beyond-llms git:20261014.de0823cA

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---
name: beyond-llms
description: "Implement advanced neuro-symbolic and genetic algorithm techniques. Use when: implement active inference, add neuro-symbolic logic, optimize using genetic algorithms."
license: CC-BY-SA-4.0
metadata:
  source: "agents/skills/ai/beyond-llms.skill.md"
  tags: "ai"
  core: "false"
  context_cost: "high"
---
# 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.

### 3. LLM & Agent Guardrails
- **Logic Subversion**: Do not allow the LLM to rewrite the deterministic rules of the symbolic engine during runtime.
- **Opaque Reasoning**: Force the hybrid architecture to emit a trace explaining exactly which sub-system (LLM or Symbolic) made the final decision.