The Recursive Think-Answer Process (R-TAP) improves the reasoning ability of large language models (LLMs) and vision-language models (VLMs) by introducing iterative reasoning cycles and a confidence-generating mechanism. This approach leads to more accurate results and reduces self-reflective errors, outperforming traditional single-pass inference methods.