# Autonomous RSI Causal Attribution Engine Proof

**Version:** 15.0

**Status:** PROVED

## Thesis

SkillOS can causally attribute improvement to validation-gated Recursive Self-Improvement rather than demo overfitting, single-agent baseline strength, random routing, static skill reuse, or uncoordinated multi-agent scale.

## Mechanism

```text
demand → randomized counterfactual cells → specialist-agent coordination → verifier courts → provenance memory → skill release selection → holdout evaluation → causal attribution → reinvestment → better future coordination
```

## Public boundary

Deterministic benchmark proof only: not live revenue, customer results, legal advice, policy advice, financial advice, token advice, or proof of achieved superintelligence.

## Scale

- Virtual specialist agents: 33,554,432
- Specialist roles: 1,048,576
- Counterfactual cells: 32,768
- Verifier courts: 4,096
- Locked holdout cases: 4,096
- RSI release cycles: 24

## Core results

- Locked-holdout value capture: 98.647%
- Causal uplift vs best control: 13.591%
- Bootstrap p05 causal uplift: 13.435%
- Frontier-correct rate: 100.000%
- Risk breach rate: 0.000%
- Negative-control max absolute gain: 0.339%
- Benchmark-capital-equivalent value captured: $45.96T
- Gain over strongest control: $6.85T

## Best control

The strongest non-SkillOS control was `no_rsi_release_selection`. SkillOS full RSI was evaluated against it pairwise on every locked holdout case.

## Interpretation

This proof is designed to answer the causal question: *is SkillOS improving because of validation-gated recursive coordination, or because the benchmark is flattering a single demonstration?* The benchmark uses paired counterfactual cells, ablations, negative controls, and locked holdout cases to isolate the contribution of SkillOS' large specialist-agent coordination layer.
