Large Language Models (LLMs) have recently shown promise in compiler optimizations such as loop vectorization and memory access restructuring. However, due to their generative nature, LLM-optimized code may contain syntax errors or semantic inconsistencies. While state-of-the-art compilers using LLMs employ symbolic verification to ensure correctness, they fail to fully utilize LLM-based optimizations due to the limited and unreliable verification coverage. This work introduces CoV, a compiler-runtime co-operative Chain of Verification framework that safely integrates LLM-based code transformations into modern compilation workflows. CoV employs a multi-stage verification pipeline that begins with lightweight static checks such as syntax validation and profiling-based checksum filtering, and then applies symbolic equivalence verification using tools like Alive2. For code fragments that cannot be statically verified, CoV inserts runtime verification mechanisms to ensure correctness during execution. These runtime checks are optimized through verification parallelization and batching to minimize overhead. This work implements a prototype CoV framework atop an LLM-based automatic vectorizer within LLVM, and evaluates it using 151 loops in the TSVC benchmark suite and three realistic applications. CoV expands vectorization coverage by 13.9% and 10.6% over LLVM and GCC -O3 vectorization, respectively. In addition, CoV successfully vectorizes loops in three realistic applications that are not handled by the -O3 vectorization.

Thanks to the computation ability on encrypted data, fully homomorphic encryption (FHE) is an attractive solution for privacy-preserving computation. Despite its advantages, FHE suffers from limited applicability in small programs because repeated FHE multiplications deplete the level of a ciphertext, which is finite. Bootstrapping reinitializes the level, thus allowing support for larger programs. However, its high computational overhead and the risk of level underflow require sophisticated bootstrapping placement, thereby increasing the programming burden. Although a recently proposed compiler automatizes the bootstrapping placement, its applicability is still limited due to lack of loop support.

Fully homomorphic encryption (FHE) offers a promising solution for privacy-preserving cloud computing by allowing cloud servers to compute on encrypted data without decryption. However, its applicability is limited by the programming burden of ciphertext management and considerable operational latency. Recently proposed FHE compilers automate ciphertext management, but they transform all data into ciphertexts without filtering private data, thus unnecessarily increasing FHE ciphertexts and the overall latency. This work introduces a new privacy-authority type, called privacy-set (PSet), that allows programmers to annotate authorized devices for each unit of private data. Moreover, this work proposes a new privacy authority-aware compiler that automatically transforms a PSet-annotated plain program into an FHE-enabled edge-cloud cooperative program with operation authority- and latency-aware partitioning. This work evaluates the PSet compiler with six machine learning and deep learning applications, and demonstrates that the PSet compiler performs 4.92 times faster than the existing FHE compilers that do not support edge-cloud partitioning.