Google Research’s "Titans" introduces a neural long-term memory module that dynamically learns to memorize and retrieve context during inference, addressing Transformers' limitations with fixed-length attention spans. By leveraging a biologically inspired "surprise metric," Titans prioritize significant information, scaling efficiently to sequences over 2 million tokens without quadratic complexity. Its three architectural variants—Memory as Context, Gate, and Layer—demonstrate strong performance in long-range reasoning tasks such as medical diagnostics and legal analysis, positioning Titans as a compelling alternative for handling extended contexts in neural models.