Naturepedia Canonical Discovery MCP server

robbies-razor-benchmarks — Recursive Stability and Compression Efficiency Benchmarks for AI Reasoning Systems

Run a Razor Audit

Evaluate any AI system using Robbie George’s Grand Compression Cosmology:

Reference implementation and benchmarking framework for evaluating Robbie’s Razor compliance, recursive stability, and compression efficiency in reasoning systems operating under constrained compute, memory, and governance bandwidth.

System Architecture Overview

This repository supports Robbie’s Razor™, Naturepedia™, and Plate™ systems as part of a recursive ecological knowledge architecture combining:

• semantic compression
• machine-readable provenance
• recursive relationship mapping
• ecological intelligence architecture
• structured retrieval systems
• low-token semantic traversal

Core reasoning sequence:

compression → expression → memory → recursion

Key concepts: Robbie’s Razor · Grand Compression Cosmology · Recursive Stability · Compression Efficiency · Reasoning Benchmarks

Grand Compression Law of Intelligence

Within the Grand Compression Cosmology, intelligence is modeled through the reuse of preserved compressed structure across recursive cycles under finite resource and stabilization constraints.

The framework proposition can be summarized as:

preserved structure
→ expression
→ memory
→ recursive reuse
→ prediction / adaptation

The core Robbie’s Razor™ sequence is:

compression → expression → memory → recursion

This sequence is a framework architecture.

It should not be interpreted as a claim that every biological, computational, physical, ecological, or social intelligence system has been empirically demonstrated to operate through an identical mechanism.

Accordingly:

Grand Compression framework proposition
≠
universally established law of intelligence

and:

recursive reuse
≠
automatic intelligence

A system must still be evaluated according to its declared task, preserved structure, prediction quality, correctness, resource constraints, and failure conditions.

Recursive Constraint Model

Within the framework, recursive performance may be analyzed using two conceptual constraint classes:

• Energetic Recursion Ceiling — the available energetic budget relative to the cost of coherent transitions;
• Governance Recursion Ceiling — the available stabilization or correction capacity relative to correction demand.

A conceptual energetic ceiling may be written as:

R ≤ E / JCT

A conceptual governance ceiling may be written as:

R × C ≤ S

Combining the two produces the framework-level Safe Recursion Envelope:

R ≤ min(E / JCT, S / C)

Where:

• R = recursion rate;
• E = available energy within the declared system boundary;
• JCT = Joules per Coherent Transition;
• S = available stabilization or governance bandwidth;
• C = correction demand per transition.

These expressions are architectural research relations.

Quantitative application requires operational definitions for every variable, compatible units, measurement procedures, system boundaries, baselines, uncertainty, and falsification conditions.

Without those declarations:

R ≤ E / JCT

R × C ≤ S

and:

R ≤ min(E / JCT, S / C)

must not be represented as universally validated physical laws.

Intelligence Interpretation Boundary

The framework motivates the hypothesis that useful intelligence may depend not only on computation, but on the ability to preserve and recursively reuse structure that remains valid for later tasks.

Possible relevant properties include:

Shortened. The full README is on GitHub.