Learning Graph – Web4’s Capability Development Protocol
The layer that defines learning when completion no longer means capability.
TL;DR
Learning Graph is an open protocol for temporal verification of capability development. 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 defines learning at the only level where verification remains possible. Other graph-based approaches operate on information. Learning Graph operates on capability persistence.
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 verify learning. Learning Graph exists because representing information is insufficient for verifying that capability internalized.
Learning Graph models learning as structure that either persists independently over time — or collapses when assistance is removed.
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 implementing Learning Graph.
The Question Learning Graph Answers
Learning Graph answers the only question that remains meaningful after AI:
What capability still exists when assistance is gone and time has passed?
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 defines learning as persistent, independent capability — rather than assisted performance — enabling systems to distinguish genuine understanding from completion theater at scale.
The Canonical Sentence
Knowledge Graphs model what is known.
Learning Graph verifies 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.
Performance scaled. Learning lost its signal. Persistence remains unfakeable.