beyond-llms

Goal

Implement and integrate advanced AI methodologies that go beyond basic foundational models, including neuro-symbolic logic, genetic algorithms, or active inference loops.

Steps

1. Analyze Constraints: Determine the problem domain requiring non-LLM or hybrid-AI reasoning.
2. Design Algorithm: Combine semantic parsing (LLM) with deterministic rule-engines (symbolic).
3. Integrate Logic: Build the hybrid pipeline, ensuring symbolic constraints can override probabilistic LLM outputs.
4. Verification: Run extensive tests to verify the symbolic engine successfully enforces hard rules over hallucinatory LLM outputs.

Security & Guardrails

1. Skill Security

• Computational Exhaustion: Genetic algorithms and advanced loops can easily consume infinite compute. Bind execution to absolute generational limits and strict timeouts.
• Unbounded Memory Use: Limit the size of hypothesis populations or world-models in active inference.

2. System Integration Security

• Sandbox Execution: When neuro-symbolic engines evaluate dynamic formulas or math, execute them via secure sandboxes to prevent code injection.
• Data Boundary Handling: Ensure symbolic reasoners don't bypass established data access controls.