Blockchain Papers

Thanh Nguyen

Cryptocurrency trading has attracted tremendous attention from both retail and institutional investors. However, most traders fail to scale their assets under management due to fragile strategies that collapse during adverse markets. The primary causes are oversized leverage, speculative position sizing, and the absence of robust risk management or hedging mechanisms. This paper introduces Talyxion, an end to end framework for crypto portfolio allocation that shifts the paradigm from speculation to optimization. The proposed pipeline consists of four stages: universe selection, alpha backtesting, volatility aware portfolio optimization, and dynamic drawdown based risk management. By combining operations research techniques with practical risk controls, Talyxion enables scalable crypto portfolios that can withstand market downturns. In live 30 day trading on Binance Futures, the framework achieved a return on investment (ROI) of +16.68%, with the Sharpe ratio reaching 5.72 and the maximum drawdown contained at just 4.56%, demonstrating strong downside risk control. The system executed 227 trades, of which 131 were profitable, resulting in a win rate of 57.71% and a PnL of +1,137.49 USDT. Importantly, these results outperformed the buy and hold baseline (Sharpe 1.79, ROI 4.36%, MDD 4.96%) as well as several top leader copy trading bots on Binance, highlighting both the competitiveness and scalability of Talyxion in real world trading environments.

Andrei Damian, Petrica Butusina, Alessandro De Franceschi, Vitalii Toderian · 6 authors

We propose the Ratio1 AI meta-operating system (meta-OS), a decentralized MLOps protocol that unifies AI model development, deployment, and inference across heterogeneous edge devices. Its key innovation is an integrated blockchain-based framework that transforms idle computing resources (laptops, smartphones, cloud VMs) into a trustless global supercomputer. The architecture includes novel components: a decentralized authentication layer (dAuth), an in-memory state database (CSTORE), a distributed storage system (R1FS), homomorphic encrypted federated learning (EDIL), decentralized container orchestration (Deeploy) and an oracle network (OracleSync), which collectively ensure secure, resilient execution of AI pipelines and other container based apps at scale. The protocol enforces a formal circular token-economic model combining Proof-of-Availability (PoA) and Proof-of-AI (PoAI) consensus. Compared to centralized heterogeneous cloud MLOps and existing decentralized compute platforms, which often lack integrated AI toolchains or trusted Ratio1 node operators (R1OP) mechanics, Ratio1's holistic design lowers barriers for AI deployment and improves cost-efficiency. We provide mathematical formulations of its secure licensing and reward protocols, and include descriptive information for the system architecture and protocol flow. We argue that our proposed fully functional ecosystem proposes and demonstrates significant improvements in accessibility, scalability, and security over existing alternatives.

Wonkyo Choe, Rongxiang Wang, Afsara Benazir, Felix Xiaozhu Lin

Proto is a new instructional OS that runs on commodity, portable hardware. It showcases modern features, including per-app address spaces, threading, commodity filesystems, USB, DMA, multicore support, self-hosted debugging, and a window manager. It supports rich applications such as 2D/3D games, music and video players, and a blockchain miner. Unlike traditional instructional systems, Proto emphasizes engaging, media-rich apps that go beyond basic terminal programs. Our method breaks down a full-featured OS into a set of incremental, self-contained prototypes. Each prototype introduces a minimal set of OS mechanisms, driven by the needs of specific apps. The construction process then progressively enables these apps by bringing up one mechanism at a time. Proto enables a wider audience to experience building a self-contained software system used in daily life

Gauthier Voron, Vincent Gramoli

Byzantine State Machine Replication (SMR) is a long studied topic that received increasing attention recently with the advent of blockchains as companies are trying to scale them to hundreds of nodes. Byzantine SMRs try to increase throughput by either reducing the latency of consensus instances that they run sequentially or by reducing the number of replicas that send messages to others in order to reduce the network usage. Unfortunately, the former approach makes use of resources in burst whereas the latter requires CPU-intensive authentication mechanisms. In this paper, we propose a new Byzantine SMR called Dispel (Distributed Pipeline) that allows any node to distributively start new consensus instances whenever they detect sufficient resources locally. We evaluate the performance of Dispel within a single datacenter and across up to 380 machines over 3 continents by comparing it against four other SMRs. On 128 nodes, Dispel speeds up HotStuff, the Byzantine fault tolerant SMR being integrated within Facebook's blockchain, by more than 12 times. In addition, we also test Dispel under isolated and correlated failures and show that the Dispel distributed design is more robust than HotStuff. Finally, we evaluate Dispel in a cryptocurrency application with Bitcoin transactions and show that this SMR is not the bottleneck.