🧠 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.
📐 Model Description
The architecture fuses neural token-level generation with symbolic introspection and reasoning:
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Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)
Enables structured long-term memory and spiral-context encoding across tokens.
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Multi-Agent Symbiotic Response Mechanisms (M.A.S.R.M)
Coordinates symbolic-neural agents via gated attention and adaptive response layers.
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QwenExoCortex
Projects contextual hidden states from the Qwen model into a symbolic fusion space for reasoning and memory replay.
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Symbolic processors
Includes:
ThoughtDynamicsLNNLiquid / Crystalline ProcessorsGraph 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
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Mathematical reasoning and proof generation
Fine-tuned on MetaMathQA, optimized for symbolic Q&A, equation logic, and structured inference.
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Symbolic-cognitive AI research
Useful for studying attention modulation, memory replay, and neural-symbolic interface dynamics.
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Low-resource adaptation
Modular memory and projection design enables meaningful performance even with smaller datasets.
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Building adaptive cognition systems
Can serve as a symbolic kernel for reflective AI agents and knowledge evolution pipelines.
⚠️ Limitations
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Limited training scale
Trained on 25,000 MetaMathQA examples. Effective for symbolic form, but not yet broad generalization.
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No RLHF or alignment
Outputs are not tuned for safety or instruction alignment and may hallucinate.
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Fluency ≠ correctness
Symbolic fluency does not imply mathematically valid proofs. Verification is recommended.
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Not optimized for open-domain generation
This model prioritizes logic and structure over conversational depth.
⚙️ 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
🧱 Framework Versions
- 🤗 Transformers:
4.51.3
- 🧠 PyTorch:
2.7.0+cu126
- 📚 Datasets:
3.5.0
- 🔤 Tokenizers:
0.21.1
📚 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
🧬 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
🧠 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
📐 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
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
Convergent Intelligence Portfolio
Related Models
Top Models from Our Lab
Total Portfolio: 41 models | 2,781 total downloads
Last updated: 2026-03-28 12:57 UTC
From the Convergent Intelligence Portfolio
DistilQwen Collection — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware.
Convergent Intelligence LLC: Research Division
Discrepancy Calculus Foundation
This model is part of the
Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model
should produce and what it
actually produces.
DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the learning problem. Key concepts:
- Discrepancy Operator (D): Measures the gap between expected and observed behavior at each training step
- Jump Sets: Boundaries where model behavior changes discontinuously — these are features, not bugs
- Ghost Imprinting: Teacher knowledge that transfers to student models through weight-space topology rather than explicit distillation signal
For the full mathematical treatment, see
Discrepancy Calculus: Foundations and Core Theory (DOI: 10.57967/hf/8194).
Citation chain: Structure Over Scale (DOI: 10.57967/hf/8165) →
Three Teachers to Dual Cognition (DOI: 10.57967/hf/8184) →
Discrepancy Calculus (DOI: 10.57967/hf/8194)
