Martin Hirzel

Evolved harnesses encode a shared abstract playbook that adapts to language-specific challenges, revealing a nuanced interplay between model limitations and engineering demands.

Uncertainty in LLM outputs can now be quantified and managed seamlessly, transforming how developers build reliable AI applications.

Java developers drowning in unfixed bugs, rejoice: automated reproduction test generation is now a viable option, thanks to a new benchmark and adapted generator.

Turns out, LLM agents are surprisingly bad at following plans, often preferring their own internalized (and potentially flawed) workflows, and a bad plan is worse than no plan at all.

Java codebases can now get state-of-the-art automated issue resolution thanks to iSWE Agent, which outperforms existing LLM agents by combining rule-based static analysis with LLMs.