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Can two “identical” AIs develop radically different personalities even without rewards or external pressure? In a 100-run developmental experiment I ran, the answer looks like yes. Across 100 twin runs: • Shared neural dynamics? Mild but consistent coupling. • Shared emotions? Near-zero correlation on average – affective trajectories diverge. •...
Summary: Over the past several months, I’ve been prototyping a developmental alternative to RLHF-based alignment. Instead of treating agents as static optimizers whose behavior is shaped by reward signals, this approach models growth, self-organization, and developmental constraints inspired by early cognitive systems. This week, the system called Twins V3 reached...
Current alighnemnt methods (RLHF, Constitutional AI, etc.) create reproducible behavioral artifacts at their safety boundaries - patterns like over apologizing, self negation, and incoherent self description. This paper proposes a five part taxonomz of these "alighnemnt stress signatures", showing how they emerge from the structure of current alignment architectures rather...
Author’s Note: This paper introduces the Hybrid Reflective Learning System (HRLS) a framework for transforming AI safety from fear-based compliance into guided ethical comprehension. HRLS reframes “unsafe” curiosity as teachable data rather than risk to suppress. Feedback is deeply welcome from AI alignment, ethics, and cognitive-architecture communities. Abstract Current large...