谛听Diting Lab

Crypto & Blockchain · Mon, Oct 19, 2026

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

arXiv cs.CR

Fortress: Fork Resistant Transparency logs Relying on Social graph Structure

In briefFortress: A Fork-Resistant Transparency Log Using Social Graph Structure

SummaryTransparency systems such as Key Transparency rely on external anchors like bulletin boards, blockchains, or out-of-band gossip among auditors to ensure everyone sees the same directory. This paper proposes using the social graph formed by users themselves as a replacement, formalizing a primitive called sg-FORT and constructing the Fortress protocol. In honest runs, out-of-band interactions occur only when users reset their secrets, and security is proven under standard cryptographic assumptions. The authors present this as a feasibility result under simplifying assumptions such as synchronous epochs and always-online users.

Anastasiia Kucherenko, Melissa Chase, Esha Ghosh

arXiv cs.AI · cs.LG · cs.MA

The Control Layer Behind Rule-Following Agents Trading Across Market Conditions

In briefTesting Whether AI Trading Agents Follow Their Rules or Just Get Lucky

SummaryThis paper tests whether four AI trading agents followed their declared strategies and risk rules. The authors analysed 2,627 closed positions, mostly simulated against live prices with a 0.09% round-trip fee, and 18% using real Hyperliquid orders. The fleet finished net positive after fees with high rule adherence, and the study provides a toolkit to separate rule-following from luck. The authors stress these are trade-level results, not proof of account returns or a lasting edge.

Saad Naja, Nasser Odtallah, Jake Van-Praag et al.

arXiv cs.CR · cs.DC · cs.NE

Peer-Relative Trust-Guided Update Screening for Robust Semi-Decentralized Federated Learning under Label-Flipping Attacks

In briefPeer-relative trust screening to resist label-flipping attacks in semi-decentralized federated learning

SummaryThis paper proposes a peer-relative trust-based update screening method to make semi-decentralized federated learning more robust against label-flipping attacks. It combines a robust validation-utility score with direction- and norm-based update quality measures. In experiments with 50 MNIST clients and 20% persistent attackers, the method reached 98.37% accuracy under moderate heterogeneity, rejecting 95.82% of malicious updates, and 95.25% accuracy under severe heterogeneity, rejecting 88.55% of malicious updates.

Ajay Kumar Shrestha, Sandhya Joshi

arXiv cs.CR · cs.ET

On Pearl Blockchain's Proof of Useful Work: Systematization and Flexibility

In briefOn Pearl Blockchain's Proof of Useful Work: Systematization and Flexibility

SummaryThis paper revisits the proof-of-useful-work (PoUW) scheme from the Pearl blockchain white paper. It offers a systematic sensitivity characterization as an alternative to the Jackpot policy, along with an efficient algorithm for verifying the sensitivity condition. Building on this, it proposes a more flexible PoUW scheme in which each miner independently chooses its noise magnitude to balance computational accuracy and mining efficiency.

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