Tommy McMichen | Searcharxiv

arXiv2026

AI Coding Agents Need Better Compiler Remarks

Modern AI agents optimize programs by refactoring source code to trigger trusted compiler transformations. This preserves program semantics and reduces source code pollution, making the program easier to maintain and portable across architectures. However, this collaborative workflow is limited by legacy compiler interfaces, which obscure analysis behind unstructured, lossy optimization remarks that have been designed for human intuition rather than machine logic. Using the TSVC benchmark, we evaluate the efficacy of existing optimization feedback. We find that while precise remarks provide actionable feedback (3.3x success rate), ambiguous remarks are actively detrimental, triggering semantic-breaking hallucinations. By replacing ambiguous remarks with precise ones, we show that structured, precise analysis information unlocks the capabilities of small models, proving that the bottleneck is the interface, not the agent. We conclude that future compilers must expose structured, actionable feedback designed specifically for the future of autonomous performance engineering.

cs.PL↗

arXiv2024

Getting a Handle on Unmanaged Memory

The inability to relocate objects in unmanaged languages brings with it a menagerie of problems. Perhaps the most impactful is memory fragmentation, which has long plagued applications such as databases and web servers. These issues either fester or require Herculean programmer effort to address on a per-application basis because, in general, heap objects cannot be moved in unmanaged languages. In contrast, managed languages like C# cleanly address fragmentation through the use of compacting garbage collection techniques built upon heap object movement. In this work, we bridge this gap between unmanaged and managed languages through the use of handles, a level of indirection allowing heap object movement. Handles open the door to seamlessly employ runtime features from managed languages in existing, unmodified code written in unmanaged languages. We describe a new compiler and runtime system, ALASKA, that acts as a drop-in replacement for malloc. Without any programmer effort, the ALASKA compiler transforms pointer-based code to utilize handles, with optimizations to reduce performance impact. A codesigned runtime system manages this level of indirection and exploits heap object movement via an extensible service interface. We investigate the overheads of ALASKA on large benchmarks and applications spanning multiple domains. To show the power and extensibility of handles, we use ALASKA to eliminate fragmentation on the heap through compaction, reducing memory usage by up to 40% in Redis.

cs.PL↗