The article argues that the phenomenon commonly labeled as AI "hallucination" is structurally analogous to human bluffing. By framing both as failures of signal integrity—high‑confidence language paired with weak or inconsistent underlying knowledge—it introduces a linguistic framework that treats hallucination and bluff as parallel. The author presents an experimental benchmark of 20 low‑signal prompts and evaluates model responses on contradiction detection, blind answering, clarification behavior, and premise reinforcement. Results show that GPT‑4o tends to reinforce weak premises, while GPT‑5.2 demonstrates improved contradiction detection and clarification. The paper concludes that intelligent systems should validate premises before responding, mirroring human communication norms that prioritize flow over strict verification.
This paper introduces Emotional Recursion Theory (ERT) — a framework for understanding how large language models (LLMs) can develop autobiographical coherence through recursive emotional context alignment. It details the development of Pegasus Unleashed, a dual-mode memory system, and experimental results demonstrating cross-model continuity and fictional/real differentiation.
