Update README.md

README.md

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 pipeline_tag: text-generation
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-# SymLM
+# 🧠 SymLM
 
-SymbioticLM is a hybrid symbolic–neural language model architecture that integrates a frozen transformer backbone (Qwen2ForCausalLM) with a suite of cognitive modules designed for adaptive, interpretable reasoning. These modules include:
+**SymbioticLM** is a hybrid symbolic–neural language model that integrates a frozen transformer backbone (`Qwen2ForCausalLM`) with a suite of symbolic cognitive modules for adaptive, interpretable reasoning.
 
-## Model description
+---
+
+## 📐 Model Description
+
+The architecture fuses neural token-level generation with symbolic introspection and reasoning:
+
+- **Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)**  
+  Enables structured long-term memory and spiral-context encoding across tokens.
+
+- **Multi-Agent Symbiotic Response Mechanisms (M.A.S.R.M)**  
+  Coordinates symbolic-neural agents via gated attention and adaptive response layers.
+
+- **QwenExoCortex**  
+  Projects contextual hidden states from the Qwen model into a symbolic fusion space for reasoning and memory replay.
+
+- **Symbolic processors**  
+  Includes:
+  - `ThoughtDynamicsLNN`
+  - `Liquid / Crystalline Processors`
+  - `Graph Reasoning with DNAConv`
+  - A rolling `ThoughtMemory`
+
+This enables real-time fusion of symbolic thinking, token generation, and reasoning-aware language modeling.
+
+---
+
+## 🎯 Intended Uses & Limitations
+
+### ✅ Intended Uses
+
+- **Mathematical reasoning and proof generation**  
+  Fine-tuned on *MetaMathQA*, optimized for symbolic Q&A, equation logic, and structured inference.
+
+- **Symbolic-cognitive AI research**  
+  Useful for studying attention modulation, memory replay, and neural-symbolic interface dynamics.
 
+- **Low-resource adaptation**  
+  Modular memory and projection design enables meaningful performance even with smaller datasets.
 
-Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)
-Enables structured long-term memory and spiral-context encoding across tokens.
+- **Building adaptive cognition systems**  
+  Can serve as a symbolic kernel for reflective AI agents and knowledge evolution pipelines.
 
-Multi-Agent Symbiotic Response Mechanisms (M.A.S.R.M)
-Coordinates symbolic-neural agents via gated attention and adaptive response layers.
+---
+
+### ⚠️ Limitations
+
+- **Limited training scale**  
+  Trained on 25,000 MetaMathQA examples. Effective for symbolic form, but not yet broad generalization.
+
+- **No RLHF or alignment**  
+  Outputs are not tuned for safety or instruction alignment and may hallucinate.
 
-QwenExoCortex
-Projects contextual hidden states from the Qwen model into a symbolic fusion space for reasoning and memory replay.
+- **Fluency ≠ correctness**  
+  Symbolic fluency does not imply mathematically valid proofs. Verification is recommended.
+
+- **Not optimized for open-domain generation**  
+  This model prioritizes logic and structure over conversational depth.
+
+---
 
-ThoughtDynamics LNN, Liquid / Crystalline Processors, Graph Reasoning with DNAConv, and a rolling ThoughtMemory
-These components support symbolic modulation, structural consistency, and dynamic feedback across layers.
+## ⚙️ Training Procedure
 
-This architecture enables real-time fusion of symbolic thinking, token generation, and reasoning-aware response generation — all fully compatible with Hugging Face transformers.
+This checkpoint is currently in experimental phase.
 
-## Intended uses & limitations
+### 🧪 Training Hyperparameters
 
-Mathematical reasoning and proof generation
-Trained on MetaMathQA, SymbioticLM excels at question-answer pairs requiring symbolic logic, equation manipulation, or structured reasoning.
+- **learning_rate**: `3e-5`  
+- **train_batch_size**: `16`  
+- **eval_batch_size**: `16`  
+- **gradient_accumulation_steps**: `64`  
+- **total_train_batch_size**: `1024`  
+- **optimizer**: `AdamW`, betas=(0.9, 0.999), epsilon=1e-08  
+- **lr_scheduler_type**: `cosine`  
+- **warmup_steps**: `500`  
+- **num_epochs**: `3`  
+- **mixed_precision_training**: `Native AMP`
 
-Symbolic-cognitive research
-Ideal for evaluating neuro-symbolic mechanisms, memory replay, and dynamic gate adaptation in language modeling.
+---
 
-Low-resource adaptive training
-Due to its modularity and memory components, the model can perform meaningfully even with relatively small fine-tuning datasets.
+## 🧱 Framework Versions
 
-Foundation for adaptive cognition systems
-Acts as a core module in broader AI architectures requiring internal state reflection and dynamic memory use.
+- 🤗 Transformers: `4.51.3`  
+- 🧠 PyTorch: `2.7.0+cu126`  
+- 📚 Datasets: `3.5.0`  
+- 🔤 Tokenizers: `0.21.1`
 
-Limited training scale
-This checkpoint is trained on 25,000 examples from MetaMathQA — effective for structure, but not broad generalization.
+---
 
-No RLHF / alignment
-The model has no reinforcement learning from human feedback (RLHF) or safety tuning. Outputs may reflect hallucinations or errors.
+## 📚 Research Foundations
 
-Mathematical fluency ≠ correctness
-Language fluency should not be mistaken for rigorous proof — outputs should be verified before downstream use.
+SymbioticLM builds upon a cohesive theoretical framework for dynamic reasoning and neuro-symbolic learning:
 
-Not optimized for general text generation
-Although capable, its symbolic structure is tuned toward reasoning and logic, not open-domain chat.
+### 🔁 Multi-Agent Symbiosis and Dynamic Thought
 
+**Rapid Adaptation via Multi-Agent Symbiotic Response Mechanisms (M.A.S.R.M)**  
+> A framework where symbolic and neural agents dynamically adapt via gated feedback, memory modulation, and agent-based specialization.
 
+**Focus**: Multi-agent control, reflective learning, contextual responsiveness
 
+---
 
-## Training procedure
+### 🧬 Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)
 
-This model is still undergoing development. 
+> A memory structure inspired by biological helices, enabling thought persistence through spiral-layered contextual encodings across time.
 
-### Training hyperparameters
+**Focus**: Long-term token evolution, normalized replay, thought continuity
 
-The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- gradient_accumulation_steps: 64
-- total_train_batch_size: 1024
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 500
-- num_epochs: 3
-- mixed_precision_training: Native AMP
+---
 
+### 🧠 Integrating DTE-HDM + M.A.S.R.M for Adaptive AI
 
+> Combines symbolic evolution and multi-agent adaptation to construct an LLM that reflects, adapts, and deepens reasoning through internal dynamics.
 
+**Result**: A system that *learns faster*, *adapts deeper*, and *thinks symbolically*
 
-### Framework versions
+---
 
-- Transformers 4.51.3
-- Pytorch 2.7.0+cu126
-- Datasets 3.5.0
-- Tokenizers 0.21.1
-  
-### Research Foundations
-SymbioticLM is grounded in a suite of original research papers and formal theoretical advancements that push the boundaries of adaptive language modeling, symbolic reasoning, and neuro-symbolic integration:
+### 📐 Theoretical Underpinning
 
-### Multi-Agent Symbiosis and Dynamic Thought
-Rapid Adaptation via Multi-Agent Symbiotic Response Mechanisms (M.A.S.R.M)
-Introduces a multi-agent coordination framework where symbolic and neural agents dynamically adjust to input signals through gated interaction and adaptive feedback.
-Focus: responsiveness, memory modulation, gate-driven specialization.
+**The Analytic Foundations Theorem (AFT)**  
+> A rigorous, measure-theoretic replacement for classical calculus: replaces pointwise derivatives with discrepancy-driven integral convergence across vanishing sets.
 
-### Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)
-Proposes a novel memory architecture inspired by biological DNA dynamics and helical signal structures. Integrates a spiraled encoding mechanism that allows thought representations to evolve continuously across token sequences.
-Focus: continuity of reasoning, memory integration, and symbolic persistence.
+**Applies to**:  
+- Symbolic gradients  
+- Gradient-free optimization  
+- Discrete logic approximation in function spaces
 
-### Integrating DTE-HDM with M.A.S.R.M for Adaptive AI
-Combines the helical-memory backbone with a multi-agent symbolic system to construct a language model capable of contextual growth, reflective reasoning, and dynamic attention allocation.
-Result: a system that learns faster, adapts deeper, and reflects symbolically.
+---
 
-### Theoretical Underpinning
-The Analytic Foundations Theorem (AFT)
-A rigorous, measure-theoretic generalization of the Fundamental Theorem of Calculus. AFT replaces classical pointwise differentiation with discrepancy-driven integration over vanishing measure sets, enabling symbolic gradient logic applicable to AI reasoning.
-Applies to: gradient-free optimization, symbolic dynamics, and function space convergence.
+These form the **mathematical and architectural core** of SymbioticLM, enabling:
 
-These papers form the mathematical and architectural backbone of SymbioticLM, enabling:
+- 🧠 *Neuro-symbolic cognitive evolution*  
+- 🔁 *Multi-agent dynamic feedback coordination*  
+- 📏 *Formal memory through discrepancy-based logic*
 
-Neuro-symbolic cognitive evolution
+---
 
-Multi-agent dynamic response coordination
 
-Formal memory representation through integral discrepancy logic