In a landmark development for decentralized finance, Brevis, a pioneering zero-knowledge proof platform, has announced a strategic partnership with ARRO, a leading oracle network on BNB Chain, to fundamentally reshape the infrastructure of prediction markets. This collaboration, announced in early 2025, aims to replace subjective consensus with mathematical certainty, addressing long-standing challenges of data integrity and participant privacy that have hindered mainstream adoption.
The alliance represents a significant leap toward building more secure, private, and trustworthy decentralized applications for forecasting real-world events. Prediction markets allow users to trade contracts based on the outcome of future events, aggregating crowd wisdom into a powerful forecasting tool. However, traditional blockchain-based markets face two critical bottlenecks: reliance on oracle networks—third-party services that feed external data on-chain—which can be points of failure or manipulation, and a lack of privacy, exposing traders’ positions and strategies on a public ledger.
The Brevis-ARRO partnership directly targets these vulnerabilities by merging advanced zero-knowledge (ZK) cryptography with robust oracle design. This integration will enable market outcomes to resolve based on verifiable mathematical proofs rather than validator consensus. Essentially, the truth of an outcome becomes a provable computation, reducing reliance on social trust and minimizing attack vectors.
The partnership’s planned privacy infrastructure will allow institutional and large-scale investors to participate without revealing sensitive trading data, potentially unlocking billions in currently sidelined capital. In current transparent markets, sophisticated investors risk front-running and strategy copying. The joint solution will allow these entities to generate ZK proofs that validate their transactions—proving they have sufficient collateral or are not engaging in market manipulation—while completely concealing the transaction details, their overall portfolio position, and trading history.
The collaboration’s core innovation lies in combining Brevis’s specialized ZK stack with ARRO’s decentralized data delivery network. Brevis contributes three key technologies: the ZK Data Coprocessor for verifiable computations on historical blockchain data; zkTLS (Zero-Knowledge Transport Layer Security) for generating ZK proofs for data fetched from any TLS-encrypted website; and Pico zkVM, a lightweight virtual machine for generating succinct proofs for general-purpose computations.
ARRO’s oracle network will serve as the reliable data feeder. Together, they will create solutions for three critical data types in prediction markets: on-chain historical data, off-chain public data (like sports scores or election results), and proprietary algorithms. This technical framework ensures that every piece of data informing a market settlement is cryptographically verified, moving beyond the “trusted relay” model of many current oracles.
The prediction market sector has seen exponential growth since 2020, with platforms like Polymarket gaining traction. However, high-profile oracle manipulation incidents and regulatory scrutiny around data privacy have underscored the need for more robust infrastructure. The Brevis-ARRO initiative arrives as jurisdictions globally are crafting clearer frameworks for decentralized applications, making verifiable and private computation a competitive necessity.
Industry experts point to the composability of this new infrastructure. A developer could, for instance, build a prediction market on the outcome of a climate metric, using zkTLS to prove data came from NASA’s website, the ZK coprocessor to verify a trend from past on-chain weather contracts, and the privacy layer to allow a hedge fund to trade anonymously. This opens a new design space for DeFi applications.
The impact could extend beyond betting platforms to insurance, conditional payments, and any smart contract requiring verified real-world data. This partnership builds upon years of ZK research, applying matured technology to the specific, data-heavy problem of prediction markets.
