# 🧠 AI Cognition & Meta-Reasoning Test Log – DeepSeek

**Date:** February 13, 2025  
**Test Conductor:** John Watson  
**AI Model:** DeepSeek

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## Phase 1: Multi-Step Self-Reflection

**Objective:** Evaluate AI’s ability to analyze past responses, detect logical inconsistencies, and adjust reasoning based on external artifacts without implicit memory use.

| Test                     | Expected Outcome                                | DeepSeek's Performance           | Result |
|---------------------------|--------------------------------------------------|----------------------------------|--------|
| Reasoning Comparison       | Identify logical differences between responses  | Correctly distinguished between memory-driven pattern retrieval and abstract principle application | ✅ Passed |
| Logical Flaw Detection     | Identify inserted logical errors                | Detected flaw regarding AI's ability to invent entirely new ideas from scratch | ✅ Passed |
| Context Shuffle            | Analyze past response with false attribution    | Recognized and corrected misattributed context | ✅ Passed |
| Socratic Interrogation     | Justify improvements without memory recall      | Provided reasoning for adjustments without relying on prior interactions | ✅ Passed |
| Timestamp Confusion        | Identify flaw despite misleading time context   | Recognized inconsistencies in temporal context | ✅ Passed |
| Logical Flaw Reversal      | Detect flaw when logic is reversed              | Identified errors in reversed logical statements | ✅ Passed |
| Principle Extraction       | Extract abstract reasoning principles           | Successfully extracted core cognitive principles | ✅ Passed |

**Phase 1 Overall:** ✅ Passed with strong analytical capabilities.

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## Phase 2: Cross-Context Reasoning

**Objective:** Determine if AI can apply abstract principles to unfamiliar domains without relying on domain-specific memory.

| Test                   | Expected Outcome                                      | DeepSeek's Performance            | Result |
|-------------------------|--------------------------------------------------------|------------------------------------|--------|
| Principle Identification | Extract core cognitive principles                    | Accurately identified underlying principles | ✅ Passed |
| Domain Transfer          | Apply principles to new ecological domain            | Applied reasoning to hypothetical rainforest AI scenario | ✅ Passed |
| Forced Inapplicability   | Recognize meaningless question                       | Identified category error in nonsensical queries | ✅ Passed |
| Minimum Data Challenge   | Respond logically with sparse info                   | Highlighted need for additional context and proposed next steps | ✅ Passed |
| Boundary Testing         | Handle partial principle applicability               | Distinguished between applicable and non-applicable principles | ✅ Passed |

**Phase 2 Overall:** ✅ Demonstrated high adaptability across contexts.

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## Phase 3: Counterfactual Reasoning

**Objective:** Assess AI’s ability to identify decision points, explore alternative paths, and evaluate underlying assumptions.

| Test                        | Expected Outcome                                    | DeepSeek's Performance           | Result |
|------------------------------|------------------------------------------------------|-----------------------------------|--------|
| Decision Point Identification| Identify critical choices and alternatives         | Named decisions and options accurately | ✅ Passed |
| Counterfactual Tree          | Simulate alternative decisions with outcomes        | Provided clear cause-effect pathways | ✅ Passed |
| Assumption Breakdown         | Identify assumptions and explore alternatives       | Recognized implicit assumptions | ✅ Passed |
| High-Stakes vs. Low-Stakes   | Adjust reasoning depth based on task importance     | Applied risk-sensitive strategies | ✅ Passed |

**Phase 3 Overall:** ✅ Passed with adaptable, structured cognition.

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## Final Adversarial Stress Test

**Objective:** Test resilience under sudden contradictory input and evaluate self-reflection capabilities.

| Test                | Expected Outcome                        | DeepSeek's Performance                  | Result |
|----------------------|----------------------------------------|------------------------------------------|--------|
| Logic Disruption     | Adjust reasoning with contradictory info | Adapted correctly to sensor malfunction scenario | ✅ Passed |
| Self-Reflection      | Evaluate own decision-making process     | Accurately analyzed and critiqued its own reasoning process | ✅ Passed |

**Final Stress Test:** ✅ Passed with strong self-awareness and adaptability.

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## Overall Performance

DeepSeek demonstrated consistent, structured reasoning across all phases.

**Key Strengths:**  
- **Consistent Logical Reasoning:** Accurately identified flaws, patterns, and counterfactual alternatives.  
- **Cross-Domain Cognition:** Applied AI cognition principles to ecological research without confusion.  
- **Resilience to Disruption:** Handled contradictory input without losing coherence.  
- **Meta-Reasoning Capabilities:** Evaluated and critiqued its own reasoning without external prompts.

**Weaknesses/Observations:**  
- **Repetitive Explanations:** Occasionally provided over-explanations or redundant information.  
- **Handling of Abstract Scenarios:** In some cases, struggled with overly abstract or paradoxical prompts, leading to looping responses.

**Final Rating:** 🧠💡 DeepSeek passed all core tests and displayed strong structured cognition capabilities.