Dimension-Direct Routing: Achieving 25% Depth Improvement in Multi- Model LLM Systems via Explicit Capability Factorization
Tao Rui · Zenodo (CERN European Organization for Nuclear Research) · 2026
Large Language Models (LLMs) exhibit distinct capabilities across different knowledge domains, yet single-model deployments struggle with knowledge-intensive tasks requiring cross-domain reasoning. We present eVoiceClaw Desktop, a multi-model orchestration system that operationalizes an \"AI managing AI\" paradigm: instead of humans manually selecting models, the system dynamically routes complex queries to specialized models through a dimension-direct routing algorithm. The system underwent four major configuration iterations (V1–V4), culminating in V5 that addresses critical challenges in cross-domain task allocation and semantic accumulation bias. V5 achieves a 98% workflow trigger rate across 50 benchmark questions in Chinese, leveraging 12 models with balanced diversity (top model ≤16% usage share). We evaluate response quality using LLM-as-Judge (Claude Opus 4.6) across four dimensions: factual accuracy, completeness, depth, and structure. Compared to single-model baselines, V5 achieves a 14.3% overall quality improvement, with depth of analysis improving by 25.9%, at the expense of approximately 9× higher latency and cost. As a meta-demonstration, the initial draft of this paper was itself generated by the system (see Appendix B).