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- 🎉 Three cheers for the weekend
🎉 Three cheers for the weekend
Some weekend reading of interest

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Gm! Donovan is out sick today, but we’ve got Luke Leasure filling in for us from the Blockworks Research team. Kick back, relax, grab a drink, and check out Luke’s weekend reading list.
Weekend Reading
The IMF published a working paper titled “Blockchain Consensus Mechanisms: A Primer for Supervisors (2025 Update).” This paper provides an overview of key blockchain consensus mechanisms—including Bitcoin’s Proof-of-Work (PoW), Ethereum’s Proof-of-Stake (PoS), and Solana’s hybrid Proof-of-History (PoH) + Tower BFT. It explores how these mechanisms impact supervisory goals like operational resilience, governance, market integrity, and settlement finality. It also surveys Layer 2 scalability solutions (e.g., rollups, state channels, sidechains), highlighting new risks related to slashing, validator concentration, staking-as-a-service, and restaking leverage. The paper stresses the need for supervisory familiarity with consensus design to mitigate financial stability risks as blockchain adoption in regulated markets grows. Read more.
The Bank for International Settlements published a working paper titled “Harnessing artificial intelligence for monitoring financial markets.” The authors propose a hybrid approach combining a recurrent neural network (RNN) with a large language model (LLM) to forecast and interpret financial market stress. They focus on deviations from triangular arbitrage parity in the EUR‑JPY pair, using the RNN to generate interpretable, time‑varying weights on input variables. The weights guide the LLM to search for narrative explanations around high‑signal variables. In tests, the model successfully anticipates periods of market dysfunction (such as before March 2023 banking turmoil) and provides contextualized signals for regulators. Read more.
Lucas Tcheyan and Arjun Yenamandra of Galaxy published a research report titled “Decentralized AI Training: Architectures, Opportunities, and Challenges.”The report surveys emerging decentralized AI training protocols (e.g. Nous Research, Prime Intellect, Pluralis, Templar, Gensyn), outlining their technical designs, incentive schemes, and verification strategies. It highlights key challenges: communication overhead, correctness verification, compute heterogeneity, and tokenomics. The authors argue that decentralized training is transitioning from conceptual to operational, but further breakthroughs in optimization, governance, and trust layers are needed before matching centralized AI capabilities. Read more.
BlockBandit published an article titled “Adventures in Spot Market Making.” In it, the author chronicles their experience building a spot market-making bot on Aster: covering strategy design, parameter tuning, order execution, and performance tracking. They delve into challenges like inventory risk, spread management, adverse selection, and volatility. The post offers lessons learned (e.g. dynamic spread adjustment, position limits), real results data, and reflections on what works vs. what doesn’t in automated market making on decentralized exchanges. Read more.
Happy reading, and enjoy the weekend!
— Luke
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