Term tag: Reasoning

Agentic Workflow

An agentic workflow is a design pattern where an LLM plans, uses tools, and iterates in a loop to solve a task, instead of answering in a single shot.

Artificial General Intelligence (AGI)

AGI is AI matching human ability across most cognitive work — and there is no agreed test for it, so the definition you pick decides the answer.

Artificial Superintelligence (ASI)

Artificial superintelligence: what it would mean, why the intelligence explosion argument is contested, and what labs and regulators actually do about it.

Causal Reasoning

Why a model trained on observational data cannot tell cause from correlation, what that costs when you act on it, and what actually identifies one.

Chain-of-Thought (CoT)

A model writing out its reasoning before answering. Once a prompting trick, now trained in by RL, billed as output tokens, and frequently unfaithful.

Computer Use (GUI Agents)

How AI operates software by reading screenshots and issuing clicks and keystrokes: the perception-action loop, why grounding is hard, and what OSWorld measures.

General Problem Solver (GPS)

The 1957 Newell-Shaw-Simon program that solved problems by means-ends analysis, and the b^d arithmetic that stopped it from scaling past puzzles.

Group Relative Policy Optimization (GRPO)

GRPO is PPO without the critic: it scores a group of sampled answers against each other. How it works, what it saves, and where it biases training.

Knowledge Representation

How you encode what a system knows fixes which conclusions are cheap, which are exponential and which are impossible. The expressiveness-tractability trade-off.

Prompt Engineering

Writing the input to a fixed, already-trained language model so it reliably produces the output you want — steering the model, not retraining it.

Reasoning Model

A model trained to think at length before answering. How it differs from a chat model, what the effort dial does, and the tasks where it earns its price.

Reinforcement Learning from Verifiable Rewards (RLVR)

RLVR replaces the learned reward model with a program that checks the answer. What that buys, where it stops working, and how it differs from RLHF.

Test-Time Compute

Spending compute at inference instead of training — how reasoning models buy accuracy with thinking time, and why it moved chip demand into serving.