谛听Diting Lab

Crypto & Blockchain · Sat, Oct 17, 2026

Bitcoin, Ethereum, stablecoins, DeFi, smart contracts, scaling and consensus.

arXiv cs.GT

Blockchain Participation Games with Upfront Contribution Commitment

In briefBlockchain participation games with upfront contribution commitment

SummaryThis paper studies blockchain participation games where entry is costly, contributors are randomly selected, and success requires reaching a threshold. It characterizes all pure Nash equilibria under homogeneous selection and analyzes how heterogeneous selection affects who commits. The authors show a minimum-outlay reward scheme that supports full contribution as a Nash equilibrium, and note that requiring zero registration reward can raise the minimum outlay under heterogeneous selection.

Zhenhe Zhang

arXiv cs.CR · cs.DC

Empirical Analysis of TEE-Enabled Rollup Designs

In briefEmpirical Study of TEE-Based Rollup Sequencer Designs

SummaryThis paper experimentally evaluates designs that use trusted execution environments (TEEs) to strengthen Ethereum Layer 2 rollup sequencers against censorship and manipulation. It compares separating proposer and builder logic (PBS) into independent services with a monolithic sequencer design. The experiments find that TEEs have a small impact on rollup performance, and PBS does not significantly amplify that impact. The authors also report that PBS can yield significant performance gains through more specialized, efficient builder implementations.

Claudiu-Nicu B\u{a}rbieru, Corentin Libert, Peterson Yuhala et al.

arXiv cs.SE · cs.AI · cs.CR

Towards an Intelligent Decision Engine for Smart Contract Deployment

In briefA decision engine for choosing where to deploy smart contracts

SummaryThis paper proposes a systematic method to help choose among Ethereum layer 2 (L2) blockchains for deploying smart contracts. It uses a set of metrics and user-defined profiles that reflect different priorities among them. The authors collect major smart contract categories from a community-maintained source and six months of transactions from Arbitrum, Optimism, and Base, then compute metrics and scores per category and chain.

Oana-Otilia Captarencu, Emanuel Onica, Andrei Arusoaie

arXiv cs.CR

ABF-Bench: A Verifiable Benchmark for Agentic Blockchain Forensics

In briefABF-Bench: A Verifiable Benchmark for Agentic Blockchain Forensics

SummaryThis paper introduces ABF-Bench, an interactive benchmark that tests whether AI agents can trace cryptocurrency funds using public ledger records, requiring each claimed transfer to cite records the agent actually retrieved. The authors also propose the ABF agent harness, built around a working memory, and compare it with general-purpose agents and fixed-order tracers. The harness reaches 75% edge recall, versus 47% for Codex and 10% for Claude Code. The paper also notes that agents often stop early and lose recall on larger tasks.

Xiaohang Yu, Hejia Geng, William Knottenbelt