reaperdoesntknow/Symbiotic-8B
reaperdoesntknow/Symbiotic-8B is a hybrid symbolic–transformer model developed by Convergent Intelligence LLC: Research Division, built upon the Qwen-8B base. It integrates modular symbolic processors and a persistent memory buffer to combine high-fidelity language generation with long-memory symbolic reasoning. This model excels at deep symbolic tasks such as theorem generation, logical chaining, and structured reasoning with retained memory across turns, making it suitable for complex cognitive applications.
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SymbioticLM-8B: Hybrid Symbolic–Transformer Model
SymbioticLM-8B, developed by Convergent Intelligence LLC: Research Division, is a unique hybrid model that merges an 8-billion parameter Qwen-based transformer with advanced symbolic cognition capabilities. Unlike traditional LLMs, it features modular symbolic processors and a persistent memory buffer, enabling it to perform deep symbolic reasoning while maintaining high-fidelity language generation.
Key Capabilities
- Hybrid Architecture: Combines a Qwen-8B transformer backbone with specialized symbolic modules like ThoughtDynamicsLNN, CrystallineProcessor, LiquidThoughtProcessor, and HelicalDNAProcessor.
- Persistent Memory: Utilizes a 2048-vector symbolic memory with entropy-aware retrieval for contextual recall across interactions.
- Deep Symbolic Reasoning: Excels at tasks such as theorem generation, logical chaining, and structured reasoning, retaining memory over multiple turns.
- "Dream Mode": Features an offline self-generation capability for symbolic cognition.
- Discrepancy Calculus Foundation: Developed under the DISC framework, which treats training singularities as structural signals for understanding learning geometry.
Intended Use Cases
- General symbolic reasoning and logical conversation.
- Memory-aware tutoring and research assistance.
- Code and mathematical proof modeling.
- Context-persistent dialogue systems requiring long-term memory.
Limitations
- Not instruction-tuned, which may require prompt engineering for chat-style inputs.
- Larger memory buffer can slightly increase CPU load.
- Symbolic inference is offline-evolved, requiring active memory seeding.