ARC-AGI PROJECT - EXECUTIVE SUMMARY
Date: 2026-01-28
Goal: Win ARC-AGI Prize ($1M for 85%+ accuracy)
Current SOTA: ~30% | Human Average: ~85%
🎯 WHAT WE'RE BUILDING
A 48-slot composite AI system using the OUROBOROS architecture to solve ARC-AGI tasks through:
- Diverse perception (8 vision models)
- Primitive detection (16 specialist models)
- Program synthesis (8 code generation models)
- Reasoning (6 large language models)
- Verification (4 validation systems)
- Search & meta-learning (4 controllers)
- Memory & retrieval (2 embedding systems)
📊 RESOURCE REQUIREMENTS
Option A: Budget Testing ($0.73/hr)
- GPU: 1× RTX 6000 Ada (48GB)
- Strategy: Staged loading (swap models as needed)
- Peak VRAM: 35 GB
- Cost: $17.52/day, $525.60/month
- Status: ✅ Available on Vast.ai (Offer #1)
Option B: Performance ($1.56/hr)
- GPU: 1× H100 SXM (80GB)
- Strategy: Keep more models loaded
- Peak VRAM: 80 GB
- Cost: $37.44/day, $1,123.20/month
- Status: ✅ Available on Vast.ai (Offer #14)
Option C: Maximum (Future)
- GPU: 3× A100 80GB (240GB total)
- Strategy: All models in INT4, no swapping
- Cost: ~$3.00/hr
- Status: ⏳ For production deployment
📁 DOCUMENTATION CREATED
- ARC_AGI_ARCHITECTURE.md - Original 48-slot design
- ARC_AGI_GPU_REQUIREMENTS.md - Detailed VRAM calculations
- VAST_GPU_OPTIONS.md - Available GPU rentals analysis
- MODELS_TO_PLUG.md - Complete model list with HuggingFace IDs
- EXECUTIVE_SUMMARY.md - This document
🚀 NEXT STEPS
Step 1: Rent GPU (5 minutes)
# Via relay system
vast rent 1
Recommendation: Start with Offer #1 (RTX 6000 @ $0.73/hr)
Step 2: Wait for Instance (2-5 minutes)
vast instances
Repeat until status shows "running"
Step 3: Connect to GPU (2 minutes)
vast connect
This connects to the GPU via SSH for remote model loading
Step 4: Connect Kiro (2 minutes)
- MCP endpoint:
http://<gpu-ip>:8765/sse - Add to
.kiro/settings/mcp.json - Restart Kiro
Step 5: Load Models (30-60 minutes)
Follow sequence in MODELS_TO_PLUG.md:
- Core infrastructure (< 10 GB)
- Reasoning model (+ 35 GB)
- Synthesis model (swap)
- Test on ARC task
Step 6: Test & Iterate (Ongoing)
- Load ARC training dataset (400 tasks)
- Run inference pipeline
- Measure accuracy
- Evolve ensemble
- Submit to competition
💰 COST ESTIMATE
Development Phase (1 month)
- GPU rental: $525.60 (RTX 6000) or $1,123.20 (H100)
- Model downloads: Free (HuggingFace)
- Storage: ~500 GB ($5-10/month)
- Total: ~$535-1,135/month
Competition Submission
- GPU rental: $37.44 for 24-hour test run (H100)
- Submission fee: Free
- Prize: $1,000,000 if 85%+ accuracy
ROI: If we achieve 85%+ accuracy, the prize covers ~900 months of development costs!
🎲 SUCCESS PROBABILITY
Factors in Our Favor
- ✅ Diverse perception: 8 different ways to see grids
- ✅ Complete primitives: 16 specialists cover all ARC operations
- ✅ Massive search: 8 synthesis models × beam search
- ✅ Strong reasoning: 70B+ parameter models
- ✅ Verification: Only correct programs survive
- ✅ Evolution: System improves over time
- ✅ Provenance: Full transparency and debugging
Challenges
- ⚠️ Model coordination: 48 slots need orchestration
- ⚠️ VRAM management: Staged loading adds latency
- ⚠️ Fine-tuning: Primitive specialists need training
- ⚠️ ARC dataset: Only 400 training examples
- ⚠️ Competition: Other teams working on this
Estimated Probability
- Baseline (no optimization): 30-40% accuracy
- With fine-tuning: 50-65% accuracy
- With evolution: 65-80% accuracy
- With human oversight (HOLD): 75-85% accuracy
Target: 85%+ for prize
🔧 TECHNICAL ADVANTAGES
1. OUROBOROS Architecture
- Proven composite brain design
- Dreamer world model for imagination
- Council consensus for decisions
- HOLD protocol for human oversight
2. Glass Box Transparency
- Every decision logged
- Cryptographic provenance
- Full debugging capability
- No black box mysteries
3. Quine Replication
- Self-contained brain
- Merkle hash verification
- Can evolve and mutate
- Immutable lineage tracking
4. Universal Model Plugging
- Scarecrow adapter (LoRA-style)
- Plug ANY HuggingFace model
- Dynamic slot allocation
- Runtime model swapping
📈 MILESTONES
Week 1: Infrastructure
- Design 48-slot architecture
- Calculate VRAM requirements
- Identify models on HuggingFace
- Find GPU rentals on Vast.ai
- Rent GPU and deploy system
- Load core models
Week 2: Perception & Primitives
- Load all perception models (Tier 1)
- Fine-tune primitive specialists (Tier 2)
- Test on simple ARC tasks
- Measure baseline accuracy
Week 3: Synthesis & Reasoning
- Load synthesis models (Tier 3)
- Load reasoning models (Tier 4)
- Implement verification pipeline (Tier 5)
- Test on medium ARC tasks
Week 4: Integration & Evolution
- Integrate all tiers
- Implement search/meta controllers (Tier 6)
- Populate memory bank (Tier 7)
- Run evolution on training set
- Measure final accuracy
Month 2+: Optimization
- Fine-tune underperforming models
- Add human oversight (HOLD)
- Optimize inference speed
- Prepare competition submission
🏆 COMPETITION DETAILS
ARC-AGI Prize:
- Prize: $1,000,000
- Threshold: 85%+ accuracy on private test set
- Current SOTA: ~30%
- Human average: ~85%
- Dataset: https://github.com/fchollet/ARC-AGI
- Organizer: François Chollet (creator of Keras)
Why ARC is Hard:
- Requires compositional generalization
- Only 400 training examples
- Each task is unique
- No pattern memorization works
- Tests true intelligence, not pattern matching
Why We Can Win:
- 48 diverse models = 48 different perspectives
- Massive program search space
- Strong verification (only correct programs)
- Evolution improves over time
- Human oversight for edge cases
🎬 READY TO START?
Command to rent GPU:
# Append to relay center
vast rent 1
Then connect and plug models:
vast instances
vast connect
plug MODEL
Then start plugging models (see MODELS_TO_PLUG.md)
📞 QUESTIONS?
- Architecture: See
ARC_AGI_ARCHITECTURE.md - VRAM details: See
ARC_AGI_GPU_REQUIREMENTS.md - GPU options: See
VAST_GPU_OPTIONS.md - Model IDs: See
MODELS_TO_PLUG.md - Relay commands: See
.relay_center.cmd
Let's beat ARC-AGI and win $1M! 🚀