What problem does it solve? Coordinating multiple local AI inference engines for multi-step reasoning tasks is complex, and single-model outputs often suffer from hallucination chains and unverified conclusions. This Skill guides the deployment of a distributed reasoning router that fragments inference across nodes and enforces consensus before producing answers. ## Core Features & Use Cases - Zero-Drift Consensus: Requires peer verification across mesh nodes before writes, with configurable confidence thresholds and escalation rules. - Adaptive Reasoning Depth: Scales reasoning from 1 to 7 steps based on problem complexity, with streaming of intermediate steps and causal audit trails. - Multilingual Semantic Routing: Routes queries across 16 languages using a universal intermediate representation, with cross-language context processing. - Use Case: A team running VLLM and Ollama on separate GPU machines can configure a reasoning mesh where code review queries are verified by multiple nodes, with full audit trails showing which node produced each reasoning step. ## Quick Start Set up a Logos Router reasoning mesh with two local nodes and run a consensus-verified query explaining a technical concept.