🧠 SymLM

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.

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📐 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.

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🎯 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.

• Building adaptive cognition systems
  Can serve as a symbolic kernel for reflective AI agents and knowledge evolution pipelines.

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⚠️ 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.

• 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.

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⚙️ Training Procedure

This checkpoint is currently in experimental phase.

🧪 Training Hyperparameters

• 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

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🧱 Framework Versions

• 🤗 Transformers: 4.51.3
• 🧠 PyTorch: 2.7.0+cu126
• 📚 Datasets: 3.5.0
• 🔤 Tokenizers: 0.21.1

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📚 Research Foundations

SymbioticLM builds upon a cohesive theoretical framework for dynamic reasoning and neuro-symbolic learning:

🔁 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

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🧬 Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)

A memory structure inspired by biological helices, enabling thought persistence through spiral-layered contextual encodings across time.

Focus: Long-term token evolution, normalized replay, thought continuity

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🧠 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

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📐 Theoretical Underpinning

The Analytic Foundations Theorem (AFT)

A rigorous, measure-theoretic replacement for classical calculus: replaces pointwise derivatives with discrepancy-driven integral convergence across vanishing sets.

Applies to:

• Symbolic gradients
• Gradient-free optimization
• Discrete logic approximation in function spaces

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These form the mathematical and architectural core of SymbioticLM, enabling:

• 🧠 Neuro-symbolic cognitive evolution
• 🔁 Multi-agent dynamic feedback coordination
• 📏 Formal memory through discrepancy-based logic

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