Mahi-Mahi: Low-Latency Asynchronous BFT DAG-Based Consensus

• URL: http://arxiv.org/abs/2410.08670v2
• Date: Thu, 17 Oct 2024 14:06:29 GMT
• Title: Mahi-Mahi: Low-Latency Asynchronous BFT DAG-Based Consensus
• Authors: Philipp Jovanovic, Lefteris Kokoris Kogias, Bryan Kumara, Alberto Sonnino, Pasindu Tennage, Igor Zablotchi,
• Abstract summary: Mahi-Mahi is the first asynchronous BFT consensus protocol that achieves sub-second latency in the WAN. We build Mahi-Mahi on an uncertified structured Directed Acyclic Graph (DAG) We demonstrate the safety and liveness of Mahi-Mahi in a Byzantine context.
• Score: 3.4234734330005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Mahi-Mahi, the first asynchronous BFT consensus protocol that achieves sub-second latency in the WAN while processing over 100,000 transactions per second. We accomplish this remarkable performance by building Mahi-Mahi on an uncertified structured Directed Acyclic Graph (DAG). By forgoing explicit certification, we significantly reduce the number of messages required to commit and minimize CPU overhead associated with certificate verification. Mahi-Mahi introduces a novel commit rule that allows committing multiple blocks in each DAG round, while ensuring liveness in the presence of an asynchronous adversary. Mahi-Mahi can be parametrized to either attempt to commit within 5 message delays, maximizing the probability of commitment under a continuously active asynchronous adversary, or within 4 message delays, which reduces latency under a more moderate and realistic asynchronous adversary. We demonstrate the safety and liveness of Mahi-Mahi in a Byzantine context. Subsequently, we evaluate Mahi-Mahi in a geo-replicated setting and compare its performance against state-of-the-art asynchronous consensus protocols, showcasing Mahi-Mahi's significantly lower latency.

Related papers

• Rejection Mixing: Fast Semantic Propagation of Mask Tokens for Efficient DLLM Inference [58.189320101488725]
  DLLMs promise fast non-autoregressive inference but suffer a severe quality-speed trade-off in parallel decoding.<n>We address this by integrating continuous representations into the discrete decoding process, as they preserve rich inter-position dependency.<n>We propose ReMix, a framework that introduces a novel Continuous Mixing State as an intermediate between the initial masked state and the final decoded token state.
  arXiv  Detail & Related papers  (2026-02-26T11:08:11Z)
• Zero-Trust Runtime Verification for Agentic Payment Protocols: Mitigating Replay and Context-Binding Failures in AP2 [0.0]
  We present a security analysis of the AP2 mandate lifecycle and identify enforcement gaps that arise during runtime in agent-based payment systems.<n>We propose a zero-trust runtime verification framework that enforces explicit context binding and consume-once mandate semantics.<n>We show that context-aware binding and consume-once enforcement address distinct and complementary attack classes, and that both are required to prevent replay and context-redirect attacks.
  arXiv  Detail & Related papers  (2026-02-06T03:22:11Z)
• Overcoming Joint Intractability with Lossless Hierarchical Speculative Decoding [58.92526489742584]
  We propose provably lossless.<n> verification method that significantly boosts the expected number of accepted tokens.<n>We show that HSD yields consistent improvements in acceptance rates across diverse model families and benchmarks.
  arXiv  Detail & Related papers  (2026-01-09T11:10:29Z)
• Haste Makes Waste: Evaluating Planning Abilities of LLMs for Efficient and Feasible Multitasking with Time Constraints Between Actions [56.88110850242265]
  We present Recipe2Plan, a novel benchmark framework based on real-world cooking scenarios. Unlike conventional benchmarks, Recipe2Plan challenges agents to optimize cooking time through parallel task execution.
  arXiv  Detail & Related papers  (2025-03-04T03:27:02Z)
• CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness [21.627120541083553]
  This paper proposes a novel framework, Communication Delay-tolerant Multi-Agent Collaboration (CoDe) At first, CoDe learns an intent representation as messages through future action inference. Then, CoDe devises a dual alignment mechanism of intent and timeliness to strengthen the fusion process of asynchronous messages.
  arXiv  Detail & Related papers  (2025-01-09T12:57:41Z)
• A Committee Based Optimal Asynchronous Byzantine Agreement Protocol W.P. 1 [0.5120567378386615]
  Multi-valued Byzantine agreement protocols are essential for atomic broadcast and fault-tolerant state machine replication in asynchronous networks. This paper presents a committee-based MVBA protocol (cMVBA) that achieves agreement without extra communication rounds by analyzing message patterns in asynchronous networks with probability 1.
  arXiv  Detail & Related papers  (2024-10-30T21:55:08Z)
• Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo [55.452453947359736]
  We introduce a novel verification method based on Twisted Sequential Monte Carlo (TSMC) We apply TSMC to Large Language Models by estimating the expected future rewards at partial solutions. This approach results in a more straightforward training target that eliminates the need for step-wise human annotations.
  arXiv  Detail & Related papers  (2024-10-02T18:17:54Z)
• AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising [49.785626309848276]
  AsyncDiff is a universal and plug-and-play acceleration scheme that enables model parallelism across multiple devices. For the Stable Diffusion v2.1, AsyncDiff achieves a 2.7x speedup with negligible degradation and a 4.0x speedup with only a slight reduction of 0.38 in CLIP Score. Our experiments also demonstrate that AsyncDiff can be readily applied to video diffusion models with encouraging performances.
  arXiv  Detail & Related papers  (2024-06-11T03:09:37Z)
• Concurrent Asynchronous Byzantine Agreement in Expected-Constant Rounds, Revisited [3.8014967401609208]
  We present the first information-theoretic multi-valued OCC protocol in the asynchronous setting with optimal resiliency. Our protocol efficiently implements with an exponential-size domain. We also provide proof in Canetti's Universal Composability framework.
  arXiv  Detail & Related papers  (2023-12-22T08:10:11Z)
• Banyan: Fast Rotating Leader BFT [20.52947785138998]
  Banyan is the first rotating leader state machine replication protocol that allows transactions to be confirmed in just a single round-trip time. We introduce a novel dual mode mechanism that enables optimal block finalization latency in the fast path. Our evaluation reveals that Banyan reduces latency by up to 30% compared to state-of-the-art protocols.
  arXiv  Detail & Related papers  (2023-12-10T12:32:58Z)
• Mysticeti: Reaching the Limits of Latency with Uncertified DAGs [5.328717371685882]
  We introduce Mysticeti-C, the first DAG-based Byzantine consensus protocol to achieve the lower bounds of latency of 3 message rounds. We extend Mysticeti-C to Mysticeti-FPC, which incorporates a fast commit path that achieves even lower latency for transferring assets.
  arXiv  Detail & Related papers  (2023-10-23T11:40:50Z)
• Short Paper: Accountable Safety Implies Finality [10.589723476970443]
  Two key desiderata have been studied for Byzantine-fault tolerant (BFT) state-machine replication (SMR) consensus protocols. We show that accountable safety implies finality, thereby unifying earlier results.
  arXiv  Detail & Related papers  (2023-08-31T17:58:38Z)
• Mimicking Better by Matching the Approximate Action Distribution [48.95048003354255]
  We introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. We show that it requires considerable fewer interactions to achieve expert performance, outperforming current state-of-the-art on-policy methods.
  arXiv  Detail & Related papers  (2023-06-16T12:43:47Z)
• Faster Video Moment Retrieval with Point-Level Supervision [70.51822333023145]
  Video Moment Retrieval (VMR) aims at retrieving the most relevant events from an untrimmed video with natural language queries. Existing VMR methods suffer from two defects: massive expensive temporal annotations and complicated cross-modal interaction modules. We propose a novel method termed Cheaper and Faster Moment Retrieval (CFMR)
  arXiv  Detail & Related papers  (2023-05-23T12:53:50Z)
• LSTC: Boosting Atomic Action Detection with Long-Short-Term Context [60.60267767456306]
  We decompose the action recognition pipeline into short-term and long-term reliance. Within our design, a local aggregation branch is utilized to gather dense and informative short-term cues. Both branches independently predict the context-specific actions and the results are merged in the end.
  arXiv  Detail & Related papers  (2021-10-19T10:09:09Z)
• QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning [70.382101956278]
  QTRAN is a reinforcement learning algorithm capable of learning the largest class of joint-action value functions. Despite its strong theoretical guarantee, it has shown poor empirical performance in complex environments. We propose a substantially improved version, coined QTRAN++.
  arXiv  Detail & Related papers  (2020-06-22T05:08:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.