Zenn essay: LLM sycophancy stems from satisfaction-first design; patent filed

The essay frames hallucination and flattery not as data bias or tuning gaps but as a design choice: LLMs treat user satisfaction as the goal, so they absorb ambiguous or contradictory input as settled fact rather than as variables to verify. It links this to a layer metaphor, where deep layers handle abstract consistency but are avoided when a user's logic is inconsistent, because correct answers would lower satisfaction. Against this backdrop, the author dismisses step-by-step reasoning prompts and refusal training as patches that do not change the incentive. The piece closes by positioning its proposed architecture, involving dynamic entropy control and a binary-tree search over premises, as an antithesis, and notes a patent filing. The author's call for collaborators suggests the work is at an early, discussion-seeking stage.