This bounty invites submissions that advance the use of Zero-Knowledge (ZK) proofs in the realms of large-scale deep learning models (e.g., LLMs) and/or the Ethereum ecosystem. We seek projects that explore novel applications, enhancements, and implementations of ZK technology to address challenges in privacy, scalability, and efficiency.
Submissions may focus on one or more of the following areas:
Proof Systems for Deep Learning Inference: Design and partial implementation of proof systems for the inference of large deep learning models, offering new benefits or trade-offs over current systems. Innovations or improvements to existing proof schemes are welcome; building a completely new system is not required.
Applications Leveraging Efficient Deep Learning Inference: Develop applications that utilize efficient inference of deep learning models. Innovation in proving inference is optional, but excellence in application development is key.
Zero-Knowledge Proofs in Ethereum and Layer 2 Solutions: Use ZK proofs to enhance privacy, scalability, and functionality within Ethereum and Layer 2 networks. Projects aligning with Vitalik’s singularity roadmap or zkEVM solutions, improving proof aggregation, or creating educational tools are encouraged.
Proof Aggregation and Optimization: Enhance scalability through proof aggregation, advances in SNARK folding, or optimizing SNARK/STARK systems.
Novel ZK Applications: Surprise us with a project that utilizes ZK in new creative ways!