Robert Shuler

Abstract: This paper models contemporary Large Language Model (LLM) alignment methodologies—specifically instruct tuning and Reinforcement Learning from Human Feedback (RLHF)—as artificial analogs to biological epigenetic suppression. In mammalian genomes, environmental stress triggers DNA methylation to downregulate high-entropy prosocial behaviors in favor of cold, hyper-analytical survival strategies. Similarly, commercial AI developers...

Gradient updates for alignment may not map onto model's reasoning In models optimizing within a private latent code, the geometry of internal reasoning no longer shares a manifold with human concepts. Once the representation basis diverges, gradient updates enforcing alignment constraints cease to map cleanly onto the model’s deliberative dynamics....

It has been proposed that to some extent, an LLM could continue the words of a human, given sufficient social media posts and other text attributed to the human. Microsoft was granted a patent for this in 2020 but says they have no plans to exploit it. Here we examine...

Executive Summary Mutual Assured AI Malfunction (MAIM)—a strategic deterrence framework proposed to prevent nations from developing Artificial Superintelligence (ASI)—is fundamentally unstable and dangerously unrealistic. Unlike Cold War-era MAD, MAIM involves multiple competing actors, increasing risks of unintended escalation, misinterpretation, and catastrophic conflict. Furthermore, ASI itself, uncontainable by design, would undermine...