Most AI systems optimize a fixed goal. That’s the problem. They don’t question what they’re solving, so they often produce results that look correct but fail in reality. This is reward hacking. SAGA takes a different approach. Instead of optimizing a static objective, it runs two loops: one generates solutions, the other analyzes failures and rewrites the goal itself. So the system doesn’t just improve answers. It improves what it’s trying to solve. That shift is already producing results: fixing unrealistic drug designs, improving materials under real-world constraints, and boosting DNA performance by nearly 50%. The key insight: the biggest gains don’t come from better solutions, but from better, evolving objectives. #AIResearch #MedicalAI #MachineLearning #AIAgents #AINews