies active code regions, organizes source-linked optimization remarks, pass traces, and IR excerpts into structured reports, and connects this feedback to three action types: source rewrites, loop-local pragmas, and supported compiler parameters. AICore searches these actions jointly through a shared compile–validate–measure loop. A candidate is retained only if it builds, matches the reference output, and improves performance; after each accepted change, the compiler feedback is refreshed from the new program. On 30 PolyBench/C kernels and 29 cBench programs, AICore achieves arithmetic-mean speedups of $4.49\times$ and $1.30\times$ over -O3, respectively, with a 100.00% validation pass rate on both suites. It exceeds our AutoPass reimplementation, instrumentation-based PGO, and OpenTuner in mean speedup on both suites. OpenCode achieves higher means of $6.33\times$ and $2.20\times$, but passes validation on only 76.67% and 86.21% of the programs. On PolyBench, the full system also exceeds the tested rewrite-only, parameter-only, and no-compiler-evidence variants. Validation establishes output agreement only for the evaluated executions.