Researchers propose Test-Time Self-Evolving via Reflection (TTSR), a self-evolving framework for adapting large language models during inference using unlabeled test inputs. TTSR improves upon existing methods by addressing bottlenecks in learnable samples and efficient exploration. The framework alternates between a Student and Teacher role, with the Teacher analyzing failed trajectories and synthesizing targeted questions. Experiments show consistent test-time improvements and strong cross-backbone generalization.