CUP 2.0 builds upon the original CUP framework to deliver stronger memory capabilities, efficiency gains, and deeper symbolic reasoning. It is not just an iteration — it refines the architecture for practical integration in complex systems.
Key Improvements in CUP 2.0
- Enhanced memory modules that allow longer retention, context recall and chaining.
- Better invertibility: outputs can more reliably reconstruct inputs in symbolic domains.
- Optimization tweaks for compactness, speed, and resource usage.
- Support for hybrid symbolic/neural pipelines: interface smooth transitions between logic modules and neural computation.
Architecture & Features
- Modular core units remain invertible, with adaptive activation functions.
- Memory layers: short-term, mid-term, long-term with differential access.
- Context masks and modulation remain core, now with improved gating and dynamic weighting.
- Symbolic embedding: allows the system to encode logical statements, rules or symbolic tokens in its structure.
- Evolution and mutation paths: modules can adapt or be replaced as system demands change.
Use Cases & Scenarios
- Embedding in agents or simulators where memory is essential (narrative agents, planning systems).
- Symbolic reasoning pipelines that require a neural “core” to support logic + noise.
- Knowledge graph augmentation: bridging neural-net output and symbolic representations.
- Research, prototyping, experimentation in hybrid AI systems.