ThetaEvolve: Test-time Learning on Open Problems

This article introduces ThetaEvolve, focusing on test-time learning for open problems. The core concept likely involves adapting models during the testing phase to improve performance on unseen data or tasks. The 'open problems' aspect suggests the research tackles challenges where the problem definition or data distribution might shift, requiring adaptability.