ZK Copilots: Verifiable AI Agents on Chain

What are ZK Copilots? These are autonomous AI agents whose decision-making is cryptographically auditable. Each inference or action they take is accompanied by a zero-knowledge proof that the result follows correctly from the agent’s model and inputs. In effect, a ZK Copilot computes off-chain (to avoid blockchain gas limits), then submits a succinct proof on-chain that its computation (e.g. a neural-net inference or decision-tree logic) was done honestly. Smart contracts can verify these proofs cheaply and trust the agent’s output without ever seeing the private data. This promises “trustless AI” where even powerful models (GPTs, recommendation engines, etc.) can be safely integrated into Web3 systems while preserving privacy and preventing tamperingmultialpha.commultialpha.com.

Architecture and Key Components

The typical ZK copilot architecture has several stages. First, the agent ingests or senses data (queries, market feeds, etc.) and runs an AI model off-chain to interpret it. Next it makes a decision (e.g. a trade, a recommendation, or a classification). Finally it produces a zero-knowledge proof attesting to the correctness of that decision given its model and inputs. The blockchain’s verifier checks this proof against the publicly committed inputs or model hash; if valid, the contract applies the certified action. Crucially, all private details (user data, model internals, etc.) remain hidden – only the proof and public outputs are exposedmultialpha.com. This pipeline relies on ZKML circuits or zkVMs to encode inference. For example, ML models (neural nets, decision trees, etc.) can be compiled into arithmetic circuits (rank-1 constraint systems) or algebraic intermediate representations. Frameworks like EZKL compile ONNX models into SNARK circuits, while Giza’s LuminAIR compiles ONNX graphs into Cairo (AIR) circuits for STARK proofsmultialpha.com. Alternatively, general-purpose zkVMs (like RISC Zero’s R0VM) simply run the model code in Rust/C++ and prove the entire execution trace with a STARKmultialpha.com.

• ZKML Circuits: ML inference (layers, activations, etc.) is expressed in a proof-friendly form. For SNARK-based systems (e.g. Modulus), models become R1CS constraints. For STARK-based systems (e.g. Giza), models become AIR/Cairo programs. In both cases the off-chain prover computes the model on secret input and generates a proof of correctnessmultialpha.com.

• Verifiable Inference: Once a proof is generated, a smart contract verifier checks it against public commitments (like the model hash and public inputs). This “verifiable inference” means the blockchain only sees the certified result, not the raw data. For instance, in Modulus Labs’ examples an on-chain chess game (Leela vs. the World) can verify each AI move with a ZK proofmultialpha.commultialpha.com.

• zkVMs and Acceleration: Platforms like RISC Zero offer a full zkVM. RISC Zero’s new R0VM 2.0 can prove complex workloads (an Ethereum block) in <1 minute, thanks to a 15× memory boost and optimized proof batchingmultialpha.com. Such VMs let developers write inference logic in familiar languages (Rust/C++) without manual circuit coding. Once the proof is on-chain, the contract logic (or ZK coprocessors like RISC Zero’s Steel) integrates the result. Steel2.0, for example, enables an EVM contract to offload multi-block or cross-chain computations off-chain and then verify a compact STARK proof on-chainrisczero.com.

• Smart Contract Integration: On-chain, a contract triggers the prover and later verifies its proof. Frameworks like Steel2.0 provide a Solidity interface: the contract gathers required inputs (historical state, logs, etc.), passes them to an off-chain prover, and then verifies the returned proof in constant gasrisczero.com. In this way, contracts can call external AI services accountably. For example, an oracle contract might call an AI model off-chain to forecast weather, then accept only a proof-verified prediction as truthmultialpha.com. Similarly, DAOs could include ZK-proofed AI logic in governance (e.g. proof that a calculated vote outcome truly followed a secret policy model).

In summary, a ZK Copilot stacks AI inference with ZK proof-generation and blockchain verification. The AI model and data are kept private in the prover, while the blockchain enforces correctness of every step via cryptographymultialpha.com.

Frameworks and Recent Progress (Apr–Jun 2025)

Several projects have made strides in ZK copilot technology in Q2 2025.

• Modulus Labs (Tools for Humanity): Builds specialized ZK pipelines (R1CS+SNARK) for ML. Their “Cost of Intelligence” report demonstrates proofs for models with ~18M parameters on-chainmultialpha.com. They showcased RockyBot, an on-chain trading bot whose every trade decision is backed by a ZK proof, and Leela vs. the World, a chess game where every AI move is verifiably correctmultialpha.com. In April 2025 Modulus (now part of Tools for Humanity) continued optimizing its prover (“Remainder”) with composed SNARK stacks (e.g. PLONK+Plonky2) to improve speed and memorymultialpha.com. Their approach yields small proofs and fast verification (ideal for contracts) but relies on heavy bespoke circuit engineering.

• RISC Zero: Offers a universal STARK-based zkVM (R0VM). On April 10, 2025 RISC Zero released R0VM 2.0multialpha.com, a major upgrade: proving an Ethereum block dropped from ~35 minutes to ~44 seconds, and proof costs fell ~5× in testsmultialpha.com. The new version also provides 3GB user memory (up 15×) for large ML workloads. Moreover, RISC Zero launched a formal-verification effort: working with Veridise/Picus to prove determinism of all RISC-V circuit componentsmultialpha.com. If complete, R0VM would be the first fully formally-verified RISC-V zkVM, eliminating many classes of ZK bugs. RISC Zero also advanced Steel 2.0 (April 2025), a ZK coprocessor for EVMs that lets contracts verify off-chain computations (event logs, cross-chain data, multi-block logic) via succinct proofsrisczero.com. Together, these push RISC Zero to be one of the fastest, most secure general zkVMs for ML and arbitrary computationmultialpha.com.

• Giza (StarkWare Ecosystem): Focuses on Cairo+STARK for ML. Early on they provided Orion, an ONNX-to-Cairo execution engine for STARK proofs, now evolving into LuminAIR. This framework compiles ONNX model graphs into Cairo programs proven via StarkWare’s Winterfellmultialpha.com. Giza envisions a decentralized AI marketplace: each model becomes an on-chain contract, and each inference is proven under STARK integrity checks. By May 2025 Giza demonstrated ONNX-based ML primitives in Cairo, highlighting STARK-based AI pipelinesmultialpha.com. Their stack requires no trusted setup (STARKs) and is quite parallelizable, but STARK proofs are larger, so on-chain verification (and gas) is more expensive.

• Aleo (Leo language): Uses a high-level DSL for SNARK circuits. In May 2025 Aleo held a zkML Hackathon, inviting developers to implement ML algorithms (linear regression, decision trees, neural nets, etc.) in Leo or build ZK “plugins” for PyTorch/TensorFlowmultialpha.com. The idea is to write ML logic directly in Leo, then have Aleo’s snarkVM prove it. Leo’s SNARKs yield very compact proofs and cheap on-chain checks (once the trusted setup is done). The tradeoff is less native parallelism (compared to STARKs) and the need for parameter ceremonies. Aleo’s roadmap also targets faster provers and enterprise features (e.g. Google Cloud support) to make this toolchain more competitivemultialpha.com.

Use Cases of ZK Copilots

ZK copilots unlock many trustless AI use cases in Web3:

• Autonomous Trading/DeFi Bots: Agents like RockyBot can run complex trading strategies off-chain and submit only proof-verified trade actions. Modulus Labs, for instance, demonstrated an on-chain trading bot whose moves were always accompanied by valid ZK proofsmultialpha.com. This prevents malicious or faulty trades, since a bad decision would fail verification.

• Verifiable Oracles & Prediction Models: Off-chain ML models can provide data (weather, price forecasts, sentiment analysis) with proofs. For example, a weather-prediction model could output a signed forecast plus a ZK proof of its inference, allowing an oracle contract to trustlessly consume that datamultialpha.com. Such proofs can automatically settle insurance claims or prediction markets. As noted by StarkNet researchers, an off-chain ML classifier can publish a proof of a prediction (e.g. temperature above a threshold) that smart contracts can use to trigger payoutsmultialpha.com. This makes oracles “honest-by-proof” and can eliminate trusted intermediaries.

• Private User Agents: Personal AI assistants (on-chain copilots) can handle sensitive data without leaking it. The agent computes recommendations off-chain on private inputs, and only a proof of the result is revealed on-chain. For example, a wallet assistant could analyze a user’s spending habits privately and prove it only executed the correct logic. The blockchain sees just the certified answer. In general, zero-knowledge ensures data privacy: as one analysis puts it, intermediate computations remain hidden and only the proof is shownmultialpha.com. This is ideal for, say, health or financial helpers where user data must stay confidential.

• Decentralized Gaming and Markets: ZK copilots enable provable AI in games or auctions. In a provably fair NFT drop or sealed-bid auction, for instance, bids or game moves can be encrypted off-chain and only proven to have followed the rules when revealed. Modulus’s Leela vs. the World chess example is one such case of a game NPC making provably correct movesmultialpha.com. More broadly, any on-chain marketplace could use ZKML to verify complex allocation or pricing algorithms without trusting a single operator.

Comparing ZK Copilot Frameworks

The leading platforms differ in proof system and design:

• Modulus (R1CS, Specialized SNARKs): Uses hand-optimized R1CS pipelines (often PLONK/recursive SNARKs). It yields small proofs and fast verification at scale – Modulus reports verifying 18M-parameter modelsmultialpha.commultialpha.com. However, it requires extensive circuit engineering (e.g. leveraging sumcheck/GKR proofs). Modulus circuits are proprietary, but their results illustrate how far R1CS pipelines can push ZKML performance.

• Giza (Cairo + STARKs): Compiles ONNX models to Cairo (an AIR language) and proves them with StarkWare’s Winterfell (a STARK engine)multialpha.commultialpha.com. There is no trusted setup and proofs are transparent. Giza aims for a “Web3 Hugging Face” model marketplace where models run on-chain with proofs for each inferencemultialpha.com. The tradeoff: STARK proofs are larger and proving work is heavier, but verification is constant-time. Giza’s Python/ONNX tooling is developer-friendly, and it can handle very large models by composing STARKs, at the cost of larger on-chain feesmultialpha.com.

• RISC Zero (zkVM + STARKs): Offers a general RISC-V-based zkVM. Developers write ordinary code (e.g. Rust) and R0VM produces STARK proofs of executionmultialpha.com. With 2.0, R0VM incorporates 15× more memory and sub-minute proving for hefty tasksmultialpha.com. It requires no custom ML circuit coding, making it easy to use, but proof generation remains heavy relative to highly specialized circuits. R0VM 2.0’s formal verification effort promises exceptional security (full determinism proved)multialpha.com. Conceptually, RISC Zero is a powerful off-chain computation layer (like a ZK “co-processor”) suitable for many applications, though its proofs are larger and slower than SNARK circuits.

• Aleo (Leo + SNARKs): Uses Leo, a Rust-like language that compiles to PLONK-based SNARK circuitsmultialpha.com. Its UX is polished for privacy dApps rather than raw ML. Aleo circuits yield very small proofs and very fast on-chain checks, making them attractive for high-throughput needs. The recent zkML hackathon shows developers can hand-write or generate circuit code for ML logic in Leomultialpha.commultialpha.com. The downsides are the need for a trusted setup and less out-of-the-box ML support. Aleo is improving prover speed (snarkOS v0.10) and usability, but as of mid-2025 Leo is best for cases where minimal proof size is critical and developers are willing to code models at a low levelmultialpha.com.

Each framework represents a point on the spectrum of generality vs. specialization. Modulus aggressively optimizes R1CS for scale; Giza and RISC Zero aim for broad compatibility via Python/ONNX or Rust code; Aleo Leo trades raw performance for developer ergonomics and tiny proofs.

Implications for Web3 Security and Governance

Autonomous Smart Contracts/DAOs: ZK copilots enable truly autonomous protocols. Smart contracts can call AI functions and trust their outputs by proof, eliminating central oracles. As one analysis notes, “smart contracts can integrate provable AI oracles, enabling autonomous DeFi protocols that adapt to real-world data”multialpha.com. In practice, a DAO could automate tasks (like rebalancing funds or adjusting parameters) by relying on proof-verified AI signals, with no human intervention required. Even blockchain consensus mechanisms might one day be informed by verifiable AI-driven votes.

Security: Zero-knowledge proofs bolster trust in AI. They ensure model integrity – one can prove that the model used is exactly what was promised, preventing hidden “backdoors” or poisoned modelsmultialpha.com. Likewise, inference integrity is guaranteed: every output is certified to follow from the inputs under the model. Verifiers need not trust any operator or hardware enclave. This is critical in high-stakes domains (finance, autonomous vehicles, etc.) where an AI agent’s behavior must be auditable and unforgeablemultialpha.com. Formal verification (as RISC Zero is pursuing) further raises the security bar by mathematically eliminating classes of proof bugs.

Regulation and Compliance: ZK copilots align with privacy and audit requirements. Enterprises can use them to prove compliance without revealing sensitive data. For example, a financial dApp could cryptographically prove it followed KYC/AML rules or data policies without exposing user information. As MultiAlpha observes, firms can “prove to regulators or auditors that an AI-driven decision process complied with data policies without exposing user data”multialpha.com. In effect, ZK proofs can satisfy regulations like GDPR or HIPAA by showing only the proof of correct behavior. This also aids enterprise adoption: companies can deploy AI agents on public chains, confident that internal secrets (models, user data) remain concealed even as regulators audit their outputs.

Decentralized AI Economy: Beyond security, these technologies enable a new ecosystem. Verifiable AI agents can power decentralized prediction markets, insure events via trustless oracles, or create AI marketplaces where model owners sell verifiable inferences. For instance, a contract could require a proof of a climate model’s prediction before settling a crop insurance claimmultialpha.com. Open marketplaces could arise where anyone can run an agent off-chain and offer proofs to on-chain clients. In this vision, DAOs and communities will govern not only code and funds, but also AI models—with every agent’s action attested by mathematics.

In summary, ZK copilots fuse AI and blockchain in a way that preserves decentralization and privacy. Recent advances from Modulus Labs, RISC Zero, Giza, and Aleo demonstrate that provable AI agents are moving from concept to realitymultialpha.commultialpha.com. As these platforms mature, we can expect a wave of innovative dApps—from trustless trading bots and oracles to private personal assistants—built on verifiable AI, reshaping Web3 security, governance, and compliance in the process.

Sources: In preparing this report, we reviewed recent announcements, blog posts, and research updates. Key references include technical overviews of ZKML pipelinesmultialpha.commultialpha.com, project milestones from Modulus, RISC Zero, Giza, and Aleomultialpha.commultialpha.commultialpha.commultialpha.com, and ecosystem analyses of verifiable AI use casesmultialpha.commultialpha.commultialpha.commultialpha.commultialpha.com. These sources provide the latest information (April–June 2025) on zero-knowledge AI agents and their implications for blockchain systems.