From Static to Adaptive Inference: A Survey on Test‐Time Learning

ABSTRACT Modern artificial intelligence (AI) systems are usually deployed under a static inference paradigm in which, after training, a model processes test inputs through fixed forward computation. This assumption becomes limiting when models encounter distribution shifts, long contexts, interactive sessions, user‐specific patterns, or changing environments. Test‐Time Learning (TTL) offers a complementary perspective by allowing a deployed model to use information available during inference to adapt parameters, statistics, representations, states, memory, prompts, or context before or during prediction. This survey provides an introductory and unified account of TTL, beginning with the original Test‐Time Training (TTT) formulation, incorporating Test‐Time Adaptation (TTA), and broadening it into a general paradigm of adaptive inference. Existing methods are organized according to the adaptation target, representative applications are reviewed in computer vision, natural language processing and large language models (LLMs), speech and signal processing, medical and scientific AI, and robotics, and key challenges are discussed in efficiency, stability, objective alignment, safety, privacy, and evaluation standards. By connecting early self‐supervised test‐time updates with recent state‐ and memory‐oriented architectures such as TTT layers and Titans, this survey highlights TTL as a step towards adaptive foundation models whose inference process can continue to learn under controlled deployment conditions, practical constraints, and governance requirements.