CL

Continual Learning

Learning from nonstationary streams without forgetting.

SS

Self-Supervised Learning

Representation learning from unlabeled data.

TT

Test-time Learning

Adapting models during inference.

IC

In-Context Learning

Learning from examples within a model's context.

CE

Creative Exploration

Novel generation and open-ended exploration.

ML

Meta-Learning

Learning to learn new tasks from few examples.

MA

Multi-Agent

Interaction and coordination among multiple agents.

LL

Local Learning

Backprop-free and biologically plausible learning rules.

RL

Reinforcement Learning

Learning to act from reward.

CN

Concept Learning

Acquiring and composing concepts from few examples.

HA

Hierarchical Abstraction

Representations across multiple levels of time and granularity.

WM

World Models

Latent predictive models for planning, prediction, and control.

LR

LLM Reasoning

Reasoning, verification, and calibration in language models.

EV

Egocentric Video

Learning from continuous first-person visual streams.

EA

Embodied AI

Agents that perceive and act in physical environments.

MM

Multimodal Learning

Joint learning across vision, language, and other modalities.

FC

Forecasting

Predicting future events from evolving evidence.

HL

Human-like Learning

Learning inspired by human cognitive development.

PA

Philosophy of AI

Foundational questions about AI.

AS

AI Safety

Safe and reliable behavior in learning systems.