Current AI coding tools waste massive amounts of time by waiting until a program is fully written before running it. This old method forces the system to sit idle, alternating between writing code and executing it in separate steps. Researchers have now discovered a way to run code while it is still being generated, effectively hiding that delay from the user.

Unlike humans who constantly edit their work as they type, large language models spit out code tokens one after another without stopping to revise them. This unique behavior creates an opportunity to execute small chunks of code immediately as they appear. The team formalized this approach into a three-stage pipeline involving generation, detection, and execution. Their new system, called Eager, uses advanced techniques like AST-based chunking and dynamic batching with gated execution to run multiple pieces of code simultaneously while catching errors early.

The results are staggering. By executing code in parallel with its creation, the system cuts non-overlapping execution time by nearly 99.9%. Even more importantly, the total time from start to finish drops by up to 55% across seven different AI models and four testing benchmarks. This breakthrough transforms how developers interact with AI assistants, making them significantly faster and more responsive for real-world tasks.

Source: Executing as You Generate: Hiding Execution Latency in LLM Code Generation by Zhensu Sun, Zhihao Lin, Zhi Chen, Chengran Yang, Mingyi Zhou et al., https://arxiv.org/abs/2604.00491