ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks

RESUMO

Coding agents are typically evaluated with desired behavior specified through issues or instructions. In practical web development, however, agents may need to infer behavior from working software and implement it in an incomplete application. We introduce ProgramDistill, a benchmark evaluating coding agents on features discovered through interaction with fully functional reference applications. We build ProgramDistill by factorizing applications into features of different granularities, each associated with replayable behaviors executable via its gold patch. Our pipeline, mine-craft-patch, discovers 1,975 replay-verified behaviors across 26 applications constructs 4,063 tasks without human intervention. Across nine frontier coding agents, GPT-6 and Astra and Claude Opus 5 achieve 49.2% and 28.8% success on cumulative workflows for full-application reconstruction. In partial-application reconstruction, success falls from 100% to 64.0% and 96% to 32% as restoration depth increases from 1 to 8. ProgramDistill thus provides a scalable benchmark with controlled difficulty for evaluating coding agents on a natural basis for future curriculum-based training.