Consensus

Introduction Distributed ledgers—whether public blockchains, permissioned networks, or hybrid hybrids—rely on state machine replication (SMR) to provide a consistent view of the ledger across a set of potentially unreliable nodes. At the heart of SMR lies a consensus protocol that decides the order of transactions, guarantees safety (no two honest nodes diverge) and liveness (the system eventually makes progress), and does so under real‑world constraints such as network latency, message loss, and Byzantine behavior. ...

Introduction In the world of modern computing, data is rarely stored on a single machine. Cloud services, micro‑service architectures, and globally replicated databases all rely on distributed systems—clusters of nodes that cooperate to provide fault‑tolerant, highly available services. At the heart of this cooperation lies a fundamental problem: how can a set of unreliable machines agree on a single value despite network failures, crashes, and message reordering? This is known as the distributed consensus problem. ...

Introduction A single language model, no matter how capable, can hallucinate, make reasoning errors, and exhibit hidden biases. The traditional solution in software engineering has always been peer review—multiple experts independently evaluate the same work, critique each other’s conclusions, and converge on a better answer. LLM Councils apply this same principle to AI systems: multiple language models independently reason about the same task, critique each other’s outputs, and converge on a higher-quality final answer through structured aggregation. ...