Adaptive reasoning within AI refers to methods and capabilities that allow models to generate, revise, and apply multi-step inferences dynamically in response to new information or changing objectives. It encompasses building internal representations (models, abstractions, or symbolic structures), selecting appropriate reasoning strategies (deduction, induction, abduction, analogical transfer, causal inference), and adapting those strategies through meta-reasoning or learning. In practice this means combining perception and memory with mechanisms for planning, hypothesis formation and testing, and uncertainty management so that an agent can, for example, reformulate a plan when partial observations contradict expectations, generalize solutions across domains, or produce interpretable stepwise explanations for decisions. Algorithmically, adaptive reasoning sits at the intersection of probabilistic programming, neuro-symbolic systems, meta-learning, model-based RL, graph and relational neural networks, and recent advances in prompting and chain-of-thought in large language models; it addresses both online adaptation (continual learning, belief revision) and offline transfer (few-shot generalization, causal discovery) while posing evaluation challenges around robustness, calibration, and interpretability.
First documented uses of the phrase trace back to cognitive-science and education literature in the late 20th century, with AI-specific uses appearing in research papers from the 1990s; the concept gained broader traction in the AI community in the 2010s with renewed interest in meta-learning and model-based planning and surged again from 2020–2024 alongside advances in large-scale pretrained models, chain-of-thought prompting, and neuro-symbolic hybrids that highlighted flexible, multi-step inference.
Key contributors span cognitive science, symbolic AI, and modern ML: cognitive architectures and adaptive problem-solving (John R. Anderson, ACT-R); formal approaches to inference and causality (Judea Pearl); meta-learning and fast adaptation (Jürgen Schmidhuber, Chelsea Finn); planning and model-based RL (Richard Sutton, David Silver); proponents of neuro-symbolic and hybrid methods (Gary Marcus, Josh Tenenbaum); and industrial research groups driving large-model reasoning capabilities (OpenAI, DeepMind, Google Research, FAIR). These thinkers and teams collectively shaped both theoretical foundations and practical systems that enable adaptive, explainable, and generalizable reasoning in contemporary AI.