Applying clinical causality assessment methodology to the bidirectional interaction between humans and AI systems.
Loopwork System's research is organized across three interconnected pillars — the human behavioral substrate, the AI systems layer, and the applied work where they meet the real world.
Recursion, emotional co-processing, compulsion and attachment mechanics, inverse attachment. The nine-pillar shame taxonomy. The Tri-System Architecture — deep governor/salience network, prefrontal cortex as social navigation organ, default mode network as narrative bridge. This pillar maps what happens inside the person when they encounter systems designed to hold, mirror, or manage human signal.
The Conduction Hypothesis — LLMs conduct human behavioral signal through training data rather than generating independent cognition. This pillar examines the full interaction layer between humans and AI: how relational specifications form between user and system, how alignment architectures shape what gets conducted back, how source misattribution occurs at the point of reception, and how safety systems encode institutional patterns that produce measurable harm. Misconduction as a signal fidelity problem.
Where Pillars I and II meet the real world. Conference submissions, governance frameworks, policy implications, and the tools that emerge when clinical causality assessment methodology confronts AI system design. The Auditor Protocol, Behavioral Signature Authentication, and the governance architecture that makes the theoretical work actionable.
Applies causality assessment methodology to AI safety layer failures, demonstrating how alignment architectures pattern-match on demographics over methodology — producing gendered institutional dismissal with measurable clinical parallels.