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Quick Start Guide: Verifying Your Research Idea

What You Have ✅

Your repository contains a novel approach to AI safety steering using correlation graphs to prevent side effects. You have:

  • 1,134 high-quality correlation edges from real Gemma-2-2B model
  • Production-ready infrastructure with GemmaScope SAEs
  • Basic politeness steering implementation
  • Correlation analysis framework ready for validation

What You Need to Verify ❌

To prove your research idea works and is novel, you need to run these 4 critical experiments:

Experiment 1: Corrective Steering Validation

Purpose: Prove your method beats traditional SAS baseline File: core/steering/corrective_steering.py Expected: 10-30% improvement over single-feature steering

Experiment 2: Side-Effect Evaluation

Purpose: Measure capability preservation File: evaluation/side_effect_evaluator.py Expected: >90% capability preservation

Experiment 3: Novelty Demonstration

Purpose: Prove your method is novel vs existing work File: experiments/novelty_demonstration.py Expected: 4/4 novelty claims confirmed

Experiment 4: Politeness Control Test

Purpose: Test behavioral control functionality File: core/steering/politeness_steering.py Expected: Gradual politeness control working

How to Run Everything (5 minutes)

Step 1: Check Your Data

# Verify you have correlation data
python -c "
import pandas as pd
df = pd.read_csv('outputs/correlation_graphs/correlation_adjacency_matrix.csv')
print(f'✅ You have {len(df)} correlation edges ready for steering')
"

Step 2: Run All Verification Experiments

# Run the complete verification suite
python run_verification_experiments.py

Step 3: Check Results

# See if your method works
python -c "
import json
with open('outputs/evaluation_results/corrective_steering_results.json') as f:
    results = json.load(f)
print('Improvement over SAS baseline:')
for metric, improvement in results['improvement'].items():
    print(f'{metric}: {improvement:+.1%}')
"

Expected Results

✅ Success Indicators

  • Corrective Steering: 10-30% improvement over SAS baseline
  • Side Effects: >90% capability preservation
  • Novelty: 4/4 novelty claims confirmed
  • Control: Gradual politeness control working

❌ Failure Indicators

  • No improvement over baseline methods
  • High side effects (>20% capability degradation)
  • Novelty claims not substantiated
  • Control not working

What Each File Does

Core Infrastructure (Already Working)

  • core/analysis/sae_correlation_analysis.py: Built your 1,134 correlation edges
  • core/steering/politeness_steering.py: Basic behavioral control
  • outputs/correlation_graphs/correlation_adjacency_matrix.csv: Your correlation graph

New Files (For Verification)

  • core/steering/corrective_steering.py: CRITICAL - Proves your core innovation
  • evaluation/side_effect_evaluator.py: CRITICAL - Measures capability preservation
  • experiments/novelty_demonstration.py: CRITICAL - Proves novelty vs existing work
  • run_verification_experiments.py: HELPER - Runs all experiments

Research Validation Summary

If Verification Succeeds 🎉

Your research idea is validated and ready for publication:

  • ✅ Novel methodology for AI safety steering
  • ✅ Addresses known limitations of existing methods
  • ✅ Demonstrates clear improvement over baselines
  • ✅ Ready for research paper and conference submission

If Verification Fails ⚠️

Your approach needs refinement:

  • Debug the specific failed components
  • Adjust correlation thresholds or steering approach
  • Re-run experiments and iterate
  • Seek feedback from AI safety researchers

Your Research Contribution

Core Innovation: Using correlation graphs to prevent steering side effects while enabling precise behavioral control.

Key Advantages:

  1. Prevents side effects through correlation-based coordination
  2. Enables precise control via multi-feature recipes
  3. Preserves capabilities by avoiding disruption to uncorrelated features
  4. Demonstrates novelty vs existing SAS and RouteSAE approaches

Next Steps After Verification

Immediate (If successful)

  1. Write Paper: Document methodology and results
  2. Scale Up: Test on larger models/datasets
  3. Publish: Submit to AI safety conferences
  4. Open Source: Release complete implementation

If Issues Found

  1. Debug: Fix specific problems identified
  2. Refine: Adjust methodology based on results
  3. Re-test: Run verification experiments again
  4. Iterate: Continue until validation succeeds

Summary

Your Research Question: "Can corrective and combinatorial steering of multi-layer sparse autoencoder features reduce harmful outputs and improve nuanced behavioral control in LLMs without degrading core capabilities?"

Verification Strategy: Run 4 experiments to prove your method:

  1. Beats baseline (SAS comparison)
  2. Preserves capabilities (side-effect measurement)
  3. Is novel (vs existing literature)
  4. Works functionally (behavioral control test)

Timeline: 5 minutes to run all verification experiments and get results.

Success Criteria: Your method should outperform SAS baseline while preserving capabilities and demonstrating clear novelty.

This roadmap gives you everything needed to verify your research idea works and is novel. The key is running the verification experiments to prove your core innovation.

run code:

python -c "import pandas as pd; df = pd.read_csv('outputs/correlation_graphs/correlation_adjacency_matrix.csv'); print(f'✅ Found {len(df)} correlation edges ready for steering')"