blockchain platform operators

Abstract: Demand on blockchain-based marketplaces arrives in bursts and at a high rate. On these platforms the data are completely visible, which makes it feasible to compare the bursty demand against the usual load. The congestion manifests as an increased demand for gas, the primary resource a blockchain sells. Existing studies consider how to price the gas. The demand itself, and what it is made of during bursts, has not been measured. We measure it on Robinhood Chain, a layer-2 blockchain that quickly became a venue for trading tokenized equities and memecoins. We collected the data from every block of its first three active months and more than 50 million Uniswap swaps that occurred during bursty periods. We find that a burst trades about as many pools as a quiet period, mostly different ones, and trades them harder, on top of elevated demand that accumulated for hours. With the bursts aligned on their peaks, the median peak is 2.5 times the usual load. Both the rise and the fall of the demand are described by the sum of two exponential functions with different timescales, a slow one of hours and a fast one of minutes. Against a matched quiet period, a burst has 1.05 times the pools, 1.40 times the trades, and 1.21 times the senders. We use two models to explain the arrival of bursts: externally triggered cascades and self-excitation. The former fits the data relatively better. Inside a burst, the entry of each trader into a pool is followed for about 24 seconds by more entries than the existing traders predict. Whether this is because of imitation or because of a shared signal is left open.

Abstract: Existing governance approaches in decentralized financial networks rely on manual review processes conducted by human committees, which introduces subjective bias, creates a scalability bottleneck under high network volume, and fails to capture complex behavioural patterns and evolving network dynamics. To address these limitations, we propose \name, a federated learning-based pairwise ranking framework in which multiple institutions collaboratively train a ranking model without sharing their private transaction data. This collaborative learning enables the model to capture network-wide behavioural patterns that signify strong network participation and directly contribute to the network effect, including diversity of counterparties, usage of multiple applications, and frequency of transactions. The proposed system ranks providers based on the authenticity of their actions, promoting genuine contributors and demoting those exhibiting suspicious behaviour. We evaluate \name\ on a real-world blockchain transaction dataset. Results demonstrate that the federated approach achieves ranking performance modestly below the centralized baseline (NDCG@15: $\approx$0.87 vs.\ $\approx$0.92) while preserving data privacy and matching its ranking stability (Kendall $τ$: $\approx$0.70 for both regimes), producing reliable governance decisions, including provider promotion and demotion (648 and 39 unique providers, respectively), without requiring centralized access to sensitive transaction data.

Abstract: An optimistic rollup is safe as long as at least one honest validator executes its state transition function (STF) and disputes any assertion that is inconsistent with the result of the execution. Allowing anyone to participate, however, does not give incentive to do so: as long as every assertion is correct, verifying assertion correctness goes unpaid. Attention mechanisms address this by paying nodes to execute the rollup's STF, regardless of whether they participate in the validation process. Since a single node operator can back many registered identities with a single execution, paying per identity does not buy execution diversity, that is, the number of operators that independently execute. In this work we provide a new modeling framework for this problem. We formalize the underlying cryptographic primitive as a new notion, arguments with registered provers, whose properties include a form of non-amortizability that, to our knowledge, has not been studied for modern succinct cryptographic proofs before. We design validation and attention reward mechanisms and analyze them as two coupled Tullock-like contests, in which an operator pays once for the required execution and then chooses how many identities to deploy in each role. We quantify the attainable range of execution diversities in equilibrium as a function of the marginal cost of deploying an extra attention identity. We use our framework as a lens through which we compare two attention mechanisms, TRACE (MARBLE 2026) and Proof of Diligence (AFT 2024). In particular, we show that as opposed to Proof of Diligence, in TRACE the marginal cost mentioned above can be adjusted to support a higher execution diversity. As a case study, we apply our results to Arbitrum One, a widely deployed optimistic rollup.

Abstract: Ahead-of-time (AOT) compilers (e.g., revmc, evmone, and DTVM) for the Ethereum Virtual Machine (EVM) reuse compilation artifacts at contract-code-hash granularity. This granularity is poorly matched to real EVM workloads dominated by \emph{contract families}: factory-, proxy-, and template-driven deployments that share instruction structure but differ in a small set of embedded constants. Across four EVM chains (Base, Ethereum, BSC, and Arbitrum), we find that 23.1--47.6\% of unique compilable bytecodes map to shared family skeletons within 10K-block windows. Per-hash AOT therefore redundantly recompiles structurally equivalent code, inflating compile time and artifact footprint while reducing workload coverage under finite compile budgets. We present \textsc{SkelOT}, an AOT framework that lifts the unit of compilation reuse from code hash to family skeleton. \textsc{SkelOT} compiles one native artifact per family, bakes invariant constants into the artifact, and reads variant constants from a per-contract runtime table. Built on revmc/LLVM and evaluated on a 10K-block Base mainnet corpus (3.52M transactions), \textsc{SkelOT} reduces compilation units by 47.5\%, artifact footprint by 57.4\%, and compile time by $2.19\times$, while preserving byte-identical execution outcomes versus per-hash AOT. At runtime, \textsc{SkelOT} delivers a $1.31\times$ median per-contract speedup across family members. Under a compile budget targeting 75\% execution-time coverage, \textsc{SkelOT} needs far fewer artifacts than per-hash AOT, and the advantage holds at every coverage target.

Abstract: We study NFT-based reward mechanisms in which a user can create multiple identities and submit fraudulent claims that mature a reward subject to vesting. We assume that the issuer stochastically verifies claims during the vesting period and that identities can be linked into clusters so that the detection of one identity submitting a fraudulent claim causes the whole cluster to be forfeited through a penalty. A farmer's payoff is then non-linear in the number of identities: rewards increase linearly, while the probability of avoiding detection decreases geometrically. We characterise the optimal farming scale in the continuous relaxation of the problem. This allows us to derive a sufficient condition for deterrence and then consider the issuer's choice of reward vesting and claim verification. Vesting reduces the probability that a fraudulent claim is paid but also affects genuine participants, while verification is costly for the issuer. We characterise the sufficient deterrence frontier for the issuer in terms of auditing cost, vesting schedule, and penalty value. When small amounts of audit capacity can be added at negligible marginal cost, vesting alone is not optimal. We also distinguish the role of penalties from that of cluster-level auditing. Finally, we discuss the implications for NFT reward programmes on high-throughput ledgers, such as Hedera.