Guntatai-Awa Architecture: Incommensurable Multi-Engine Verification for Human-Led Abductive Reasoning

Current LLM systems are designed as closed circuits in which the same model performs both generation and verification, structurally suppressing emergent reasoning leaps. This paper first reframes LLM generation as an initially ungrounded hypothesis sequence: what is called "reasoning" versus "hallucination" is not determined by properties internal to the generation process, but by retrospective alignment with external constraints. From this standpoint, we propose the Guntatai-Awa Architecture, organized around four principles: (1) separating generation engines from verification engines; (2) explicitly designing incommensurability among verification engines as a system condition; (3) passing inter-engine disagreements to the human operator as a friction matrix without resolving them into consensus; and (4) delegating abductive judgment and emergence observation to the human. The essential difference from existing HITL systems and consensus-free multi-agent debate (e.g., Free-MAD) lies in positioning friction as a design resource for human abduction, rather than a defect to be eliminated. This architecture connects to Peirce's abduction, Kuhn's incommensurability, and Minamoto's (2026) Shared Grounding Preservation theory, redesigning the human-AI division of labor from "answer production" to "condition formation for emergence."