Update README.md

README.md

@@ -15,27 +15,60 @@ base_model:
 pipeline_tag: text-generation
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # SymLM
 
-This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+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:
 
 ## Model description
 
-More information needed
+
+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.
+
+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.
+
+This architecture enables real-time fusion of symbolic thinking, token generation, and reasoning-aware response generation — all fully compatible with Hugging Face transformers.
 
 ## Intended uses & limitations
 
-More information needed
+Mathematical reasoning and proof generation
+Trained on MetaMathQA, SymbioticLM excels at question-answer pairs requiring symbolic logic, equation manipulation, or structured reasoning.
+
+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.
+
+Foundation for adaptive cognition systems
+Acts as a core module in broader AI architectures requiring internal state reflection and dynamic memory use.
+
+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.
+
+Mathematical fluency ≠ correctness
+Language fluency should not be mistaken for rigorous proof — outputs should be verified before downstream use.
+
+Not optimized for general text generation
+Although capable, its symbolic structure is tuned toward reasoning and logic, not open-domain chat.
+
 
-## Training and evaluation data
 
-More information needed
 
 ## Training procedure
 
+This model is still undergoing development. 
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -51,7 +84,6 @@ The following hyperparameters were used during training:
 - num_epochs: 3
 - mixed_precision_training: Native AMP
 
-### Training results
 
 
 
@@ -60,4 +92,33 @@ The following hyperparameters were used during training:
 - Transformers 4.51.3
 - Pytorch 2.7.0+cu126
 - Datasets 3.5.0
-- Tokenizers 0.21.1
+- 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:
+
+### 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.
+
+### 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.
+
+### 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 papers form the mathematical and architectural backbone of SymbioticLM, enabling:
+
+Neuro-symbolic cognitive evolution
+
+Multi-agent dynamic response coordination
+
+Formal memory representation through integral discrepancy logic