Hevolve AI: Self-Evolving Multimodal AI Agents

Turn your domain expertise into AI agents that keep learning. Hevolve AI lets experts build multimodal AI systems by talking to them and correcting them in real time, with no code to write.

Key Features

Quick Links

© 2024 Hevolve AI Pvt Ltd. All rights reserved.

← All research
Artificial Intelligence
arXiv (cs.LG) · July 17, 2026

The Honest Quorum Problem: Epistemic Byzantine Fault Tolerance for Agentic Infrastructure

Jun He, Deying Yu

Classic consensus protocols are built to survive participants who lie, go silent, or actively collude. The guarantee has a hidden assumption though: everyone outside that faulty set is expected to execute the rules correctly. For ordinary software that is a fair assumption, because code either follows the transition rules or it does not.

Agents that reason are a different proposition. One can be properly authenticated, answer promptly, never contradict itself, and follow the protocol exactly as written, and still endorse a transition that is semantically wrong, because its reasoning was wrong. The paper names this an epistemic fault, and notes the uncomfortable word in the standard framing: honest has always meant protocol-compliant, not correct.

The collective version is the Honest Quorum Problem. A quorum can satisfy every rule the protocol checks and still agree on something invalid, because the protocol was never checking meaning in the first place. Every participant passes the test, and the group is still wrong.

From the arXiv (cs.LG) abstract

State machine replication (SMR) and Byzantine fault-tolerant (BFT) consensus guarantee agreement despite a bounded number of arbitrary, colluding faulty participants. However, these guarantees rely on participants outside this set correctly executing the protocol's transition semantics. Agentic validators expose a weaker boundary: an authenticated, responsive, non-equivocating, and protocol-compliant reasoning participant may still endorse a semantically invalid transition…


More Artificial Intelligence papers