Apodex Discovery: Framework for Discoverative AI with Real-World Benchmarks

A recent preprint on arXiv (2608.11341) presents Apodex Discovery, a framework aimed at developing and assessing 'discoverative' AI systems. The authors liken the framework's mission architecture to that of the Apollo program, highlighting the necessity for AI to address significant real-world issues that are not straightforwardly executable or verifiable. At its core is a 'heavy-duty solver' that integrates a foundation model, harness, tools, and control policies to facilitate extensive, stateful, and verifiable inquiries. It incorporates a problem-scouting process that examined 561 industries across 16 sectors, identifying 423 valuable real-world challenges and selecting 20 for initial implementation. Additionally, the framework provides a standardized environment-task-evaluation structure to assess AI capabilities, aiming to transition AI from solving defined tasks to tackling open-ended discovery problems.