Batching the Proof Queue: IBM’s GAPP Targets Cheaper ZK Aggregation

Zero-knowledge systems increasingly face a batching problem: applications may need to prove many related computations, but generating and verifying each proof separately creates avoidable cost. A paper presented at CRYPTO 2026 proposes GAPP—Generic Aggregation of Polynomial Protocols—as a general answer to that problem.

The framework reduces many univariate polynomial identities to one bivariate polynomial identity. The resulting relation can then be handled through a bivariate polynomial commitment scheme. In practical terms, the approach packages a collection of proof obligations into a single aggregate proof instead of treating every obligation as an isolated artifact.

According to the authors’ IBM Research summary, an aggregate proof has O(log n) size for n instances and can be verified with O(log² n) operations. The paper also introduces bPCLB, a bivariate polynomial commitment scheme, and a folding technique called Lagrangian folding. These are intended to make the aggregation pipeline efficient without requiring a separate bespoke aggregator for every polynomial protocol.

The most relevant result for ZK builders is the PLONK experiment. The authors report 25–30% faster proof generation than the naïve baseline of producing n separate PLONK proofs. They also outline applications to tuple lookups and non-uniform computation, suggesting that aggregation could become a reusable layer beneath several proof-system components rather than a feature tied to one rollup design.

That distinction matters for engineers. GAPP is not a drop-in SDK, a new proving network, or evidence that every workload will see the same improvement. The reported 25–30% gain is an experimental result from the authors’ implementation and should not be treated as a universal production benchmark. GAPP is a research framework presented at CRYPTO 2026, not a production-ready library or deployment guarantee.

The practical takeaway is architectural: when a system already produces polynomial proofs, the next optimization may be to aggregate the proof queue before it reaches the verifier. Teams evaluating this direction should inspect the commitment assumptions, field and curve compatibility, setup requirements, memory profile, and behavior under non-uniform workloads before considering integration.