blockchain infrastructure teams

Abstract: Permissioned blockchains authenticate transactions, votes and quorum certificates with computationally secure signatures, and post-quantum replacements inflate consensus traffic by orders of magnitude. Information-theoretic alternatives have required a trusted authority, quantum channels, per-signature interaction, or single-use keys with bounded transfer. We present SILMARILS-MV, a multi-verifier unconditionally secure signature in which a signature is the vector $α= A\,e(m)$ and each verifier holds a secret functional $c_i = Γ^T λ(x_i)$ derived from virtual anchors $Γ$ that nobody learns. Against unbounded adversaries corrupting up to $ω$ verifiers and any signers, and observing every decision, an honest verifier accepts a forged or invalid signature with probability at most $Q/q + 1/(q-1)$, a bound that is tight in its leading term. The scheme is strongly unforgeable, non-repudiable, transferable across unboundedly many hops, and linearly aggregatable. Keys are issued by the validators through perfectly secure MPC for $t < n/3$, without broadcast. To scale, we let the committee itself sign: in threshold notarization, finality certificates have constant size $32(ω+1)$ bytes and per-validator issuance cost no longer depends on $n$. Arbiter-verified keys give clients 32-byte signatures, robust redistribution supports membership changes and share refresh, and verifiers can be enrolled retroactively. A certificate verifies in 2.4 microseconds. In an analytic normal-case model at 1000 validators, threshold notarization needs 71 KiB of authentication traffic per validator per block, 22x less than ML-DSA-44 for admitted verifiers. In a Rust HotStuff prototype with 100 validators, SILMARILS-MV commits 2.8x more transactions per second than ML-DSA-44 with 67x less cryptographic CPU, and Monte-Carlo attacks match the bounds.

Abstract: Computing in the network (COIN) supports latency-sensitive Metaverse services, but blockchain commitment of persistent shared-state updates competes with application execution for limited resources. We consider the joint problem of deciding which updates to commit, how much Byzantine redundancy to provision, and where to execute Metaverse application tasks. We formulate this problem under resource, latency, and practical Byzantine fault tolerance (PBFT) constraints, accounting for update persistence and semantic significance. We develop a hierarchical redundancy-aware PBFT (R-PBFT) approach that decomposes the decision process into slot-wise computation offloading and sequential update-admission and redundancy control. We formulate the former as an identical-interest game and address the latter using a feasibility-filtered double deep Q-network (DDQN). Analytical downlink allocation accounts for the energy and delivery-feasibility effects of consensus latency. For fixed slot state and redundancy, we establish the game's exact-potential property and finite termination of strict sequential improvements. Feasible-action filtering enforces configured Byzantine redundancy and blockchain-budget constraints independently of learning. Numerical results show relative improvements of up to $46.7\%$ in reward averaged across tested user loads and seeds over Minimum Secure, the strongest baseline on average. At the highest tested Byzantine bound, the secure timely-update ratio improves by $62.3\%$ over the same baseline. Ablations assess the contributions of feasibility filtering and adaptive deferral.

Abstract: Layer 2 (L2) blockchain solutions emerged in recent years, predominantly in the Ethereum ecosystem, as a viable option for scaling a main blockchain network (L1). However, the selection of a L2 solution for deploying smart contracts backing blockchain applications is a decision that depends on many factors, such as costs, efficiency, popularity, and stability. We propose here a systematic methodology to help with this decision making process by using a set of relevant metrics and introducing user-defined profiles that capture different priorities among these metrics. We collect the major categories of smart contracts from a community-maintained data source, and we gather transactions for a six months period from three major L2 solutions: Arbitrum, Optimism, and Base. For each contract category and each blockchain, we compute a set of metrics and scores tailored to each user profile. By applying our methodology, we identify a clear bias in the classification, showing which L2 solutions are better suited for particular application types and user profiles.

Abstract: DAG-based consensus protocols can achieve great throughput and the optimal three-message-delay limit for n = 3f+1 consensus. While two-delay protocols exist, they pay with reduced resilience (requiring 5f+1-style committees) or rely on fallbacks that sacrifice the DAG's high throughput. This paper introduces Hydrozoan, the first DAG protocol with a dual commit path under a hybrid fault model of f Byzantine and c crashed validators, on n = 3f+c+2p+1 validators. Leaders commit in two message delays whenever at most p validators are faulty, and in three otherwise, with no extra messages, no view changes, and multiple leaders per round. Both paths are evaluated on the same DAG, using a novel graded indirect rule to reconcile them so that every honest validator reaches the same decision. We show that under geo-distributed conditions, which path is faster is a property of geography rather than the protocol, as rounds reaching a remote region cost far more than those that do not. The (f, c, p) knobs place the fast quorum where the deployment requires it, allowing a commit in two message delays. If misconfigured, Hydrozoan can still commit in three message delays: Hydrozoan commits on whichever path fires first. We also present Optimal-Hydrozoan, a variant that tolerates one more fault on the fast path, the first construction to match the known lower bound. The safety and liveness of both protocols are machine-checked in Lean 4. Our geo-distributed evaluation shows that Hydrozoan matches Mysticeti's throughput, commits ~25% faster when the fast quorum fits fast regions, and falls back to three message delays when it does not or past p faults, where existing two-delay protocols stall.

Abstract: We study cross-chain arbitrage when autonomous AI agents, rather than humans or bots, are the searchers. We model agents as both arbitrage extractors and Maximal Extractable Value targets, derive the optimal trade size for a risk-averse agent under mean-variance utility with stochastic bridge delays, and formalize multi-chain path selection as a belief-weighted online learning problem whose belief estimates converge under a Robbins-Monro schedule. Using 23,000 Uniswap V3 swap events across Ethereum, Arbitrum, and Base, we find that Ethereum-Arbitrum price gaps average 0.044% at 10-second resolution and Arbitrum--Base gaps average 0.013%, so $10,000 trades clear in 63% of L2-L2 windows via CCTP while L1-L2 routes require $50,000 or more for comparable viability. Our adaptive path-selection algorithm outperforms standard baselines by 11% on average, and moderate randomization cuts MEV exposure by over 50% with only modest profit loss.