Would Rolling Up Thousands of Tiny Proofs into One Aggregated Proof Noticeably Cut Latency for Cross-Chain Oracle Updates?

Concise summary: Aggregating many proofs into a single proof can dramatically cut verification gas and blockspace, but it rarely lowers end‑to‑end latency for cross‑chain oracle updates; in many pipelines it increases it unless you use lightweight aggregation (Merkle/BLS) or run a hybrid “fast attestation + delayed ZK finality” path. We lay out concrete thresholds, architectures, and numbers to decide when aggregation helps and how to implement it safely.

The latency question you actually need to answer

“Will aggregation reduce our cross‑chain update latency?” translates to: which stage is your current bottleneck?

Source chain finality and batching
Cross‑chain transport (relay/bridge)
Destination chain verification cost and block capacity
Proof generation time (if you generate recursive/aggregated ZK proofs)
Calldata/DA fees and mempool inclusion

If destination‑chain verification is your bottleneck, aggregation often helps. If proof generation or source/destination finality dominate, heavy recursive aggregation can make latency worse (while still improving cost). Chainlink CCIP’s own docs emphasize that the main driver for cross‑chain message latency is time‑to‑finality on the source chain; finality varies widely—from sub‑second (Avalanche, Solana) to tens of minutes (Ethereum, many L2s when using “finalized” tags). CCIP batches finalized messages into a Merkle root per relay, so aggregation there is used to amortize costs, not primarily to shave latency. (docs.chain.link)

What “aggregation” really means (and why this distinction matters for latency)

There are three very different aggregation techniques in play:

Signature aggregation (BLS multi‑signatures)

What it does: many validators/oracles sign one message; you verify one aggregated signature on‑chain.
Why it’s fast: one pairing check can validate N signers. With BLS on Ethereum post‑Pectra (EIP‑2537), a 2‑pairing verify is roughly ~103k gas, replacing dozens of ECDSA verifications. This cuts on‑chain work to milliseconds without extra proving time. (eips.ethereum.org)

Merkle/batch commitments (no ZK)

What it does: commit to thousands of items in a Merkle root; verify signatures once on the root and check inexpensive Merkle proofs per item.
Why it’s fast: the heavy step is signature verification on the root; per‑item verification is just hashing. Pyth’s Perseus upgrade moved to a 400 ms slot cadence and verifies Wormhole signatures once per slot, achieving 50–80% cost reduction for multi‑feed updates without adding proof‑generation latency. (pyth.network)

Recursive/ZK proof aggregation (SNARK/STARK recursion, SnarkPack‑style)

What it does: compress thousands of proofs into one succinct proof verified on‑chain.
Why it’s tricky for latency: on‑chain verification becomes cheap (logarithmic pairings, O(1) gas scale), but generating the aggregated proof takes seconds to minutes depending on batch size and hardware. Aligned Layer reports that recursive aggregation adds minutes of latency (for “hard finality” on L1), whereas their off‑chain verification (aggregated BLS signatures) returns in milliseconds. (blog.alignedlayer.com)

In short: if your latency SLO is sub‑second to a few seconds, prefer signatures/Merkle batching; if you need infrequent, high‑security checkpoints with low on‑chain cost, use recursive ZK aggregation.

Where aggregation clearly reduces latency

Aggregation reduces latency only when on‑chain verification throughput is the bottleneck:

Blockspace limits: Ethereum L1 can verify only a limited number of proofs per block (pairing‑heavy verifiers). Off‑chain verification plus aggregated signatures (e.g., Aligned’s Proof Verification Layer) raises practical throughput to thousands of proofs per second and writes one batched result to L1. That avoids multi‑block queuing, which is perceived as latency by downstream applications. (blog.alignedlayer.com)
Per‑update signature storms: If your oracle update requires verifying dozens of publisher signatures on destination chains, BLS aggregation turns N signature checks into one, shrinking on‑chain time from many milliseconds to a single precompile call. After EIP‑2537 (activated in Pectra on April 23, 2025), BLS12‑381 precompiles make this pattern cheaper and faster than the BN254 alternative. (blog.ethereum.org)
Calldata‑bound L2s post‑EIP‑7623: If you’re shipping lots of signatures or proofs as calldata, rising calldata prices incentivize aggregation to keep payloads small. EIP‑7623 increases calldata cost for data‑heavy transactions; batching (signatures → one BLS; proofs → one SNARK) reduces bytes and pairing checks, helping avoid mempool delays and block‑size contention. (eips-wg.github.io)

Concrete number: verifying an aggregated BLS signature in the EVM takes on the order of ~113k gas with BN254 precompiles; with BLS12‑381 in Pectra the pairing cost per “pair” is ~70k gas and ~103k for a typical two‑pairing check, improving both speed and security parameters. That’s far faster than verifying dozens of ECDSAs individually. (ethresear.ch)

Where aggregation increases latency (often by minutes)

ZK recursion adds extra, sometimes large, prover time:

Aligned Layer’s Aggregation Service (testnet) reports “minutes for recursive proving,” trading throughput/gas savings for added latency. Their off‑chain verification mode is fast, but the on‑chain aggregated proof (“hard finality”) is slower to produce. (blog.alignedlayer.com)
Research and production systems show similar trade‑offs: SnarkPack aggregates thousands of Groth16 proofs with verification in ~163 ms (native), but aggregation time is seconds to tens of seconds depending on n and hardware—time you must wait before posting on‑chain. (eprint.iacr.org)
zkVM‑based recursion: recent zkVMs like SP1 Hypercube and RISC Zero R0VM have pushed toward sub‑12‑second real‑time proving for typical Ethereum blocks on large GPU clusters, but “minutes to tens of seconds” is still common outside ideal setups. If your oracle wants a 400 ms–2 s p95, recursive aggregation will exceed that budget today. (blog.succinct.xyz)

Bottom line: if your current bottleneck is source/destination finality, network propagation, or L2 sequencing, recursive aggregation won’t help latency and can hurt it; use it for cost/security, not speed.

Case study: Pyth’s cross‑chain pipeline—aggregation without ZK to keep latency low

Pyth aggregates publisher quotes on Pythnet, then every 400 ms publishes a Merkle root of all price updates; Wormhole guardians sign the root, and users submit the signed root + Merkle proofs to destination chains when they need the price “pulled” on‑chain. Only one signature set is verified per slot; per‑price verification is just Merkle checks. This raises update frequency (1 s → 400 ms) and cuts per‑update verification costs by 50–80% across ecosystems—all without introducing ZK proving delays. (pyth.network)

This is the pattern we recommend for latency‑sensitive oracle updates: aggregate with BLS/Merkle so verification is cheap and constant‑time on the destination chain while avoiding long proof‑generation waits. Pyth’s production metrics (hundreds of millions of updates per quarter) validate that this scales. (messari.io)

Case study: CCIP and “aggregation by finality window”

Chainlink CCIP explicitly waits for source‑chain finality, then relays a Merkle root of finalized messages to the destination. Aggregation here reduces per‑message verification cost, but overall latency is dominated by finality (e.g., ~15 minutes on Ethereum for “finalized” tag; similar minutes for many L2s). If your SLA targets seconds, your gating factor isn’t proof verification— it’s finality policy. Tuning the finality requirement (e.g., using block depth vs. finalized tag) moves the needle far more than cryptographic aggregation. (docs.chain.link)

Case study: zk light clients and bridges—aggregation improves cost, not always speed

Polyhedra’s zkBridge uses a two‑layer approach: a distributed prover (deVirgo) generates a fast STARK‑style proof of consensus, then a Groth16 “wrapper” compresses it for cheap on‑chain verification (~220–230k gas). They can prove and verify an Ethereum/Cosmos block header cross‑chain within roughly 12–20 seconds in benchmark setups. This is fast for validity bridges, but still much slower than signature/Merkle pipelines; batching more headers further lowers cost but increases the waiting time. (theblock.co)

The operational lesson: if you must deliver strict validity guarantees on every cross‑chain update, expect second‑to‑minute latency; you can batch to lower cost, but latency won’t shrink unless prover time drops below your SLO. (docs.zkbridge.com)

Hard numbers you can budget for

Groth16 verify on Ethereum: roughly 200k–250k gas + ~7k gas per public input; STARK verification >1M gas. Aggregating thousands of proofs into a single Groth16 can bring verify to a few pairings (O(1)/O(log n)); but aggregation latency is your trade‑off. (hackmd.io)
Signature aggregation: EIP‑1108 sets BN254 pairing cost to 34k·k + 45k gas; simple BLS checks often use two pairings (~113k gas). With EIP‑2537, BLS12‑381 precompiles lower costs per pair and raise security—great for oracle/bridge attestations. (eips.ethereum.org)
Off‑chain verification + BLS attestation: Aligned’s Verification Layer reports ~350k gas for a batch with one proof (any system) and ≈40k gas per proof at batch size 20, with verification latency in milliseconds (operators sign) and on‑chain read available after one block. (blog.alignedlayer.com)
Electron Labs’ Groth16 “super‑proof”: ~380k gas base per batch plus ~16k gas per consumer contract inclusion call amortized across n included proofs; per‑proof gas ≈ 16k + 380k/n. This is a clear cost win, but end‑to‑end latency depends on how long you wait to accumulate n. (docs.electron.dev)

The 3 patterns that work in production (and when to use them)

Low‑latency trading/risk engines (p95 < 1–2 s) → Merkle + BLS, no ZK on the hot path

Publish updates every slot (e.g., 400 ms).
Sign one root (BLS) per slot; verify once on destination; per feed = Merkle branch.
Example: Pyth Perseus (400 ms slots, one signature set, multi‑feed cost down 50–80%). (pyth.network)

Mixed latency/stronger assurances → Hybrid “fast attestation now, validity checkpoint later”

Immediate soft‑finality: off‑chain verification AVS signs result (milliseconds).
Periodic hard‑finality: recursively aggregate proofs and post a single validity proof to L1 every T minutes/blocks to cap trust. Example: Aligned’s two modes. (blog.alignedlayer.com)

“Every update must be validity‑proven” (bridges, security‑critical ops) → zk light client with tunable batching

Pick N (headers/updates per batch) to hit your fee targets.
Expect 10–120 s update latency in current stacks unless you run very large GPU clusters. Example: zkBridge’s deVirgo + Groth16 wrapper (~220k gas verify; 12–20 s cross‑chain in demos). (blog.polyhedra.network)

How to decide: a simple break‑even model

Define:

V: on‑chain cost/time to verify one “tiny” proof (e.g., 230k gas Groth16)
A(n): time to generate an aggregated proof of size n (recursive/pack)
G: per‑block gas limit for proofs (or your cost budget)
F_src/F_dst: source/destination finality wait

Then:

Non‑aggregated latency ≈ queue_delay(V, G) + F_src + relay + inclusion + F_dst
Aggregated latency ≈ wait_to_fill(n) + A(n) + inclusion + F_dst (often you already waited for F_src at relay)

Aggregation helps latency if and only if queue_delay(V, G) > wait_to_fill(n) + A(n). In practice, on L1 the queue delay is often the bottleneck only under load (many proofs per block). If your volume is modest, ZK aggregation primarily saves gas, not time.

Plugging numbers:

If you need to push 1,000 Groth16 verifies and the chain can clear ~180 per block (gas bound), you’re looking at multiple blocks of queuing. An aggregated proof verified once can clear the backlog in one transaction—provided you can generate it fast enough. If your aggregator takes 2–4 minutes to produce the recursive proof, this is worse for a 1‑second SLO but better for large batches under gas pressure. (blog.alignedlayer.com)

2025 updates that change the calculus

Pectra activated EIP‑2537 (BLS12‑381 precompiles). This reduces gas and raises security for BLS aggregation, making signature‑based batching even more attractive for low‑latency pipelines. (blog.ethereum.org)
EIP‑7623 increased calldata prices for data‑heavy txs; keep payloads lean (Merkle + one aggregated signature/proof) and prefer blobs on L2 where applicable. (eips-wg.github.io)
zkVM proving has become dramatically faster (SP1 Hypercube, R0VM 2.0), with sub‑12‑second proofs for most Ethereum blocks on large clusters. This makes “validity every block” realistic for certain bridges, but it still won’t beat BLS/Merkle for sub‑second oracle updates. (blog.succinct.xyz)

Engineering gotchas (learned the hard way)

Batched verification isn’t automatically safe. Zellic disclosed zero‑knowledge/soundness issues in gnark’s Groth16 extension for certain batched settings—audits and formal verif matter when you roll custom aggregation. Don’t assume “batch == safe.” (zellic.io)
Aggregation windows can create artificial back‑pressure. Dynamic batching with explicit SLOs (e.g., flush every X ms or Y items, whichever first) prevents “waiting to fill the batch” stalls during low traffic.
Don’t forget fees and mempool dynamics. On rollups, blobs (EIP‑4844) make large payloads cheaper and more stable; on L1, calldata can surge and EIP‑7623 penalizes data‑heavy bundles. Size your updates with current fee markets in mind. (blocknative.com)
Security vs. liveness: ZK “hard finality now” vs. BLS/Merkle “soft finality now + hard finality later” is an explicit product decision. Offer both paths where possible to let integrators choose.

Implementation patterns with precise details

Price‑feed fanout (EVM L2s): publish updates every 400 ms (slot); sign one root; app transactions include the latest update package (root + Merkle branches). Expect 50–80% multi‑feed verify cost savings, with negligible added latency. (pyth.network)
Proof‑heavy apps (ZK coprocessors, verifiable AI): verify proofs off‑chain in an AVS; post an aggregated BLS attestation (~one pairing check). Settled result available after one block; periodically (e.g., per hour) aggregate recursively to L1 for hard finality. Today this balances user experience with L1 security. (blog.alignedlayer.com)
Bridge validity checkpoints: if you must prove every header, batch headers to hit ~220–300k gas per verify on EVM (Groth16‑wrapped recursion). Tune batch size N to stay within your SLO; if you need <10 s, plan substantial GPU capacity or reduce N. (blog.polyhedra.network)

A quick decision checklist for teams

Target p95 latency
- < 1 s: Merkle + BLS. No ZK on the hot path.
- 1–30 s: hybrid (AVS fast attest + periodic ZK checkpoints) or small recursive batches with GPU capacity.
- 30 s: full ZK light client per update is feasible; aggregate aggressively to minimize fees.
Destination chain realities
- If finality is minutes (e.g., “finalized” Ethereum blocks), don’t expect cryptographic aggregation to change end‑to‑end latency; optimize finality policy/route selection instead. (docs.chain.link)
Cost/capacity constraints
- If you face proof‑verification gas saturation, aggregation reduces queuing latency by fitting into one tx—assuming you can produce the aggregated proof within your SLO. (blog.alignedlayer.com)
Security posture
- If your stakeholders require validity per update, accept higher latency or invest in real‑time proving clusters; hybridize where possible.

Practical next steps (what we deploy with clients)

Instrument your pipeline end‑to‑end: measure per‑stage p50/p95—source finality, relay, prove, verify, include. Decide based on data, not intuition.
Start with a Merkle + BLS design; keep a ZK checkpoint path you can turn on later.
If you explore recursion:
- Use hardened stacks (SP1, R0VM) and plan GPU capacity; run small‑N benchmarks with your real circuits and public inputs.
- Reuse proven aggregation schemes (SnarkPack‑style) and keep public inputs minimal to control calldata. (eprint.iacr.org)
- Audit batched verification logic; do not improvise extensions without reviews. (zellic.io)
On EVM:
- Prefer BLS12‑381 precompiles (EIP‑2537) for aggregation and future‑proof security.
- Keep an eye on calldata pricing (EIP‑7623); aggregate to minimize bytes; on L2s use blobs where supported. (blog.ethereum.org)

The verdict

Yes, aggregation can reduce perceived latency—when the bottleneck is on‑chain verification throughput or mempool inclusion due to large payloads.
No, aggregation does not inherently lower end‑to‑end latency for cross‑chain oracle updates; heavy recursive proving often adds seconds to minutes. To get both speed and cost efficiency, use signature/Merkle aggregation on the hot path and reserve recursive ZK aggregation for periodic hard‑finality checkpoints.

If your business depends on sub‑second, market‑reactive updates, ship a Merkle + BLS pipeline today (as Pyth does) and layer in ZK checkpoints later. If your use case demands validity proofs per message, plan for higher latency or real‑time proving infrastructure—then aggregate proofs to keep verification costs sane.

References and further reading:

Chainlink CCIP on execution latency and finality by chain. (docs.chain.link)
Pyth Perseus: 400 ms slots, single‑root verification, 50–80% cost reduction. (pyth.network)
Aligned Layer: milliseconds off‑chain verification vs. minutes for recursive aggregation; gas/throughput numbers. (blog.alignedlayer.com)
Electron Labs: Groth16 super‑proof gas math (~380k base + ~16k per‑proof access). (docs.electron.dev)
SnarkPack aggregation performance; verification and aggregation times. (eprint.iacr.org)
Pectra/EIP‑2537: BLS12‑381 precompiles now live; cheaper, faster, stronger aggregation. (blog.ethereum.org)
EIP‑1108 pairing costs; implications for on‑chain BLS. (eips.ethereum.org)
zkBridge: two‑layer proving (deVirgo + Groth16), ~220–230k gas verifies, 12–20 s demo latencies and batching trade‑offs. (blog.polyhedra.network)
Zellic on batch‑verification pitfalls in gnark’s Groth16 extension. (zellic.io)

7Block Labs can help you prototype both paths—a low‑latency Merkle/BLS oracle pipeline and a staged roll‑out of ZK checkpoints—benchmarking with your actual feeds and chains to choose the right break‑even points.