Consensus and Byzantine Fault Tolerance for Agents
Classical distributed-systems BFT meets stochastic LLMs. In 2025-2026 three research directions emerged: **CP-WBFT** (arXiv:2511.10400) weighs each vote by a confidence probe; **DecentLLMs** (arXiv:2507.14928) goes leaderless with parallel worker proposals and geometric-median aggregation; **WBFT** (arXiv:2505.05103) combines weighted voting with Hierarchical Structure Clustering to split Core and Edge nodes. The honest empirical result from "Can AI Agents Agree?" (arXiv:2603.01213) is that even scalar agreement is fragile today — a single deceptive agent can compromise a Mixture-of-Agents. BFT is necessary but not sufficient. This lesson builds a minimal BFT protocol, injects three agent-specific attacks (byzantine lie, sycophantic conformity, correlated-error monoculture), and measures how each consensus variant copes.
This lesson isn’t interactive yet.
It is part of the curriculum and will get the same treatment as Phase 1 — a visual cover, hands-on labs, derivations with numeric checks and a quiz. Until then, the original lesson is the best place to read it:
Part of Phase 16 — Multi-Agent & Swarms. Use the previous / next cards below to keep browsing the phase.