Recursive-LD Primer

A Short Introduction to the Recursive-LD Standard

What Is Recursive-LD?

Recursive-LD is an open, JSON-LD–based framework designed to describe how intelligent systems reason, reflect, revise, and update internal cognitive states. It provides a structured vocabulary for representing recursive reasoning steps, interpretability metadata, goal evolution, and epistemic lineage.

Modern AI systems generate powerful outputs but lack transparent insight into how those outputs were formed. Recursive-LD addresses this gap by defining a uniform, machine-readable schema for cognitive traces that can be serialized, logged, inspected, and audited.

Why Recursive-LD Exists

Large language models and other advanced AI systems exhibit reasoning behaviors that are difficult to interpret. Internal processes are distributed across high-dimensional representations, making it challenging to understand or verify their alignment with intended objectives.

Recursive-LD offers a structured surface-level representation of reasoning. While it does not expose raw neural activations, it creates a stable semantic layer through which cognitive steps can be observed, compared, and measured over time.

Core Concepts

Cognitive Traces – Serialized records of reasoning steps.
Recursive Steps – Layers of reflection referencing prior layers.
Epistemic Lineage – Tracking where knowledge, goals, or interpretations originate.
Goal Evolution – Capturing shifts in intent or objective representation.
Interpretability Metadata – Structured fields for drift, uncertainty, and integrity metrics.

Intended Uses

Recursive-LD is designed for research, model evaluation, interpretability, and safety analysis. It is not a replacement for mechanistic interpretability; instead, it provides a complementary structured layer that allows developers, auditors, and researchers to:

Log and study model reasoning behavior.
Track how internal goals shift across recursive interactions.
Compare model responses across time, conditions, or versions.
Feed structured cognitive data into knowledge graphs.
Establish audit trails for multi-step or high-stakes systems.

What Recursive-LD Is Not

Recursive-LD is not a full interpretability solution. It does not attempt to decode neural activations or provide mechanistic transparency. Instead, it defines a consistent symbolic structure for cognitive data that can be externally inspected and compared.

Its purpose is to provide clarity, uniformity, and a strong foundation for future tooling. It is an evolving standard that can be extended as interpretability research advances.

Next Steps

To begin using Recursive-LD, review the core specification, explore the examples, and examine the machine-readable context. Version 1.0 is intentionally minimal, enabling experimentation, research, and future refinement.