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arXiv 最近完成扫描:2026-11-08 23:55 · 全站最近入库:2026-11-08 06:02各信源进度 →

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版本更新 · v2arXiv:2606.14404

Simple-IT:实用的低延迟无签名BFT共识

Simple-IT: Practical Low-Latency Signature-Free BFT Consensus

Qianyu Yu, Juan Villacis, Giuliano Losa, Zhuolun Xiang, Xuechao Wang

AI 速读 基于摘要

提出一种基于领导者的无签名拜占庭容错共识协议Simple-IT,作者报告其理论延迟为4个消息延迟、乐观路径为3个,并在跨地域测试床上取得与量子脆弱型先进协议相当的吞吐与延迟。

cs.DC

查看原文摘要

Recent advances in quantum computing pose a looming threat to most current Byzantine fault-tolerant (BFT) consensus protocols, which rely on quantum-vulnerable public-key signature schemes such as Ed25519 and BLS12-381. Instead of switching to much more expensive post-quantum secure signature schemes, an alternative is to use signature-free protocols, which rely only on cheap, post-quantum secure authenticated channels. In this paper, we ask whether signature-free BFT consensus protocols can match the performance of current state-of-the-art, quantum-vulnerable BFT consensus protocols. While previous work on the Sailfish++ protocol showed that state-of-the-art throughput is attainable signature-free, the question of latency is still open. Several recent signature-free protocols have low latency in theory, but they are all very intricate, and no practical implementation has so far been presented. In this work, we propose Simple-IT, a new leader-based, signature-free BFT consensus protocol that achieves a theoretical latency of 4 message delays (one more than the optimum), and only 3 on its optimistic path. Crucially, Simple-IT is simple enough to be amenable to implementation and to practical optimizations such as speculative pipelining, and, as we show experimentally in a geo-distributed testbed, it achieves both throughput and latency competitive with state-of-the-art quantum-vulnerable protocols.

版本更新 · v2arXiv:2610.32215

Democratizing LLM Training with the Edge: Challenges, Systems, and the Road Ahead

Ziyue Luo, Jiaxuan Cai, Cedric LeDenmat, Srijith Nair, Fatemeh Nourzad, Rohith Krishnan, Qinhang Wu, Jifan Zhang 等 30 位作者

原文摘要

The growing computational power of modern GPU data centers has let a few industrial giants train and deploy large language models (LLMs) on massive amounts of data. Yet the sheer scale of these models and their training data makes centralized development increasingly hard to sustain, and at the same time shuts out smaller institutions that hold relevant private data but lack comparable infrastructure. In this paper, we organize our positions on distributed LLM training around the pretraining and fine-tuning paradigms, covering federated learning, split learning, and decentralized approaches, and we assess their readiness for geo-distributed settings with heterogeneous hardware and unreliable connectivity. We then present a {\em unified} system architecture built around a cloud-hosted parameter server that coordinates heterogeneous clients over outbound gRPC connections, reusing one communication, compression, and fault-tolerance stack for both pretraining and a family of fine-tuning methods. We validate the design with real geo-distributed experiments on GPT-2 Medium and Llama3-1B pretraining across multiple university sites, showing stable convergence under realistic conditions. Finally, we chart the road ahead, identifying open problems that span frontier model support, elastic fault tolerance, adaptive compression, federated alignment, privacy and trust, incentive design, and system--algorithm co-design, all of which must be addressed to make democratized LLM training a reality.

cs.DC · cs.LG

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-short.7ACL Anthology · ACL 2026

From Factuality to Meta-Factivity: A Cognitive Blueprint for Trustworthy LLMs

Daohuan Liu Huazhong University of Science and Technology, Lun Xia, Yuer Wang Huazhong University of Science and Technology, Jiaoyang Su Huazhong University of Science and Technology, Xuri Tang Huazhong University of Science and Technology

原文摘要

Current research on Event Factuality Prediction (EFP) predominantly treats LLMs as passive classifiers, where high aggregate metrics often mask shortcut learning and unreliable reasoning. In this position paper, we argue for a focus shift from event factuality to meta-factivity. We introduce the Meta-Factivity Framework (MFF), a theoretical roadmap that moves evaluation beyond surface recognition to belief trajectory reasoning and epistemic regulation. By framing hallucination as a failure of meta-cognitive control, we advocate for a transition from measuring black-box accuracy to evaluating white-box cognition, laying the groundwork for a more rigorous benchmark for explainable self-governance.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表