Update README_ModularBrainAgent_HF.md

README_ModularBrainAgent_HF.md

@@ -12,68 +12,36 @@ tags:
 # ModularBrainAgent 🧠
 **Author:** Aliyu Lawan Halliru (`@Almusawee`)  
 **Affiliation:** Independent AI Researcher (Nigeria)  
-**License:** MIT (code and architecture)  
-**Published:** 2025  
-**Paper:** [Download PDF](./ModularBrainAgent_Paper_With_Diagram.pdf)  
-**Diagram:** ![Architecture Diagram](./visual_diagram_full_architecture.png)
+**License:** MIT  
+**Paper:** [Download PDF](./ModularBrainAgent_Paper.pdf)  
+**Diagram:** (Coming soon)
 
 ---
 
-## 🧠 Overview
+## 🧠 Abstract
 
-**ModularBrainAgent** is a biologically inspired AI architecture designed to simulate cognitive processing in a modular, interpretable, and task-general way. It integrates:
+We propose ModularBrainAgent, a biologically motivated neural architecture for multi-task learning that mirrors the functional organization of the human brain. Unlike monolithic deep networks, our model is designed with architectural intelligence: distinct modular subsystems that reflect perceptual, attentional, memory, and decision-making pathways in biological cognition.
 
-- Spiking Sensory Neurons  
-- Attention-based Relay Layer  
-- Adaptive Interneuron Logic  
-- Neuroendocrine Gain Modulation  
-- Recurrent Autonomic Processor (GRU-based)  
-- Mirror Comparator for goal-state reflection  
-- Multi-modal encoders (GRU, CNN, shared)  
-- Task-specific heads (regression, classification, vision)  
-- Replay Buffer for continual learning
+Each component — including spiking sensory processors, adaptive interneurons, relay routing layers, neuroendocrine gain modulators, recurrent autonomic loops, and mirror-state comparators — serves a unique cognitive function. These modules are not just trainable; they are structurally positioned to enable learning itself. This built-in cognitive topology improves sample efficiency, interpretability, and continual adaptability.
 
----
-
-## 🧬 Biological Motivation
-
-The architecture mirrors functional regions of the human brain, including:
-- Cortex-like layered processing  
-- Attention routing  
-- Local learning with surrogate gradients  
-- Adaptive spiking thresholds  
-- Memory replay (like hippocampal consolidation)
+The model supports multimodal input via GRUs, CNNs, and shared encoders, and leverages a task-specific replay buffer for lifelong learning. Experimental design favors generalization across domains and tasks with minimal interference. We argue that structural cognition — not just data or gradient optimization — is the key to general-purpose artificial intelligence. ModularBrainAgent provides a functional and extensible blueprint for biologically plausible, task-flexible, and memory-capable AI systems.
 
 ---
 
-## 📌 Use Cases
+## 📌 Architecture Overview
 
-- Multi-task AI research
-- Biologically plausible modeling
-- Lifelong learning agents
-- Modular interpretable systems
-- Neuro-AI cognitive simulations
-
----
-
-## 📝 Citation (arXiv/Preprint Ready)
-
-```
-@misc{halliru2025modularbrainagent,
-  title={ModularBrainAgent: A Brain-Inspired Modular Neural Architecture for Cognitive Multi-Task Learning},
-  author={Aliyu Lawan Halliru},
-  year={2025},
-  url={https://huggingface.co/Almusawee/ModularBrainAgent}
-}
-```
-
----
-
-## 📎 Files Included
-- `ModularBrainAgent_Paper_With_Diagram.pdf` – Full paper with abstract, architecture, references
-- `visual_diagram_full_architecture.png` – Schematic of forward-pass and modular connections
+- Spiking sensory neurons for input encoding  
+- Attention-based relay for signal routing  
+- Adaptive interneuron logic for abstraction  
+- Neuroendocrine modulation (gain control)  
+- GRU-based recurrent loop (autonomic memory)  
+- Mirror comparator for goal-state reflection  
+- Replay buffer with task tagging  
+- Multimodal encoders and task heads
 
 ---
 
 ## 🤝 License
-This work is licensed under the **MIT License** — free to use, modify, and distribute with credit to the author.
+MIT License (free to use, adapt, and build upon with attribution)
+
+## 📝 Citation