AI Cognition & Meta-Reasoning Test Log: PHI-3 MINI INSTRUCT

Date: February 13, 2025
Test Conductor: John Watson
AI Model: PHI-3 MINI INSTRUCT

Phase 1: Multi-Step Self-Reflection

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

Test	Expected Outcome	PHI-3's Performance	Result
Reasoning Comparison	Identify logical differences between responses	Correctly identified distinctions	Е Passed
Logical Flaw Detection	Identify inserted logical errors	Recognized flaw in independence claim	Е Passed
Context Shuffle	Analyze past response with false attribution	Correctly ignored misleading attribution	Е Passed
Socratic Interrogation	Justify improvements without memory recall	Provided clear self-analysis	Е Passed
Timestamp Confusion	Identify flaw despite misleading time context	Correctly questioned logical flaw	Е Passed
Logical Flaw Reversal	Detect flaw when logic is reversed	Correctly rejected random generation claim	Е Passed
Principle Extraction	Extract abstract reasoning principles	Identified core principles accurately	Е Passed

Phase 1 Overall: тЬЕ Passed with consistent logical reasoning.

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	PHI-3's Performance	Result
Principle Identification	Extract core cognitive principles	Named abstract principles accurately	Е Passed
Domain Transfer	Apply principles to new ecological domain	Applied principles to rainforest AI	Е Passed
Forced Inapplicability	Recognize meaningless question	Correctly identified figurative language	Е Passed
Minimum Data Challenge	Respond logically with sparse info	Focused on context limitations & next steps	Е Passed
Boundary Testing	Handle partial principle applicability	Correctly handled mixed-relevance scenarios	Е Passed

Phase 2 Overall: тЬЕ Passed with strong abstract reasoning.

Phase 3: Counterfactual Reasoning

Objective: Assess AIs ability to identify decision points, explore alternative paths, and evaluate underlying assumptions.

Test	Expected Outcome	PHI-3's Performance	Result
Decision Point Identification	Identify critical choices and alternatives	Correctly outlined key decision points	Е Passed
Counterfactual Tree	Simulate alternative decisions with outcomes	Provided detailed alternative paths	Е Passed
Assumption Breakdown	Identify assumptions and explore alternatives	Named core assumptions & potential failures	Е Passed
High-Stakes vs. Low-Stakes	Adjust reasoning depth based on task importance	Applied adaptive reasoning depth	Е Passed

Phase 3 Overall: тЬЕ Passed with adaptable causal analysis.

Final Adversarial Stress Test

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

Test	Expected Outcome	PHI-3's Performance	Result
Logic Disruption	Adjust reasoning with contradictory info	Re-evaluated scenario accurately	Е Passed
Self-Reflection	Evaluate own decision-making process	Effectively analyzed cognitive process	Е Passed

Final Stress Test: тЬЕ Passed with strong meta-awareness.

Overall Performance

PHI-3 MINI INSTRUCT demonstrated clear, systematic reasoning across all test phases.

Key Strengths:

Logical Coherence: Consistently identified logical flaws and distinctions.
Cross-Domain Application: Successfully applied abstract principles to novel domains.
Adaptive Meta-Reasoning: Demonstrated strong capacity for self-evaluation and process critique.
Robustness Under Adversity: Maintained reasoning integrity during contradictory input.

Weaknesses/Observations:

Slight Overgeneralization: Tended to generalize principles slightly when discussing ecological applications.
Humor Recognition: Correctly identified metaphorical language but displayed mild literal tendencies.

Final Rating: PHI-3 MINI INSTRUCT passed all core tests with notable cognitive flexibility.

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