5. Hanna AI Engine: Satirical Logic and Debate

5.1 Transformer-Based Foundations

• Model Architecture: Built on a GPT-like architecture, but with specialized layers focusing on rhetorical strategies, comedic timing, and factual cross-checking.

• Large-Scale Pretraining: Exposed to diverse corpora (political transcripts, comedic roasts, news analyses) to better model sarcasm, irony, and truth-based arguments.

5.2 Exposing Truth Through Contextual Analysis

• Relevance Retrieval: Before generating a comedic or investigative answer, the model retrieves relevant context from the aggregator’s enriched knowledge base.

• Cross-Reference: Compares contradictory statements or suspicious patterns, weaving them into a satirical narrative that highlights hypocrisy or misinformation.

• Factual Confidence: The model internally scores how certain it is about each claim. If confidence is low, it may present disclaimers or comedic disclaimers about speculation.

5.3 Reinforcement Learning from Human Feedback (RLHF)

1. User Feedback Loop: Upvotes/downvotes, factual correctness flags, or comedic depth ratings form the reward signal.

2. Multi-Objective Reward: Balances comedic punch, truth exposure, ethical constraints, and overall clarity.

3. Incremental Model Updates: In scheduled intervals (e.g., every 12 hours), the AI is fine-tuned with the newly accrued feedback. If the updated model surpasses baseline metrics, it’s promoted to production — unless the DAO intervenes for ethical or comedic reasons.