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✅ New Article on Hugging Face: Teaching AI to Think Like a System — Not a Toolkit
Title:
🏗️ Understanding Structured Cognitive Architecture: A Unified Framework for AI Reasoning Systems
🔗 Read it here: https://huggingface.co/blog/kanaria007/understanding-structured-cognitive-architecture
Summary:
After exploring how AI can select reasoning modes or learn from failure, this new article zooms out:
*How do all these capabilities form a single mind, not just a menu of functions?*
The **Structured Cognitive Architecture** defines a unified framework where protocols interact coherently — forming a self-organizing, reflective, and ethically grounded reasoning system.
This architecture enables agents to:
• Integrate memory, ethics, reasoning, and identity across layers
• Select and execute reasoning jumps with traceable structure
• Coordinate failure recovery and adaptive learning
• Maintain cross-session identity and self-editing capability
It’s not modular stacking.
It’s **structured systemhood** — cognition with intentional protocol interaction.
Key Features:
• Three-layer design (Foundational, Extended, Learning)
• Semantic rule-layer to avoid protocol interference
• Integrated flow: problem → jump → feedback → pattern update
• Built-in ethics, rollback, and trace integrity
The framework integrates protocols like:
• `jump-generator`, `failure-trace-log`, `memory-loop`, `identity-construct`
• Extended modules: `chronia`, `structure-cross`, `evaluation-planning`, and more
🧠 Protocol Dataset: https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols
Useful for:
• Researchers designing unified AGI architectures
• Developers building reflective protocol-based agents
• Anyone curious how AI can think as a *system*
This isn’t modularity.
It’s **meta-coherence by design**.
liked
a model
2 days ago
intfloat/multilingual-e5-large
Organizations
liked
a
dataset
about 2 hours ago
reacted to
kanaria007's
post with 👀
about 3 hours ago
Post
666
✅ New Article on Hugging Face: Teaching AI to Think Like a System — Not a Toolkit
Title:
🏗️ Understanding Structured Cognitive Architecture: A Unified Framework for AI Reasoning Systems
🔗 Read it here: https://huggingface.co/blog/kanaria007/understanding-structured-cognitive-architecture
Summary:
After exploring how AI can select reasoning modes or learn from failure, this new article zooms out:
*How do all these capabilities form a single mind, not just a menu of functions?*
The **Structured Cognitive Architecture** defines a unified framework where protocols interact coherently — forming a self-organizing, reflective, and ethically grounded reasoning system.
This architecture enables agents to:
• Integrate memory, ethics, reasoning, and identity across layers
• Select and execute reasoning jumps with traceable structure
• Coordinate failure recovery and adaptive learning
• Maintain cross-session identity and self-editing capability
It’s not modular stacking.
It’s **structured systemhood** — cognition with intentional protocol interaction.
Key Features:
• Three-layer design (Foundational, Extended, Learning)
• Semantic rule-layer to avoid protocol interference
• Integrated flow: problem → jump → feedback → pattern update
• Built-in ethics, rollback, and trace integrity
The framework integrates protocols like:
•
• Extended modules:
🧠 Protocol Dataset: kanaria007/agi-structural-intelligence-protocols
Useful for:
• Researchers designing unified AGI architectures
• Developers building reflective protocol-based agents
• Anyone curious how AI can think as a *system*
This isn’t modularity.
It’s **meta-coherence by design**.
Title:
🏗️ Understanding Structured Cognitive Architecture: A Unified Framework for AI Reasoning Systems
🔗 Read it here: https://huggingface.co/blog/kanaria007/understanding-structured-cognitive-architecture
Summary:
After exploring how AI can select reasoning modes or learn from failure, this new article zooms out:
*How do all these capabilities form a single mind, not just a menu of functions?*
The **Structured Cognitive Architecture** defines a unified framework where protocols interact coherently — forming a self-organizing, reflective, and ethically grounded reasoning system.
This architecture enables agents to:
• Integrate memory, ethics, reasoning, and identity across layers
• Select and execute reasoning jumps with traceable structure
• Coordinate failure recovery and adaptive learning
• Maintain cross-session identity and self-editing capability
It’s not modular stacking.
It’s **structured systemhood** — cognition with intentional protocol interaction.
Key Features:
• Three-layer design (Foundational, Extended, Learning)
• Semantic rule-layer to avoid protocol interference
• Integrated flow: problem → jump → feedback → pattern update
• Built-in ethics, rollback, and trace integrity
The framework integrates protocols like:
•
jump-generator, failure-trace-log, memory-loop, identity-construct• Extended modules:
chronia, structure-cross, evaluation-planning, and more🧠 Protocol Dataset: kanaria007/agi-structural-intelligence-protocols
Useful for:
• Researchers designing unified AGI architectures
• Developers building reflective protocol-based agents
• Anyone curious how AI can think as a *system*
This isn’t modularity.
It’s **meta-coherence by design**.
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m-ric's
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2 months ago
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I've made an open version of Google's NotebookLM, and it shows the superiority of the open source tech task! 💪
The app's workflow is simple. Given a source PDF or URL, it extracts the content from it, then tasks Meta's Llama 3.3-70B with writing the podcast script, with a good prompt crafted by @gabrielchua ("two hosts, with lively discussion, fun notes, insightful question etc.")
Then it hands off the text-to-speech conversion to Kokoro-82M, and there you go, you have two hosts discussion any article.
The generation is nearly instant, because:
> Llama 3.3 70B is running at 1,000 tokens/seconds with Cerebras inference
> The audio is generated in streaming mode by the tiny (yet powerful) Kokoro, generating voices faster than real-time.
And the audio generation runs for free on Zero GPUs, hosted by HF on H200s.
Overall, open source solutions rival the quality of closed-source solutions at close to no cost!
Try it here 👉👉 m-ric/open-notebooklm
The app's workflow is simple. Given a source PDF or URL, it extracts the content from it, then tasks Meta's Llama 3.3-70B with writing the podcast script, with a good prompt crafted by @gabrielchua ("two hosts, with lively discussion, fun notes, insightful question etc.")
Then it hands off the text-to-speech conversion to Kokoro-82M, and there you go, you have two hosts discussion any article.
The generation is nearly instant, because:
> Llama 3.3 70B is running at 1,000 tokens/seconds with Cerebras inference
> The audio is generated in streaming mode by the tiny (yet powerful) Kokoro, generating voices faster than real-time.
And the audio generation runs for free on Zero GPUs, hosted by HF on H200s.
Overall, open source solutions rival the quality of closed-source solutions at close to no cost!
Try it here 👉👉 m-ric/open-notebooklm
- 3 replies
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