QIREL: A Distributed Cognitive Operating System for Fault-Tolerant Multi-Expert Language Model Orchestration

We present QIREL(Query Intelligence Routed Expert Language System), a novel distributed cognitive architecture for language model inference that fundamentally departs from the paradigm of scaling a single large model. QIREL orchestrates thirty-two domain-specialized Small Language Model(SLM) adapters, each fine-tuned via Low-Rank Adaptation(LoRA) on domain-specific corpora, within a seven-layer inference pipeline designed around verification, structured reasoning transport, and adaptive correction. The system introduces seven novel mechanisms:(1) Structured Claim Transport System, wherein expert outputs are represented as verifiable claim objects rather than raw text;(2) Claim Locking, which renders verified facts immutable across downstream components;(3) Claim Ancestry Tracking, an append-only transformation history enabling full reasoning auditability;(4) Expert Reputation Engine, a persistent verification-driven scoring mechanism that dynamically biases routing without retraining;(5) Semantic Contradiction Graph, a severity-ordered resolution framework for inter-expert claim conflicts;(6) Confidence Delta Routing, which prevents stagnant correction cycles by switching experts when confidence improvement falls below a defined threshold; and (7) Multilingual Semantic Locking, which guards against meaning drift during translation by comparing semantic graphs before and after linguistic reconstruction. QIREL is built entirely on the LLaMA 3.2 3B Instruct architecture with four-bit quantization, making it deployable on consumer-grade hardware while achieving multi-domain reasoning coverage across thirty-two knowledge domains. Preliminary evaluation demonstrates that QIREL's structured verification pipeline produces confidence-scored, ancestry-traced outputs with verified claim locking, representing a substantive advance in explainable, fault-tolerant language model inference. We release the full architecture specification and implementation to support reproducibility and further research in distributed cognitive AI systems.