Brain-inspired architecture separates generation and evaluation

The concept of decoupling generation and evaluation addresses the high computational cost and consensus-seeking nature of large monolithic models.

• –By using small models for generation, the system mimics the brain's low-energy Default Mode Network (DMN).
• –Framing hallucination as a mechanism for discovering obscure knowledge and genuine novelty shifts the paradigm from suppression to utilization.
• –The challenge lies in orchestrating these unaligned models and ensuring the evaluator can effectively parse high-variance noise without overwhelming computational cost.
• –Highlights a growing interest in multi-model and agentic workflows over simply scaling up single models.