We present an agent based automated testing and repair system for verified compilers that contain four kinds of code: verified code, checked code, unverified code, and specification. We present specialized defect detection techniques that exploit the structure present in such compilers. For each surfaced defect the system invokes a coding agent to repair the defect and validate the repair. We evaluate the system on the Axon compiler, a compiler completely generated by a coding agent operating under developer supervision. The compiler was validated during development on a small benchmark set (the Livermore benchmarks), raising the possibility that its coding agent reward hacked the compiler. We also evaluate the possibility that the repair system reward hacked the repairs and find no evidence of reward hacking in either the Axon compiler or the repairs. We present results that characterize the testing and repair effectiveness and discuss repair characteristics.
While autonomous coding agents have significantly advanced automated test generation, they remain fundamentally limited by lazy generation, a phenomenon where agents prematurely terminate tasks and systematically avoid complex programmatic logic, resulting in inadequate code cove…
Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL). Standard execution-based verification requires running unit tests inside per-repository enviro…
Large-scale AI datacenter platforms comprise thousands of heterogeneous hardware components whose validation requires comprehensive fault injection test plans. Today these plans are authored manually: engineers review hardware self-healing validation documents and bills of materi…
Verify-repair loops are a standard means for large language model (LLM) agents to correct faulty plans in code generation, mathematical reasoning, and tool use. When both the verifier and the repairer are noisy, repair can damage already-correct plans, and reported acceptance kee…
Agentic coding assistants are increasingly given extra capabilities, such as browser based testing tools and design oriented system prompts, on the assumption that more capability yields better software. This study tested that assumption directly. Ninety independent agent runs bu…
Large Language Model (LLM)-based Test-Driven Development (TDD) has advanced automated code generation. However, existing approaches depend heavily on human-crafted test cases and cannot operate effectively when only natural-language requirements are available. Although recent wor…