#!/usr/bin/env bash
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# ============================================================================
# Prepare paper for arXiv submission

cat << 'ARXIV'
# arXiv Submission Preparation
## Getting your paper on arXiv.org

---

## Pre-Submission Checklist

### Paper Requirements
- [ ] LaTeX source (required)
- [ ] All figures in EPS or PDF format
- [ ] Bibliography in .bib format
- [ ] Abstract < 1920 characters
- [ ] All citations properly formatted

### Mathematical Verification
```bash
blackroad verify              # All equations pass
blackroad physics-check       # No contradictions
blackroad paper-checklist     # Review citations
```

### Content Requirements
- [ ] Novel contributions clearly stated
- [ ] All standard equations cited
- [ ] Methods section explains framework
- [ ] Results present β_BR
- [ ] Discussion addresses limitations
- [ ] Testable predictions included

---

## arXiv Categories

### Primary Category
**quant-ph** (Quantum Physics)
- Most appropriate for quantum-classical framework
- High visibility to quantum ML community

### Secondary Categories
- **cs.LG** (Machine Learning)
- **cond-mat.stat-mech** (Statistical Mechanics)
- **physics.bio-ph** (Biological Physics)
- **cs.AI** (Artificial Intelligence)

---

## Title Strategies

### Option 1: Direct
"The BlackRoad Constant: A Dimensionless Measure of the Quantum-Classical Boundary in Learning Systems"

### Option 2: Framework Focus
"Unified Framework for Quantum Mechanics, Thermodynamics, and Neural Learning via Spiral Operator"

### Option 3: Testable
"Experimental Predictions for Quantum-Classical Transitions in AI: The β_BR = 1 Hypothesis"

**Recommendation:** Option 3 (emphasizes testability)

---

## Abstract Template

```
We propose a dimensionless constant β_BR = (ℏω/k_BT)·(|∇L|/L) 
characterizing the quantum-classical boundary in learning systems. 
The constant combines quantum/thermal energy ratio (ℏω/k_BT) with 
neural network loss landscape structure (|∇L|/L), predicting three 
regimes: β_BR >> 1 (quantum-coherent), β_BR ≈ 1 (critical), and 
β_BR << 1 (classical-thermal). We derive this from a spiral operator 
framework 𝓤(θ,a) = e^((a+i)θ) unifying quantum mechanics, 
thermodynamics, and gradient-based learning. Key prediction: 
biological brains operate at β_BR ≈ 1 to maximize information 
processing at the edge of quantum decoherence. We provide symbolic 
verification of all mathematical claims and propose three experimental 
tests: (1) EEG measurements during learning should yield β_BR ≈ 1, 
(2) quantum neural networks should show advantage near β_BR ≈ 1, 
(3) biological neural performance should peak at temperatures where 
β_BR ≈ 1. All code and verification available at [github].

[987 characters - under 1920 limit]
```

---

## File Structure for Submission

```
blackroad-framework/
├── main.tex                 # Main paper
├── sections/
│   ├── introduction.tex
│   ├── framework.tex
│   ├── derivations.tex
│   ├── results.tex
│   ├── discussion.tex
│   └── conclusions.tex
├── figures/
│   ├── spiral-operator.pdf
│   ├── beta-br-regimes.pdf
│   └── verification-results.pdf
├── references.bib
└── supplementary.tex        # Verification code, proofs
```

---

## Supplementary Material

Include:
1. **Complete verification code**
   - Python scripts (SymPy verification)
   - All test results
   - Reproducibility instructions

2. **Mathematical proofs**
   - Detailed derivations
   - Symbolic algebra steps
   - Dimensional analysis

3. **Data availability**
   - GitHub repo link
   - Verification results
   - Analysis scripts

---

## Common Mistakes to Avoid

❌ Submitting PDF only (need LaTeX source)
❌ Forgetting to cite standard equations
❌ Overclaiming ("we discovered quantum mechanics")
❌ Missing figure captions
❌ Bibliography errors
❌ Abstract over 1920 characters

---

## Submission Process

1. **Create account** at arxiv.org
2. **Prepare files**
   ```bash
   tar -czf submission.tar.gz main.tex sections/ figures/ references.bib
   ```
3. **Upload** to arXiv submission portal
4. **Select categories**
   - Primary: quant-ph
   - Cross-list: cs.LG, cond-mat.stat-mech
5. **Review** automated checks
6. **Submit** for moderation

**Timeline:**
- Upload: Day 1
- Moderation: 1-3 business days
- Publication: Announced next business day 8pm ET

---

## After Publication

### Promote on:
- Twitter (tag: #QuantumML #AIResearch)
- Reddit (r/MachineLearning, r/Physics)
- Hacker News
- LinkedIn

### Send to:
- Quantum ML researchers
- Neuroscience labs
- AI safety groups
- Potential collaborators

### Track:
- Citations (Google Scholar alert)
- Downloads (arXiv stats)
- Comments/feedback
- Collaboration requests

---

## Example Citation

Once published, others will cite as:

```bibtex
@article{amundson2025blackroad,
  title={The BlackRoad Constant: Quantum-Classical Boundary in Learning Systems},
  author={Amundson, Alexa},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025}
}
```

---

## Next Steps

1. Convert white paper to LaTeX
2. Create figures (spiral operator, β_BR regimes)
3. Run final verification: `blackroad all-reports`
4. Get feedback from trusted colleagues
5. Submit to arXiv
6. Announce publication

**Goal: On arXiv within 2 weeks**

ARXIV

echo ""
echo "arXiv preparation guide created"
echo "Time to get published!"
