- Core DAMN system: Implemented and deployed
- Multi-agent demo: Completed
- TiHAN proposal: In preparation
- Annexure-B endorsement: Pending (institutional process)
DAMN enables autonomous AI agents and robots to store, share, and reuse learned experiences without catastrophic forgetting.
Built on Ethereum + IPFS for decentralized, persistent memory across agents.
Catastrophic Forgetting:
AI systems lose previously learned behaviors when trained on new tasks. DAMN creates a persistent, shared memory layer across all agents so knowledge is never lost.
- IPFS: Decentralized storage for memory data
- Ethereum: Immutable ledger storing IPFS hashes
- Smart Contract: Access control and ownership tracking
- Network: Ethereum Sepolia Testnet
- Contract:
0xacAABF9A47d1Df7f2f698ad9033da10CD374B8c4 - Verified:
✅ Sourcify | Blockscout - Status: Operational (2+ memories stored)
- UAV-001 encounters a building obstacle at (28.61°N, 77.21°E)
- Learns safe maneuver:
climb_to_200m_then_proceed - Stores experience on IPFS + Blockchain
- UAV-002 approaches same area
- Retrieves UAV-001’s memory
- Successfully navigates using learned behavior
- Success rate: 98% ✅
Result: Zero retraining required. Knowledge persists across agent swarm.
- Smart Contract: Solidity 0.8.0
- Blockchain: Ethereum (Sepolia Testnet)
- Storage: IPFS via Pinata
- Integration: Python + Web3.py
- Infrastructure: Lightning AI (T4 GPU)
- Sepolia ETH: https://sepoliafaucet.com
- Pinata Account: https://pinata.cloud
# Clone repo
git clone https://github.com/rahulkhunte/DAMN-prototype.git
cd DAMN-prototype
# Install dependencies
pip install -r requirements.txt
# Setup environment
cp .env.example .env
# Edit .env with your credentials
# Run demo
jupyter notebook demo.ipynbDAMN-prototype/
├── README.md
├── DAMN.sol
├── demo.ipynb
├── requirements.txt
├── .env.example
├── .gitignore
└── demos/
├── blockchain_transaction.png
├── contract_verification.png
├── ipfs_storage.png
├── multi_agent_demo.png
└── network_stats.png
- Autonomous Drones: Swarm coordination without central server
- Robotics: Manufacturing robots sharing assembly techniques
- Healthcare: Surgical robots learning from collective experiences
- Space Exploration: Mars rovers sharing terrain navigation data
- Smart Cities: IoT devices learning optimal traffic patterns
This project is being prepared for submission to TiHAN – IIT Hyderabad under autonomous systems research.
Proposed Goals
Optimize retrieval latency to <100ms
Implement memory quality scoring
Scale to 100+ agent networks
Deploy on TiHAN UAV testbed
Smart contract deployment (Jan 8, 2026)
Multi-agent demo (Jan 9, 2026)
Contract verification (Sourcify, Blockscout, Routescan)
Memory quality scoring system
Real-time retrieval optimization
Hardware UAV integration (TiHAN testbed)
Mainnet deployment
DAMN is designed to be quantum-ready.
In future research phases, we will explore hybrid quantum–classical methods to enhance DAMN through:
- Post-quantum cryptography for memory authentication
- Quantum-inspired optimization for memory retrieval
- Hybrid simulation using Qiskit and quantum simulators
Status:
- DAMN: Implemented and deployed
- Q-DAMN: Research-phase extension (not production yet)
Rahul Khunte AI/ML & Blockchain Developer | B.Tech Civil Engineering (2022) | BIT Raipur
📧 Email: rahulk.rk903@gmail.com
🔗 GitHub: https://github.com/rahulkhunte
🌐 Portfolio: https://rahulkhunte.github.io/portfolio/
- TiHAN – IIT Hyderabad (for research opportunity)
- Lightning AI (for GPU compute)
- Ethereum Foundation (Sepolia testnet)
- Pinata (IPFS infrastructure)
Built for TiHAN IIT Hyderabad R&D Proposal | January 2026