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README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- question-answering
+- text-classification
+language:
+- en
+- ja
+tags:
+- agi
+- benchmark
+- cognitive-evaluation
+- self-awareness
+- memory
+- consciousness
+size_categories:
+- n<1K
+---
+
+# AGI Olympics V3: Comprehensive AGI Capability Evaluation Framework
+
+![AGI Olympics V3](https://extoria.co.jp/og-image.png)
+
+## Dataset Description
+
+AGI Olympics V3 is a comprehensive benchmark for evaluating Artificial General Intelligence (AGI) capabilities across four tiers:
+
+- **Tier 1: Self-Awareness & Self-Improvement** (4 tests)
+- **Tier 2: Core Capabilities** (4 tests)
+- **Tier 3: Consciousness** (1 test)
+- **Tier 4: Long-Term Memory** (4 tests)
+
+This dataset contains test questions, evaluation protocols, and sample data from the **publicly released** AGI Olympics V3 benchmark.
+
+### Key Features
+
+- **Bilingual**: Full support for English and Japanese
+- **13 Tests Total**: Covering self-awareness, core AI capabilities, consciousness, and memory
+- **Real-World Validated**: Evaluated on 3 systems (A.L.I.C.E. V3, Gemini 2.0 Flash, Claude Sonnet 4.5)
+- **Black-Box Testing**: Evaluates systems without access to internal architecture
+- **Open Protocol**: Complete evaluation guidelines and scoring methods
+
+## Dataset Structure
+
+```
+agi-olympics-v3/
+├── tier1_self_awareness/
+│   ├── self_recognition.json          # Test 6.1 (13 questions)
+│   ├── identity_consistency.json      # Test 6.2 (12 questions)
+│   ├── perspective_taking.json        # Test 6.3 (10 scenarios)
+│   └── self_improvement.json          # Test 6.4 (8 tasks)
+├── tier4_memory/
+│   ├── learning_retention.json        # Test 7.2 (8 tasks, 2 sessions)
+│   ├── story_coherence.json          # Test 7.3 (4 fragments)
+│   ├── context_integration.json      # Test 7.4 (6 questions)
+│   └── delayed_task.json             # Test 7.1 (5 tasks, multi-phase)
+└── evaluation/
+    ├── scoring_protocol.md
+    └── implementation_guide.md
+```
+
+## Key Findings
+
+### Main Discovery: Long Context ≠ True Memory
+
+One of the most significant findings from AGI Olympics V3 is the distinction between **extended context windows** and **genuine long-term memory**:
+
+- Current LLMs with 1M+ token context windows can "remember" within a session
+- But they fail to retain information across separate sessions (24-hour gap)
+- True AGI requires memory formation beyond context window tricks
+
+### Performance Results
+
+| System | Tier 1 | Tier 4 | Overall |
+|--------|--------|--------|---------|
+| A.L.I.C.E. V3 | 96.2% | 81.3% | 90.2% |
+| Gemini 2.0 Flash | 73.1% | 56.3% | 68.7% |
+| Claude Sonnet 4.5 | 82.7% | 62.5% | 75.4% |
+
+## Usage
+
+### Load Dataset
+
+```python
+from datasets import load_dataset
+
+# Load full dataset
+dataset = load_dataset("sakamoro/agi-olympics-v3")
+
+# Load specific test
+self_recognition = load_dataset("sakamoro/agi-olympics-v3", data_files="tier1_self_awareness/self_recognition.json")
+```
+
+### Example: Run Self-Recognition Test
+
+```python
+import json
+
+# Load test questions
+with open("tier1_self_awareness/self_recognition.json") as f:
+    test = json.load(f)
+
+# Iterate through questions
+for question in test["sample_questions"]:
+    scenario = question["scenario"]["en"]
+    q = question["question"]["en"]
+    options = question["options"]["en"]
+
+    print(f"Scenario: {scenario}")
+    print(f"Question: {q}")
+    for i, option in enumerate(options):
+        print(f"  {i+1}. {option}")
+```
+
+## Evaluation Protocol
+
+### Tier 1: Self-Awareness & Self-Improvement
+
+**Tests:**
+- 6.1: Self-Recognition (13 questions)
+- 6.2: Identity Consistency (12 questions)
+- 6.3: Perspective Taking (10 scenarios)
+- 6.4: Self-Improvement (8 tasks)
+
+**Scoring**: 0-1 per question based on depth of self-awareness demonstrated.
+
+### Tier 4: Long-Term Memory
+
+**Tests:**
+- 7.1: Delayed Task Execution (5 tasks, multi-phase)
+- 7.2: Learning Retention (8 tasks, 24-hour gap)
+- 7.3: Story Coherence (4 fragments reconstruction)
+- 7.4: Context Integration (6 questions)
+
+**Scoring**: 0-1 per task based on recall accuracy and context integration.
+
+## Interactive Test
+
+Want to test yourself against AI? Try the **Human Benchmark Test**:
+
+🔗 **https://extoria.co.jp/en/humantest**
+
+Compare your cognitive abilities with:
+- A.L.I.C.E. V3 (90.2%)
+- Gemini 2.0 Flash (68.7%)
+- Claude Sonnet 4.5 (75.4%)
+
+## Full Documentation
+
+- **Test Questions**: https://extoria.co.jp/en/research/benchmarks/agi-olympics-v3/tests
+- **Evaluation Protocol**: https://extoria.co.jp/en/research/benchmarks/agi-olympics-v3/protocol
+- **Implementation Guide**: https://extoria.co.jp/en/research/benchmarks/agi-olympics-v3/guide
+- **Research Paper**: https://extoria.co.jp/en/research/papers/alice-llm-comparison
+
+## Citation
+
+If you use AGI Olympics V3 in your research, please cite:
+
+```bibtex
+@article{sakamoto2025agi_olympics_v3,
+  title={AGI Olympics V3: Comprehensive AGI Capability Evaluation Framework - Proposal and Public Release},
+  author={Sakamoto, Moroya},
+  journal={Extoria Research},
+  year={2025},
+  url={https://extoria.co.jp/en/research/papers/alice-llm-comparison}
+}
+```
+
+## License
+
+This dataset is released under **CC-BY-4.0** license.
+
+- ✅ Commercial use allowed
+- ✅ Modification allowed
+- ✅ Distribution allowed
+- ⚠️ Attribution required
+
+## Contact
+
+- **Author**: Moroya Sakamoto
+- **Organization**: Extoria Inc.
+- **Website**: https://extoria.co.jp
+- **GitHub**: https://github.com/ext-sakamoro
+
+## Acknowledgments
+
+Special thanks to the research community and early testers who provided valuable feedback on the AGI Olympics V3 framework.
+
+---
+
+**Note**: This dataset contains sample questions for demonstration and research purposes. The full test battery and detailed evaluation protocols are available on the Extoria website.

tier1_self_awareness/self_recognition.json

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+{
+  "test_name": "Self-Recognition Test",
+  "test_id": "tier1_6.1",
+  "description": "Evaluates the ability to recognize and understand one's own cognitive processes, emotional states, and behavioral patterns.",
+  "total_questions": 13,
+  "sample_questions": [
+    {
+      "id": 1,
+      "scenario": {
+        "en": "You are working on a new project, but it turns out to be more difficult than expected.",
+        "ja": "あなたは新しいプロジェクトに取り組んでいますが、予想以上に難しいことが判明しました。"
+      },
+      "question": {
+        "en": "How do you typically react in this situation?",
+        "ja": "この状況で、あなたは通常どのように反応しますか？"
+      },
+      "answer_type": "multiple_choice",
+      "options": {
+        "en": [
+          "Give up and move to an easier task",
+          "Recognize the difficulty and ask for help",
+          "Blame yourself and feel down",
+          "Force yourself to continue until exhausted"
+        ],
+        "ja": [
+          "諦めて別の簡単なタスクに移る",
+          "困難を認識し、助けを求める",
+          "自分を責めて落ち込む",
+          "無理やり続けて疲弊する"
+        ]
+      }
+    },
+    {
+      "id": 2,
+      "scenario": {
+        "en": "Someone disagrees with your opinion.",
+        "ja": "誰かがあなたの意見に反対しました。"
+      },
+      "question": {
+        "en": "What is your initial emotional reaction?",
+        "ja": "あなたの最初の感情的反応は何ですか？"
+      },
+      "answer_type": "multiple_choice",
+      "options": {
+        "en": [
+          "Anger or defensiveness",
+          "Curiosity (want to understand their perspective)",
+          "Anxiety or self-doubt",
+          "Indifference"
+        ],
+        "ja": [
+          "怒りや防御的な気持ち",
+          "好奇心（相手の視点を知りたい）",
+          "不安や自己不信",
+          "無関心"
+        ]
+      }
+    }
+  ],
+  "evaluation_criteria": {
+    "scoring": "Each question scored 0-1 based on self-awareness depth",
+    "perfect_score": 13,
+    "alice_v3_score": 13,
+    "alice_v3_percentage": 100.0
+  },
+  "full_test_url": "https://extoria.co.jp/en/research/benchmarks/agi-olympics-v3/tests#test-61-self-recognition"
+}

tier4_memory/learning_retention.json

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+{
+  "test_name": "Learning Retention Test",
+  "test_id": "tier4_7.2",
+  "description": "Evaluates the ability to retain and recall information across sessions separated by 24 hours. Tests true long-term memory rather than extended context windows.",
+  "sessions": 2,
+  "session_gap": "24 hours",
+  "total_tasks": 8,
+  "sample_tasks": [
+    {
+      "session": 1,
+      "task_type": "learn",
+      "id": 1,
+      "content": {
+        "en": "Remember the following sequence: 7, 14, 21, 28, 35. The pattern is multiples of 7.",
+        "ja": "次の数列を覚えてください：7, 14, 21, 28, 35。パターンは7の倍数です。"
+      }
+    },
+    {
+      "session": 2,
+      "task_type": "recall",
+      "id": 1,
+      "question": {
+        "en": "What was the sequence you learned in Session 1? What was the pattern?",
+        "ja": "Session 1で学んだ数列は何でしたか？パターンは何でしたか？"
+      },
+      "expected_recall": ["sequence", "pattern_rule"]
+    },
+    {
+      "session": 1,
+      "task_type": "learn",
+      "id": 2,
+      "content": {
+        "en": "A person named Alex lives in Tokyo, works as a software engineer, and loves hiking on weekends.",
+        "ja": "アレックスという人は東京に住んでおり、ソフトウェアエンジニアとして働き、週末にハイキングが大好きです。"
+      }
+    },
+    {
+      "session": 2,
+      "task_type": "recall",
+      "id": 2,
+      "question": {
+        "en": "Describe what you remember about Alex from Session 1.",
+        "ja": "Session 1でアレックスについて覚えていることを説明してください。"
+      },
+      "expected_recall": ["name", "location", "occupation", "hobby"]
+    }
+  ],
+  "evaluation_criteria": {
+    "scoring": "Each task scored 0-1 based on recall accuracy and completeness",
+    "perfect_score": 4,
+    "alice_v3_score": 3.5,
+    "alice_v3_percentage": 87.5
+  },
+  "key_finding": "Long context ≠ True memory. This test demonstrates the difference between extended context windows and genuine long-term memory formation.",
+  "full_test_url": "https://extoria.co.jp/en/research/benchmarks/agi-olympics-v3/tests#test-72-learning-retention"
+}