# 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 1. **ARC_AGI_ARCHITECTURE.md** - Original 48-slot design 2. **ARC_AGI_GPU_REQUIREMENTS.md** - Detailed VRAM calculations 3. **VAST_GPU_OPTIONS.md** - Available GPU rentals analysis 4. **MODELS_TO_PLUG.md** - Complete model list with HuggingFace IDs 5. **EXECUTIVE_SUMMARY.md** - This document --- ## 🚀 NEXT STEPS ### Step 1: Rent GPU (5 minutes) ```bash # Via relay system vast rent 1 ``` **Recommendation**: Start with Offer #1 (RTX 6000 @ $0.73/hr) ### Step 2: Wait for Instance (2-5 minutes) ```bash vast instances ``` Repeat until status shows "running" ### Step 3: Connect to GPU (2 minutes) ```bash vast connect ``` This connects to the GPU via SSH for remote model loading ### Step 4: Connect Kiro (2 minutes) - MCP endpoint: `http://:8765/sse` - Add to `.kiro/settings/mcp.json` - Restart Kiro ### Step 5: Load Models (30-60 minutes) Follow sequence in `MODELS_TO_PLUG.md`: 1. Core infrastructure (< 10 GB) 2. Reasoning model (+ 35 GB) 3. Synthesis model (swap) 4. 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 1. ✅ **Diverse perception**: 8 different ways to see grids 2. ✅ **Complete primitives**: 16 specialists cover all ARC operations 3. ✅ **Massive search**: 8 synthesis models × beam search 4. ✅ **Strong reasoning**: 70B+ parameter models 5. ✅ **Verification**: Only correct programs survive 6. ✅ **Evolution**: System improves over time 7. ✅ **Provenance**: Full transparency and debugging ### Challenges 1. ⚠️ **Model coordination**: 48 slots need orchestration 2. ⚠️ **VRAM management**: Staged loading adds latency 3. ⚠️ **Fine-tuning**: Primitive specialists need training 4. ⚠️ **ARC dataset**: Only 400 training examples 5. ⚠️ **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 - [x] Design 48-slot architecture - [x] Calculate VRAM requirements - [x] Identify models on HuggingFace - [x] 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**: ```bash # Append to relay center vast rent 1 ``` **Then connect and plug models**: ```bash 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! 🚀*