The field of language models is moving towards the development of more reflective and self-aware models. Recent research has focused on improving the ability of language models to recognize and correct their own errors, as well as to generate more accurate and informative responses. One of the key directions in this area is the development of self-reflective generation algorithms, which allow models to reflect on their own output and correct errors before generating new text. Another important area of research is the development of frameworks for evaluating and improving the self-awareness of language models, including their ability to recognize their own strengths and weaknesses. Notable papers in this area include: Self-Reflective Generation at Test Time, which proposes a lightweight test-time framework for self-reflective generation. MedReflect, which introduces a framework for teaching medical language models to self-improve via reflective correction. Moral Anchor System, which proposes a novel framework for detecting and mitigating value drift in AI agents.