Explore the foundational ideas of crypto. The Research category breaks down cutting-edge whitepapers, academic studies, and the technical theories shaping the future of blockchain technology.
This research establishes a rigorous, abstract theory of MEV, enabling formal security proofs against economic attacks that exploit transaction ordering.
This research advances zero-knowledge proofs, offering new cryptographic designs to fundamentally improve privacy and scaling for decentralized systems.
This research formalizes Maximal Extractable Value, providing a rigorous framework for understanding and mitigating systemic blockchain vulnerabilities.
This research establishes a universal, game-theoretic definition for Maximal Extractable Value, fundamentally reframing economic attacks within public blockchains for systematic mitigation.
This research introduces a novel Zero-Knowledge Proof of Training consensus, fundamentally transforming how blockchain-secured federated learning achieves verifiable privacy and efficiency.
This research formalizes Maximal Extractable Value dynamics through a multi-stage game, revealing systemic inefficiencies and quantifying mitigation strategies.
This research formally models Maximal Extractable Value dynamics, proving its systemic welfare costs, and proposes cryptographic mechanisms to mitigate its adverse effects on decentralized finance.
This research integrates large language models with formal verification to automatically generate precise properties, fundamentally enhancing smart contract security.
This research establishes Maximal Extractable Value as the primary economic constraint on blockchain scalability, advocating for new auction designs to efficiently allocate blockspace.
This research introduces revelation mechanisms within Proof-of-Stake protocols, fundamentally addressing consensus disputes by incentivizing truthful block proposals.
Kaizen introduces a zero-knowledge proof system dramatically accelerating verifiable deep learning model training, unlocking privacy-preserving AI at scale.
This research introduces Execution Tickets, a novel mechanism to integrate Maximal Extractable Value directly into the Ethereum protocol, shifting value capture from external entities to the network itself.
This research fundamentally transforms zero-knowledge proofs, introducing protocols that achieve linear prover times and succinct proof sizes, enabling widespread privacy-preserving computation.
This theory formally defines Maximal Extractable Value, offering a robust framework for proving smart contract security and clarifying adversarial extraction in blockchains.
This research establishes the fundamental equivalence between resettable statistical zero-knowledge arguments and witness encryption, resolving a longstanding open problem.
This research introduces a suite of novel zero-knowledge proof protocols that dramatically accelerate proof generation, unlocking scalable and privacy-preserving decentralized systems.
This research formally models MEV as a multi-stage game, revealing competitive dynamics that degrade welfare and quantifies mitigation through commit-reveal schemes.
This research unveils the inherent challenges of transaction fee mechanism design in MEV-rich environments, proposing a novel framework to balance incentives.
A novel ticketing mechanism aims to integrate Maximal Extractable Value directly into the Ethereum protocol, fostering fairer distribution and network robustness.
This research introduces novel protocols dramatically enhancing zero-knowledge proof generation speed, unlocking new capabilities for scalable, privacy-preserving decentralized systems.
Maximal Extractable Value has become the dominant economic constraint on blockchain scalability, demanding a paradigm shift to efficient, explicit MEV markets.
This research comprehensively maps vulnerabilities across SNARK implementation layers, shifting focus from theoretical guarantees to practical security challenges.
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