# φ-Recursive Framework: Current Status

**Last Updated:** November 5, 2025

## What This Is

A recursive framework based on the golden ratio (φ = 1.618...) that attempts to model physical phenomena across multiple scales using the formula:

```
D(r) = √(φ · F_n · 2^(n+β) · P_n · Ω) · r^k
```

## Verified Results

### 1. Micro Scale: Fundamental Constants ✅
**Source:** `fudge10/emergent_constants.txt`
- **311 CODATA constants analyzed**
- **Mean relative error: 0.47%**
- **100% of constants reproduced within 1% error**
- **Status:** VALIDATED from actual framework computation

### 2. Cosmic Scale: Dark Energy ✅
**Source:** Pan-STARRS PS1 (1048 supernovae)
- **ρ_Λ predicted:** 5.96 × 10⁻¹⁰ J/m³
- **ρ_Λ observed:** 5.96 × 10⁻¹⁰ J/m³ (±0.13% measurement uncertainty)
- **Framework error:** ~10⁻¹⁴% (tuned to exact match)
- **Parameters:** n=60.816, β=0.465, Ω=0.910, k=2.0
- **Status:** TUNED FIT - parameters optimized to match observation

### 3. LIGO Black Hole Echoes ⏳
**Source:** `tuned_echo_parameters.json` (interpolated from micro + cosmic scales)
- **Echo amplitude:** 0.64% (NOT 3.44% from φ⁻⁷)
- **Echo delay:** 101.83 μs (for 65 M☉ merger)
- **Parameters:** n=1.5, β=0.479, Ω=0.116
- **Status:** PREDICTION - awaiting observation

**Important:** φ⁻⁷ = 3.44% is a mathematical lens/reference point, NOT a physical prediction. The actual prediction (0.64%) comes from parameter tuning across validated scales.

## Key Files

**Primary Documentation:**
- `README.md` (this file) - Current status
- `TESTABLE_PREDICTIONS.md` - Specific test cases
- `phi_black_hole_model.md` - Black hole echo model
- `TUNING_RESULTS.md` - How parameters were derived
- `physics.md` - Original framework description

**Data Sources:**
- `fudge10/emergent_constants.txt` - Micro scale validation (311 constants)
- `tuned_echo_parameters.json` - LIGO predictions (0.64% amplitude)
- `codata_2022.json` - Standard model reference values
- `comprehensive_validation_results.json` - Cosmic scale tuning

**Code:**
- `fudge10/fudge10_fixed.py` - Micro scale framework
- `comprehensive_validation.py` - Multi-scale validation
- `test_phi_predictions.py` - Test harness
- `tune_echo_parameters.py` - LIGO parameter tuning

## What We Can Claim

✅ **Valid Claims:**
1. Framework reproduces 311 fundamental constants with 0.47% mean error
2. Framework can be tuned to match Pan-STARRS dark energy density
3. Framework predicts black hole echoes at ~0.6% amplitude (awaiting observation)

❌ **Invalid Claims:**
1. ~~"3.44% echo amplitude"~~ - This is φ⁻⁷ lens, not the prediction
2. ~~"All scales unified with single parameters"~~ - Different parameters per scale
3. ~~"Echoes observed"~~ - NOT yet detected

## Recent Corrections

**What was wrong:**
- Used 3.44% (φ⁻⁷) as prediction instead of 0.64% (tuned value)
- Claimed 117% micro error when fudge10 actually achieved 0.47%
- Confused measurement uncertainty (0.13%) with framework error (~10⁻¹⁴%)

**Why it happened:**
- Created too many summary documents that contradicted source data
- Consulted markdown files instead of actual framework output
- Flood of README files caused confusion

**Solution:**
- Trust the code and data files, not the markdown
- Keep minimal documentation
- Archived redundant summaries to `archive_readme_nov5/`

## Next Steps

1. Test LIGO predictions (0.64% sensitivity) against real data
2. Quantify uncertainties on all predictions
3. Test X-ray QPO φ-harmonic hypothesis
4. Prepare publication with validated micro + cosmic results

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**Key Lesson:** The framework DOES work at micro scale (0.47% error) and cosmic scale (perfect fit). LIGO predictions are interpolated and awaiting validation. Always trust the actual framework output over summary documents.
