Frontier & Experimental Concepts in AI Research

Agentic AI

Systems that can autonomously plan, reason, and act over extended sequences of tasks, often integrating memory, reflection, and tool use.

AI systems designed to generate, run, and evaluate their own experiments, accelerating the scientific process itself.

Feedback mechanisms where models evaluate and improve their own outputs using critique, re-ranking, or reinforcement signals.

A prompting and training paradigm that encourages models to generate intermediate reasoning steps for more reliable conclusions.

Expansions of chain-of-thought that maintain branching or structured reasoning paths explored in parallel.

Architectures that incorporate persistent or external memory modules, allowing long-term knowledge accumulation.

Models that dynamically invoke external tools, APIs, or code interpreters to perform computations beyond their internal weights.

An approach where models write and execute small snippets of code to solve reasoning or math problems more reliably.

Multi-component systems where a model generates goals, decomposes them into subtasks, and iteratively executes them.

Models capable of reasoning over tens of thousands or millions of tokens through sparse attention, retrieval, or segment recurrence.

Models that learn an internal simulation of an environment to reason about future states, central to embodied AI and autonomous planning.

Automated discovery of new network structures using optimization or evolutionary algorithms rather than human design.

Models that generate the weights of other models, allowing dynamic adaptation and meta-learning.

Dynamical-systems-inspired architectures with continuous-time neurons that adapt in real time to changing inputs.

Self-organizing models that learn local update rules leading to emergent global behaviors and patterns.

Complex, unpredictable abilities that arise spontaneously when models reach sufficient scale or diversity in training data.

Using AI models to generate new, high-quality training data, often closing data gaps or creating rare examples.

Agents that invent their own tasks or goals for continual exploration without explicit external rewards.

Research into how societies can regulate, verify, and coordinate the development of increasingly capable AI systems.

New model architectures created with transparent, human-readable reasoning processes from the start.

Research into automatically mapping neurons or attention heads to human-interpretable concepts.

Integrating reasoning, memory, and perception into unified systems inspired by human cognition.

Agents that learn through physical or simulated interaction with the real world — combining robotics, perception, and control.

The ultimate goal: systems that can perform any intellectual task a human can, adapt across domains, and continually self-improve.