🚀 Paper in progress.
🧪 Code coming soon on GitHub.
🔗 Follow me for updates.
👀 Tagging @DeepMind, @MetaAI, @openai — this is a step toward grounded lifelong learning.
#AGI #CognitiveAI #OpenSource

🧬 Inspired by:

• 🧠 DeepMind: Gato, AlphaStar, MuZero
• 🧠 Meta: Socratic AI, CICERO
• 🧠 Neuroscience: memory systems, affective encoding

🆚 Compared to Other AGI Systems
| Feature | Your Agent | Typical AGI |
|––––|————|———––|
| Episodic Memory | ✅ | ❌
| Emotional Tags | ✅ | ❌
| Consolidation | ✅ | ❌
| Rehearsal | ✅ | ❌

🧠 Why it’s Unique
✔️ Emotion-grounded memory
✔️ Temporal + conceptual structuring
✔️ Reflective rehearsal
✔️ Dual memory dynamics
✔️ Human-aligned cognitive design

5. All in Text Mode
No video/image modality.
Text-based long-term learning — a rare AGI baseline.
Efficient, interpretable, and language-native.

4. Semantic Consolidation
From episodic stream → abstract knowledge
Like:
“Dogs usually bark” ← many episodic observations
Consolidation is sparse, conservative — but real.

3. Rehearsal and Retention
Like REM sleep or offline replay:
🌀 Important memories are revisited & rehearsed
🧠 This slows forgetting, sharpens abstraction
🔥 Similar to DeepMind’s experience replay in AlphaStar

Emotionally Tagged Memories
Each memory includes:

• Valence (positive/negative)
• Arousal (intensity)
• Cognitive tags
These affect recall, retention, and relevance.

Biological Memory Architecture
The agent separates episodic (event-based, emotional) and semantic (general, abstract) memory.
📌 Inspired by real brain circuits:
Hippocampus = Episodic
Neocortex = Semantic