Cognitive Framework for Human-AI Collaboration

Deconstructing the Dual Black Box: A Plug-and-Play Cognitive Framework for Human-AI Collaborative Enhancement and Its Implications for AI Governance

This paper proposes a new paradigm of 'human-AI collaborative cognitive enhancement,' aiming to transform the dual black boxes into a composable, auditable, and extensible 'functional white-box' system through structured 'meta-interaction.'

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Executive Impact at a Glance

1 First Engineering Proof for Cognitive Equity

100% Framework Open-Sourced

2 Paradigm Shift (AI as Tool to Partner)

3+ Cross-Domain Framework Reusability (min)

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Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

Core Methodology: Meta-Interaction

Meta-interaction (元交互) is defined as systematic collaboration with AI on a cognitive level, transcending the 'user-tool' paradigm. AI transforms from a passive receiver to an equal thinking partner, building cognitive frameworks and inspiring deeper thought.

Comparison with Mainstream Cognitive Enhancement Technologies

Technology Type	Core Objective	Cognitive Interaction Mode	Core Limitations
Prompt Engineering	Optimize input prompts to improve the model's accuracy for specific tasks	One-way input (Human→AI) without cognitive feedback	Does not involve cognitive transformation; poor generalization
Chain-of-Thought	Guide the model to generate linear reasoning steps and enhance logical capabilities	One-way reasoning (AI→Human), non-interactive and uncorrectable	Linear structure; inability to extract human implicit cognition
AI Agent	Independently complete complex task closed loops (Perception-Decision-Action)	Task-oriented; human-AI interaction limited to instructions and outcomes	Black-box decision-making; lack of cognitive adaptability
Meta-interaction (This Study)	Human hot cognition → structured cold cognition; AI cold cognition → contextualization	Recursive adversarial meta-interaction (Human-AI cognitive symbiosis)	Currently relies on text-based interaction modality

RAMTN Architecture

The Recursive Adversarial Meta-Thinking Network (RAMTN) engineeringly implements the meta-interaction paradigm. It's designed to structure human 'hot cognition' and contextualize AI 'cold cognition' through a three-layer recursive adversarial structure: Constructor, Critic, and Observer.

RAMTN Three-Layer Constructor-Critic-Observer Cyclic Architecture

User Input

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Layer 1 Constructor

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Layer 1 Critic

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Layer 1 Observer

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Layer 2 Constructor

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Layer 2 Critic

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Layer 2 Observer

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Layer 3 Constructor

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Layer 3 Critic

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Layer 3 Observer

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System Output

Comprehensive Workflow: Framework Extraction & Enhancement

Expert Dialogue Input

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RAMTN Extraction Mode Activation

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Recursive Adversarial Module (Extraction)

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Generate Cognitive Framework Package

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Framework Package Storage

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New Problem Input

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RAMTN Enhancement Mode Activation

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Recursive Adversarial Module (Framework-Guided)

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Output Expert-Style Results

Case Studies & Validation

This section showcases RAMTN's core capabilities: extracting expert implicit intuition and providing actionable cognitive enhancement solutions for novices across high-stakes domains like investment, medical diagnosis, and education.

Buffett's Investment Framework: Strategy Extraction & Application

Scenario: Investment

Core Extracted Framework: Strict price discipline; Pricing power as a margin of safety; Preference for minimal reinvestment cash flow; Retention of original team for risk control.

Implanted Adaptation Strategies: Prioritize narrow-domain AI medical stocks (with clear FDA approval pathways); Conservative position allocation; Verification of customer stickiness and pricing power evidence.

Confidence (Extraction/Adaptation): 0.88 / 0.78

Medical Diagnostic Framework: Complex Case Analysis for Grassroots

Scenario: Medical Care

Core Extracted Framework: Treatment failure triggers re-evaluation; Hierarchical elimination + focus on high-risk factors; Multimodal evidence closed loop; Optimization of invasive examination timing.

Implanted Adaptation Strategies: Prioritize exclusion of critical non-infectious etiologies; Repeated CT scan + tuberculin test; Structured preparation for teleconsultation.

Confidence (Extraction/Adaptation): 0.82 / 0.82

Teaching Decision Framework: Adaptive Pedagogy for Rural Settings

Scenario: Education

Core Extracted Framework: Integrated pre-class and initial-class academic situation diagnosis; Real-time feedback and dynamic regulation; Hierarchical strategy matching with learning obstacles.

Implanted Adaptation Strategies: 'Three-question sketch' to simplify academic situation diagnosis; Focus on the core process of 'formulating equations → substituting into formulas'; Blackboard-visualized error correction.

Confidence (Extraction/Adaptation): 0.92 / 0.83

Case Study Summary: Frameworks & Adaptation Strategies

Scenario	Core Extracted Framework (Expert Decision Logic)	Implanted Adaptation Strategies (Contextual Adjustments)	Confidence Level
Investment	1. Strict price discipline; 2. Pricing power as a margin of safety; 3. Preference for minimal reinvestment cash flow; 4. Retention of original team for risk control	1. Prioritize narrow-domain AI medical stocks (with clear FDA approval pathways); 2. Conservative position allocation; 3. Verification of customer stickiness and pricing power evidence	0.78
Medical Care	1. Treatment failure triggers re-evaluation; 2. Hierarchical elimination + focus on high-risk factors; 3. Multimodal evidence closed loop; 4. Optimization of invasive examination timing	1. Prioritize exclusion of critical non-infectious etiologies; 2. Repeated CT scan + tuberculin test; 3. Structured preparation for teleconsultation	0.82
Education	1. Integrated pre-class and initial-class academic situation diagnosis; 2. Real-time feedback and dynamic regulation; 3. Hierarchical strategy matching with learning obstacles	1. 'Three-question sketch' to simplify academic situation diagnosis; 2. Focus on the core process of 'formulating equations → substituting into formulas'; 3. Blackboard-visualized error correction	0.83

Capturing Counter-Intuitive Insights

Deep Logic Unlocking hidden expert heuristics beyond surface-level observations.

Dynamic Scenario Adaptation

Optimal Fit Adapting frameworks to diverse constraints, avoiding rigid application.

Identifying Cognitive Boundaries

White-Box Clarity Grading confidence levels for transparent, auditable decision-making.

AI Governance Implications

The RAMTN framework provides a novel approach to AI governance, emphasizing transparency through interaction protocols rather than internal model mechanisms, aligning with 'AI for Social Good' initiatives.

AI Transparency Regulation

Functional White-Box Auditable AI via transparent interaction protocols, not internal model weights.

Digital Divide & Cognitive Equity

Cognitive Common Transforming expert implicit knowledge into shareable public assets.

RAMTN vs. DeepSeekMath-V2 (Governance Perspective)

Dimension	DeepSeekMath-V2	RAMTN (This Study)
Core Objective	Solve rigorous reasoning problems in closed mathematical domains	Construct a general cognitive architecture in open scenarios to realize the extraction and large-scale enhancement of human experts' implicit cognition
Scenario Constraints	Clear problem boundaries and strictly verifiable answers	Ambiguous and open problems, need to adapt to resource constraints, and rely on expert intuition
Output Value	Highly rigorous mathematical reasoning processes and results	Plug-and-play human cognitive frameworks that support cross-scenario transfer and novice cognitive enhancem

Calculate Your Cognitive ROI

Understand the potential time savings and strategic value RAMTN can unlock for your enterprise.

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Implementation Roadmap

A clear path to integrating RAMTN and unlocking your organization's full cognitive potential.

Phase 1: Discovery & Framework Extraction

Initial consultations to understand organizational cognitive challenges. Virtual dialogues with key experts to extract implicit decision logics and codify them into plug-and-play cognitive frameworks. Establish initial confidence baselines.

Phase 2: Pilot Deployment & Adaptive Enhancement

Deploy RAMTN with extracted frameworks in a targeted pilot group. Implement meta-interaction protocols, continuously refine frameworks based on user feedback and real-world scenarios. Optimize for contextual adaptability and output quality.

Phase 3: Scaled Integration & Cognitive Inclusion

Expand RAMTN deployment across relevant departments/teams. Integrate with existing knowledge management systems. Train more users to leverage cognitive enhancement, fostering a culture of continuous learning and collective intelligence.

Phase 4: Governance & Sustainable Evolution

Establish AI governance policies focused on transparency of interaction protocols. Create a framework for continuous expert knowledge contribution and updates. Monitor long-term impact on decision quality and cognitive equity, ensuring sustainable value creation.

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