Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents 文章

ArXiv CS.AI2026-05-29NEWSen作者: Xiang Liu, Sa Song, Zhaowei Zhang, Huiying Lan, Jason Zeng, Ming Wu, Michael Heinrich, Yong Sun, Ceyao Zhang

摘要

arXiv:2605.29910v1 Announce Type: cross Abstract: Consensus protocols form the backbone of distributed systems and blockchains, where implementation bugs can cause data corruption and financial losses. While LLM-based approaches show promise in code analysis, they struggle with deep protocol-level logic bugs involving complex state-dependent behaviors across multiple execution stages. We present Agora, a domain-aware multi-agent framework that integrates hypothesis-driven testing with LLM capabilities for systematic protocol verification. Agora employs specialized agents that collaboratively explore protocol state spaces, synthesize attack scenarios using domain-specific constraints, and validate findings through iterative refinement. This explicit role separation enables reasoning about global protocol invariants beyond single-function code analysis. We evaluate Agora on four consensus implementations (Raft, EPaxos, HotStuff, BullShark) using four state-of-the-art LLMs.