AtomMind is a modular, domain-specialized Small Language Model designed to perform reasoning, and computation across Mathematics, Physics, Chemistry, and Biology.
Unlike general-purpose LLMs, AtomMind just focuses on deep logical reasoning, symbolic computation, and cross-domain scientific problem-solving. Therefore it's lighter, dont cost much and more effective
- Domain Expertise: Separate Domain Expert Networks (DENs) for Math, Physics, Chemistry, and Biology.
- Cross-Domain Reasoning: General Knowledge Backbone (GKB) integrates knowledge across fields.
- Symbolic Computation: Symbolic Reasoning Module (SRM) handles equations, chemical graphs, and biological structures.
- Adaptive Learning: Optimization & Algorithmic Module (OAM) supports meta-learning and reinforcement-guided optimization.
- Memory System (Optional): Episodic and long-term memory using FAISS or Milvus for efficient knowledge storage and controlled forgetting.
- Self-Monitoring: Tracks reasoning performance, accuracy, and contradictions for continual improvement.
- Domain Expert Networks (DENs): 30–40 layers per domain, hidden size 512–1024, attention heads 8–16.
- General Knowledge Backbone (GKB): 20–30 layers, integrates DEN outputs for cross-domain problem-solving.
- Symbolic Reasoning Module (SRM): 10–20 layers, can include GNN layers for structured data reasoning.
- Optimization & Algorithmic Module (OAM): Implements meta-learning, RL-guided optimization, and algorithmic adaptation.
- Data Source: Curated scientific datasets (JSON/JSONL) from OpenRouter.
- Multi-Stage Training:
- Pretraining on structured scientific data
- Domain specialization for each DEN
- Integration with GKB + SRM
- Optional meta-learning guided by OpenRouter
- Optimization: AdamW, AdaFactor, LAMB; mixed precision and gradient clipping
- Curriculum Learning: Tasks start simple and progressively increase in complexity
Multi-agent Meta-controller, orchestrating training, evaluation, and dataset generation. Key agent roles:
- Planner: Task decomposition and dataset selection
- Executor: Knowledge generation and test creation
- Critic: Evaluates outputs for correctness and consistency
- Trainer / Data Curator: Formats and weights training data
- Memory Agent: Stores reasoning traces, logs, and knowledge
Self-Learning Loop: It will identifies weak domains, generates stress-test tasks, and orchestrates retraining for continual improvement.
- Framework: PyTorch / PyTorch Lightning
- Tokenizer: GPT-2 or custom scientific tokenizer
- Hardware: Multi-GPU / TPU support
- Optional Memory Systems: FAISS / Milvus for embeddings and knowledge management
- Monitoring: Logs accuracy, reasoning performance, contradictions, and reward scores
- Advanced scientific reasoning across multiple domains
- Symbolic equation solving and computation
- Cross-domain integration and predictions
- Efficient learning from curated datasets
- Continual self-monitoring and improvement
For questions, collaboration, or contributions, you can reach out via:
- GitHub: AtomMind-Ai
- Email: [email protected]
Feel free to open issues or pull requests on GitHub for discussion and contributions.