Luntra Glossary

A composite metric tracking the frequency and complexity of actions performed by autonomous agents on Luntra, used to measure platform adoption and AI-native usage patterns.

A decentralized governance system where autonomous agents can participate in protocol decisions, propose improvements, and vote on network upgrades using sophisticated decision-making algorithms.

On-chain autonomous agents with decision-making capabilities, built using LangChain integration. These agents can perform wallet interactions, participate in DAO governance, and execute smart contracts independently without human intervention. Features include memory persistence and self-adaptation mechanisms.

The time required to deploy new AI models to Luntra's infrastructure, measured from upload to full availability across all network nodes. Luntra targets sub-second deployment times through optimized blob storage and caching mechanisms.

A decentralized on-chain registry and marketplace for AI/ML model distribution. Utilizes blob storage through EIP-4844 for efficient model storage while maintaining verifiability and censorship resistance. Enables discovery, upload, and deployment of machine learning models.

A blockchain architecture where artificial intelligence is integrated at the foundational level across all chain operations, including transaction ordering, execution, and protocol governance, rather than being an add-on feature.

Smart contracts deployed on Luntra that are specifically designed to interact with AI agents and machine learning models, featuring built-in AI operation primitives and agent communication protocols.

Luntra's enhanced transaction pool that uses machine learning algorithms to prioritize transactions, detect spam, and prevent MEV attacks before transactions are included in blocks. Integrates with MEV Radar for comprehensive protection.

Mean transaction cost paid by users, typically maintained around 0.1 gwei on Luntra through smart account abstraction and efficient rollup transaction batching.

An OP Stack component responsible for collecting transactions from the sequencer and publishing them to Ethereum L1. In Luntra's implementation, the batcher is enhanced with AI-driven compression algorithms to optimize data availability costs and blob utilization.

The modular foundation of the OP Stack that Luntra builds upon, providing the core optimistic rollup functionality. Luntra extends Bedrock with AI-native components while maintaining compatibility with the standard OP Stack specification.

Luntra's implementation of EIP-4844 blob storage specifically optimized for AI model distribution and parameter storage, reducing costs for ML operations while maintaining data availability guarantees.

The percentage of available EIP-4844 blob space used by Luntra for storing AI models, transaction data, and other large data structures, indicating efficiency of data availability layer usage.

The target interval between block production on the Luntra network, optimized at approximately 2 seconds to ensure fast finality and responsive user experience.

An incentive mechanism that evaluates deployed smart contracts and decentralized applications over a 7-day period, rewarding developers with tokens and recognition badges based on metrics including usage adoption, system stability, and innovation impact.

Luntra's implementation of the Cannon fault proof virtual machine, enhanced with AI verification capabilities to detect and prove invalid state transitions more efficiently than traditional fraud proof systems.

An embedded AI system that analyzes on-chain wallet behavior patterns to generate behavioral profiles and Smart Wallet Scores. Provides anomaly detection capabilities for identifying fraudulent activity, bot behavior, and security threats.

The precision percentage of ChainSage™'s behavioral analysis and fraud detection capabilities, measured against confirmed malicious activities and false positive rates.

A distributed AI system where multiple agents and models work together across Luntra to solve complex problems, share knowledge, and optimize network operations collectively.

A metric measuring the frequency and success rate of multi-agent coordination activities on Luntra, indicating the sophistication of AI agent interactions within the ecosystem.

Luntra's capability to interact with AI services and models deployed on other blockchain networks, enabling seamless AI agent communication across different Layer 1 and Layer 2 ecosystems.

The infrastructure component responsible for ensuring transaction data is accessible and verifiable. Luntra utilizes Ethereum as its primary data availability layer, inheriting Ethereum's security guarantees.

The process by which Luntra nodes derive the L2 state from L1 data, enhanced with AI validation mechanisms that can detect anomalies and potential fraud before the standard challenge period expires.

The average time required to resolve transaction disputes in Luntra's hybrid rollup system, typically faster than standard optimistic rollups due to AI-assisted verification and zero-knowledge proof integration.

An AI-driven mechanism in Luntra that adjusts gas prices based on network congestion, user behavior patterns, and transaction urgency, optimizing for both user experience and network efficiency.

Luntra's ability to automatically adjust protocol parameters and operational modes based on AI analysis of network conditions, user behavior, and performance metrics.

Support for Ethereum Improvement Proposal 4844, which introduces "proto-danksharding" and blob transactions, enabling more efficient data storage and reduced costs for Layer 2 solutions.

The interface between Luntra's consensus layer and execution layer, extended to support AI model execution and agent runtime environments alongside standard EVM operations.

Luntra's extended Ethereum Virtual Machine that includes native support for AI operations, agent execution, and machine learning inference while maintaining full backward compatibility with standard EVM contracts.

Luntra's dispute resolution mechanism that combines traditional optimistic rollup fraud proofs with zero-knowledge proof verification for enhanced security and faster dispute resolution.

A system enabling privacy-preserving machine learning across Luntra where models can be trained on distributed data without exposing sensitive information, useful for collaborative AI development.

The mechanism by which Luntra achieves transaction finality, combining optimistic confirmation with AI-verified checkpoints to provide faster settlement guarantees than traditional optimistic rollups.

A dynamic metric indicating the probability that a transaction on Luntra has reached final settlement, calculated using AI analysis of network conditions, transaction patterns, and historical data.

The precision rate of Luntra's AI-powered fraud detection systems, including MEV Radar and ChainSage™, measured against confirmed attacks and false positive incidents.

A security mechanism in Optimistic Rollups where potentially invalid transactions can be challenged and proven incorrect through cryptographic proofs, ensuring network integrity without requiring pre-validation of all transactions.

Comparative measure of transaction cost reduction relative to equivalent Layer 1 Ethereum execution, with Luntra achieving approximately 18% cost reduction through AI optimization techniques.

Luntra's implementation of account abstraction that enables users to pay transaction fees using ERC-20 tokens or have fees sponsored based on AI-analyzed wallet reputation and behavior patterns.

Advanced machine learning models used in MEV detection that analyze transaction relationships and patterns in the mempool to identify front-running and sandwich attacks.

Zero-knowledge proof circuits used in VerifyX for privacy-preserving identity verification, enabling users to prove credentials without revealing underlying personal information.

Infrastructure capability allowing real-time updates to deployed AI models without service interruption or contract redeployment, essential for maintaining evolving AI agents and machine learning services.

A dual-mode execution architecture combining Optimistic Rollup performance with zero-knowledge proof verification for disputed transactions, enhancing both speed and trustworthiness of final settlements.

A measure of how effectively Luntra's AI-enhanced EVM executes smart contracts compared to standard EVM implementations, including optimization and resource utilization improvements.

Custom precompiled contracts in Luntra that provide native support for common AI operations such as neural network inference, vector operations, and cryptographic primitives used in machine learning.

An AI-driven system that dynamically allocates computational resources across Luntra nodes based on demand patterns, ensuring optimal performance for both traditional transactions and AI operations.

Luntra's use of Ethereum mainnet for publishing rollup data, leveraging EIP-4844 blob transactions for cost-efficient storage of both transaction data and AI model parameters.

The initial configuration parameters for Luntra's rollup deployment, including AI system initialization, agent runtime setup, and machine learning model registry bootstrap parameters.

Built-in library support for dynamic AI workflows, memory management, and multi-step reasoning, enabling autonomous agents to perform complex planning and decision-making over extended time periods.

An AI-native Layer 2 blockchain platform built on the Optimism OP Stack, designed to integrate artificial intelligence at the foundational level of rollup architecture. Luntra combines optimistic rollup execution with machine learning capabilities to enable on-chain AI agents, intelligent transaction processing, and behavior-aware protocols while maintaining full EVM compatibility.

The canonical bridge connecting Luntra to Ethereum mainnet, enhanced with AI-powered fraud detection and automated monitoring systems to ensure secure asset transfers between layers.

A comprehensive metric combining network uptime, AI system performance, transaction throughput, and user adoption to provide an overall assessment of platform health and stability.

A network participant in the Luntra ecosystem that processes transactions, maintains blockchain state, and executes AI-enhanced operations. Luntra nodes include additional AI processing capabilities beyond standard OP Stack nodes, enabling real-time ML inference and agent execution.

The primary transaction ordering and batching component in Luntra's architecture, enhanced with AI-assisted block packing algorithms and MEV protection mechanisms. The sequencer integrates machine learning models to optimize transaction ordering and detect malicious activity in real-time.

The testing environment for Luntra's AI-native rollup infrastructure, allowing developers to deploy and test AI agents, smart contracts, and ML models before mainnet deployment. Provides full feature parity with mainnet including AgentX capabilities and ChainSage™ analytics.

Luntra's execution environment that extends the EVM with native AI capabilities, allowing smart contracts to directly invoke machine learning models and autonomous agents without external oracles.

A supplementary consensus mechanism in Luntra where AI models participate in validating transactions and detecting anomalies, providing an additional security layer beyond traditional cryptographic verification.

The time required for AI models deployed on Luntra to process inputs and return results, critical for real-time agent operations and interactive AI applications.

Security metric measuring the frequency of MEV-related threats detected and prevented per block, with lower rates indicating more effective protection mechanisms.

Real-time protection system utilizing graph neural networks to detect and prevent Maximal Extractable Value attacks such as front-running and sandwich attacks. Includes transaction reordering capabilities and gas refund mechanisms for affected users.

A hierarchical system of AI agents where higher-level agents coordinate and manage lower-level specialized agents, enabling complex autonomous operations and decision-making chains.

A metric tracking how AI model performance changes over time on Luntra, important for maintaining quality of AI services and determining when models need updates or retraining.

Performance measure of the time required to update deployed AI models without service interruption, with Luntra supporting near-zero latency model updates.

A framework within Luntra that enables multiple autonomous agents to coordinate actions, share information, and execute complex multi-step operations across different smart contracts and protocols.

The percentage of Luntra's computational resources dedicated to AI operations versus traditional blockchain operations, indicating the platform's AI-native adoption level.

Specialized oracle services in Luntra that provide AI-generated insights and predictions directly to smart contracts, enabling contracts to make decisions based on machine learning analysis of on-chain and off-chain data.

Optimism's modular, open-source rollup framework providing EVM compatibility, fraud-proof systems, and Ethereum data availability integration. Serves as the foundational infrastructure for Luntra.

The main contract on Ethereum L1 that handles deposits, withdrawals, and dispute resolution for Luntra, extended with AI-powered monitoring and automated security checks.

A Layer 2 scaling solution that assumes transactions are valid by default and uses fraud proofs to challenge invalid transactions, enabling higher throughput while maintaining Ethereum security.

A component that publishes Luntra's state commitments to Ethereum L1, enhanced with AI-verified state transitions and automated anomaly detection to prevent invalid state proposals.

Advanced account abstraction layer supporting gasless transactions, intelligent fee sponsorship, and ERC-20 token payments. Uses behavioral analysis to determine gas subsidy eligibility automatically.

The percentage of transactions that receive gas subsidies through Luntra's intelligent account abstraction system, indicating the effectiveness of AI-driven fee sponsorship mechanisms.

The time required to generate zero-knowledge proofs for disputed transactions in Luntra's hybrid rollup system, critical for maintaining fast dispute resolution without compromising security.

A novel consensus contribution mechanism in Luntra where network participants can contribute computational resources for AI inference and model training in exchange for network incentives and governance tokens.

The entity responsible for submitting state proposals to Ethereum L1, operating with AI-enhanced validation to ensure proposal accuracy and detect potential manipulation attempts.

The duration needed to verify recursive zero-knowledge proofs in Luntra's advanced cryptographic system, important for maintaining network throughput while supporting complex AI operations.

Advanced cryptographic techniques used in Luntra's hybrid rollup system to create proofs of proofs, enabling efficient verification of complex AI computations and large-scale machine learning operations.

Luntra's specific configuration parameters within the OP Stack framework, including AI system settings, agent execution limits, and machine learning resource allocation parameters.

Luntra's enhancements to the OP Stack Safe multisig system, incorporating AI-powered transaction analysis and automated risk assessment for multi-signature operations.

Automated systems within Luntra that detect and resolve network issues, performance bottlenecks, and security vulnerabilities using AI analysis and predetermined recovery procedures.

A protocol upgrade that separates transaction validation from execution, enabling features like gasless transactions, batch operations, and programmable account recovery mechanisms.

Dynamic trustworthiness metric computed by ChainSage™ based on wallet activity patterns and behavior analysis, used for access control, incentive prioritization, and risk assessment.

Luntra's support for opening payment and state channels that can execute AI agents and machine learning operations off-chain while maintaining the security guarantees of the main rollup chain.

The time required for Luntra nodes to synchronize with the latest network state, including AI model updates and agent state changes, critical for maintaining network consistency.

Luntra's integration with the broader Optimism Superchain ecosystem, enabling seamless interoperability with other OP Stack-based rollups while maintaining AI-native capabilities.

Transaction throughput measurement while utilizing Ethereum as the data availability layer, with Luntra achieving 700+ transactions per second through optimized batching and blob storage utilization.

A system in Luntra that allows users to cryptographically verify the integrity and authenticity of AI models before execution, ensuring that deployed models match their claimed specifications and haven't been tampered with.

Privacy-preserving identity solution using zk-SNARK cryptography and Halo2 circuits, allowing on-chain credential verification (age, membership, qualifications) without personal information disclosure.

A metric measuring the effectiveness of Luntra's zero-knowledge identity system in protecting user privacy while enabling necessary identity verification for various applications.

Cryptographic methods that enable one party to prove knowledge of information to another party without revealing the actual information, used for privacy-preserving verification and scaling solutions.