> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mindlab.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Glossary

> A glossary of core concepts for the MindLab platform.

* **Adaptive Computation:** The ability of a system, such as a Heterogeneous Mixture-of-Experts model, to dynamically allocate computational resources based on the complexity of the input.
* **Agentic Orchestration Layer:** The emerging layer in the AI technology stack responsible for managing and coordinating complex, multi-step workflows across a heterogeneous workforce of AI agents.
* **AI Bill of Materials:** A formal record of the components, dependencies, and training data used to build an AI model or agent. In MindLab, the Capsule manifest serves this function.
* **All-to-All Communication:** The collective communication primitive where every device in a distributed system sends a subset of its data to every other device. This is the primary communication pattern for expert parallelism in MoE models.
* **Argumentation:** A reasoning process based on constructing and comparing pro and con arguments, rather than propagating numeric certainty factors.
* **Capsule (Agent Pack):** A self-contained, version-controlled, and distributable unit of work that encapsulates a team of specialized AI agents, their associated playbooks, governance policies (defined in a **Model Card**), and evaluation suites.
* **Chain-of-Thought (CoT) Prompting:** A technique for improving the multi-step reasoning abilities of LLMs by providing demonstrations that include a series of intermediate reasoning steps.
* **Co-Intelligence:** A term coined by Ethan Mollick to describe the optimal human-AI relationship, where AI acts as a partner that augments and enhances human expertise.
* **Collective Brain:** A concept from Joseph Henrich describing the distributed network of knowledge, skills, and practices held across a social group. The MindLab Marketplace is designed to create a "collective brain" for an industry.
* **Conditional Computation:** The architectural principle of activating only the most relevant parts of a system for a given task.
* **Context Engineering:** The disciplined practice of designing and managing the context provided to an LLM to ensure reliable and high-quality outputs.
* **Control Problem:** The challenge of ensuring that advanced AI systems remain aligned with human values and intentions.
* **Counterfactual Fairness:** A rigorous, causality-based definition of fairness. An algorithm is counterfactually fair if its decision for an individual would have been the same in a hypothetical world where that individual's protected attributes were different.
* **Direct Preference Optimization (DPO):** A simple, stable, and computationally efficient method for aligning language models with human preferences.
* **Gating Network:** The component in a Mixture-of-Experts system responsible for routing an input to the most appropriate expert(s). In MindLab, the Orchestrator functions as the gating network.
* **Heterogeneous Mixture of Experts (HMoE):** An advanced MoE architecture that uses a portfolio of "expert" sub-networks of varying sizes and capacities.
* **Improvising Mind:** Nick Chater's central thesis that the mind has no hidden depths but is a brilliant improviser, generating thoughts and actions in the moment based on a history of precedents.
* **Jagged Frontier:** A term coined by Ethan Mollick to describe the unpredictable and counter-intuitive landscape of AI capabilities.
* **Knowledge Distillation:** The process of transferring knowledge from a large "teacher" model to a smaller "student" model.
* **Livewired:** A term coined by David Eagleman to describe the brain as a dynamic, self-reconfiguring system whose physical structure is constantly being shaped by experience.
* **Model Card:** A standardized documentation framework for reporting the performance, intended uses, and limitations of a trained AI model.
* **NIST AI RMF:** The Artificial Intelligence Risk Management Framework developed by the U.S. National Institute of Standards and Technology.
* **Orchestrator:** The user's personal, persistent, and stateful AI assistant. It serves as the single point of interaction and governance.
* **Scaling Laws:** A set of power laws that govern the performance of Transformer language models as a function of model size, dataset size, and training compute.
* **SpanBERT:** A pre-training method that improves performance on span-selection tasks by masking contiguous spans of text and using a Span-Boundary Objective (SBO).