Boundless Mainnet Activates, Rewiring Blockchain Economics with Verifiable Compute
Boundless's mainnet launch introduces Proof of Verifiable Work, a novel mechanism that aligns incentives with useful computation, setting a new standard for blockchain scalability.
Ethereum Fusaka Upgrade Enhances Layer 2 Data Availability
The Fusaka upgrade introduces PeerDAS, a data availability sampling mechanism, optimizing Layer 2 operational efficiency and expanding network throughput.
ZKPoT: Private, Efficient Consensus for Federated Learning Blockchains
A novel Zero-Knowledge Proof of Training consensus validates federated learning contributions privately, overcoming traditional blockchain inefficiencies and privacy risks.
Ethereum Advances Layer 2 Scaling, Targeting Tenfold Throughput
Ethereum's architectural evolution prioritizes Layer 2 solutions to achieve a 10x scaling factor, enhancing network throughput and interoperability.
Optimizing Zero-Knowledge Proofs for Practical Scalability and Efficiency
This research introduces novel Zero-Knowledge Proof protocols that significantly reduce prover time and enhance efficiency, enabling scalable and trustless applications in blockchain and AI.
Boundless Mainnet Activates, Revolutionizing Blockchain Verifiable Compute
Boundless’s mainnet activation introduces Proof of Verifiable Work, establishing a direct market for useful computation crucial for internet-scale blockchain applications.
OR-Aggregation: Constant-Size ZKPs for Resource-Constrained Networks
This research introduces a novel OR-aggregation technique, fundamentally transforming privacy and verifiable computation efficiency in resource-constrained environments.
Verifiable Work Reshapes Blockchain Incentives for Scalable, Purposeful Computation
Boundless introduces Proof of Verifiable Work, a paradigm shift from arbitrary cryptographic puzzles to rewarding useful computation, enhancing blockchain scalability and efficiency.
Systematic Survey of Zero-Knowledge Proof Frameworks and Applications
This research systematically evaluates zero-knowledge proof frameworks, demystifying their capabilities and guiding developers towards optimal privacy-preserving solutions.
