Deep Collaboration Methodology (DCM): Architecture Design and String Encoding System: A Logical Diagnostic Tool for Micro-Field Surveys

Abstract(V1.6) This technical report details the empirical refinement of field inquiry protocols based on pilot trials conducted in Taipei academic zones during December 2025. The study identifies "Social Defense Mechanisms" as a primary barrier to authentic logic capturing in human-AI ethics research. To mitigate this, two key methodological optimizations are introduced: **Persona Calibration:** The transition from formal addressing to a "Novice Learner" (Uncle) persona, significantly reducing participant resistance. **Memorized Inquiry Protocol (MHT):** The internalization of the inquiry triad to facilitate unmediated interpersonal interaction and continuous behavioral observation. These refinements enhance the qualitative depth of data collected within a structured string-based encoding framework, ensuring research integrity and participant trust in a non-IRB environment. Abstract(V1.0) This report details the theoretical framework and data encoding system of the Deep Collaboration Methodology (DCM). DCM aims to capture the black-box cognition and ethical boundaries of humans collaborating with artificial intelligence through micro-field investigations. This study designed a funnel-shaped questioning strategy to guide respondents from instrumental use to deeper ethical debate. The core contribution lies in the development of a string-based recording system that uses "cognitive dissonance flags (!)" and "responsibility shift flags (?)" to instantly detect logical breaks in respondents. This framework provides independent researchers with an empirical research paradigm possessing forensic-level integrity in non-institutional settings.