This talk presents Aegon, a self-auditable key transparency scheme designed for global-scale encrypted messaging. It builds on Iron Dict's polynomial commitment approach to remove per-epoch global work, shards the dictionary for horizontal scaling, and uses proof caching to bound storage. The result is constant-size audit proofs that a lightweight client can verify itself over an entire history, rather than relying on external auditors.
TruthTable is a verifiable database engine that produces succinct cryptographic proofs for SQL query execution. It supports a wide range of SQL queries by leveraging query plans, polynomial encoding, and operator-specific PIOPs. It features a query planner with proof-specific optimizations and a novel batch compilation engine (ARCPOP). Benchmarks on TPC-H show average proving times of 55 seconds, verification times of 32 milliseconds, and proof sizes of 24kB, significantly outperforming prior academic and industrial systems in speed and expressiveness.
Haechi introduces a novel, keyless, and commitment-based system for in-person verifiable elections, addressing the limitations of encryption-based approaches like ElectionGuard. By leveraging Peterson vector commitments and compact zero-knowledge proofs, Haechi ensures post-quantum ballot privacy, eliminates complex key management, and significantly reduces election record sizes, making E2E verifiable elections practical and scalable.
A summary of a talk on a new language-agnostic approach using abstract interpretation to find critical vulnerabilities in Zero-Knowledge Proof (ZKP) programs by modeling and detecting mismatches between prover computations and verifier constraints.
Circle CEO Jeremy Allaire delves into how programmable money and the Arc blockchain will power the emerging AI agentic economy. He explains how stablecoins like USDC offer an internet-native financial infrastructure for micro-transactions and large settlements, addressing the limitations of traditional banking for AI agents. The discussion covers the foundational principles of full-reserve banking, the unique attributes of Arc blockchain for machine-driven economic activity, the tokenization of real-world assets, and a bold vision for AI's potential to drive double-digit GDP growth and foster new on-chain organizational structures within the next decade.
Balaji Srinivasan discusses the paradoxical nature of AI, which lowers creation costs while simultaneously raising verification costs. He argues this tension pushes society toward a "trusted tribe" model, similar to the Chinese internet, where AI excels within high-trust groups but struggles between them. The conversation covers why physical tasks are easier to automate than digital ones, how AI makes everyone a CEO rather than obsolete, and why crypto, particularly Zcash, serves as a necessary counterbalance for inter-tribe transactions in an AI-driven world.