Learning Graph – Web4’s Capability Development Protocol
The missing layer that verifies how humans learn, not just what they complete.
TL;DR
Learning Graph is an open protocol that tracks capability development as temporal, verifiable evolution of understanding. Where Contribution Graph proves what you created, Learning Graph proves how you developed the capability to create it. Together with MeaningLayer’s semantic preservation, they enable systems to distinguish genuine learning from performance theater at scale.
What is Learning Graph?
Learning Graph is not a subtype of existing graph systems. It is a prerequisite for making learning itself verifiable. Other graph-based approaches operate on information. Learning Graph operates on capability.
Most systems treat learning as accumulation and progress as completion. This breaks when AI makes perfect performance frictionless. Without temporal verification of capability persistence, completion becomes indistinguishable from assisted performance that collapses when assistance is removed.
A system can have perfect knowledge representations and still fail to learn. Learning Graph exists because representing information is insufficient for verifying capability.
Learning Graph models learning as dynamic structure: nodes represent concepts, actions, or experiences; edges show influence, dependencies, and capability transfer; evolution captures how understanding develops and persists independently over time. Verification happens through temporal testing when assistance is absent and genuine capability must stand alone.
If a system cannot distinguish between assisted performance and independent capability, it is not a Learning Graph.
The Question Learning Graph Answers
The question Learning Graph answers is not ”what was completed?”, but ”what capability remains when assistance is removed?”
This is not a Graph Neural Network, which is an implementation technique. This is not a Knowledge Graph, which represents static information. Learning Graph is the protocol for tracking verified capability development that survives independently across time and contexts.
Integration with Web4
Learning Graph integrates with MeaningLayer to preserve semantic significance as capability evolves. Where Learning Graph tracks structural development, MeaningLayer ensures that development retains interpretable meaning. Together, they solve the fundamental challenge: AI can predict behavior without understanding what it signifies. Learning Graph captures evolution. MeaningLayer preserves what it means.
This completes Web4’s verification infrastructure: Portable Identity provides cryptographic ownership, Contribution Graph proves outputs, Learning Graph verifies capability development, and MeaningLayer ensures semantic preservation. The result is learning that is owned, verifiable, portable, and meaningful independent of platform intermediation.
The Canonical Definition
Learning Graph is Web4’s protocol for temporal verification of capability development: the infrastructure that makes learning verifiable as persistent, independent capability rather than assisted performance, enabling systems to distinguish genuine understanding from completion at scale.
The Canonical Sentence
Knowledge Graphs model what is known; Learning Graphs verify how capability develops and persists over time.
Terminological Precision
Throughout this framework, learning refers exclusively to verified, persistent capability development, not exposure, activity, or completion.