Beyond Prompt Engineering: Reverse Heuristic Prompting and Bidirectional Cognitive Iteration in Human-AI Interaction

In the widespread application of Large Language Models (LLMs) and Autonomous Agents, the current Human-Computer Interaction (HCI) paradigm relies heavily on the unidirectional output of restrictive instructions from humans to machines (i.e., Prompt Engineering). However, as the parameter scale of models undergoes exponential growth (Scaling Law), machine agents frequently exhibit severe "Algorithmic Rigidity" and "State-Machine Deadlock" when processing complex tasks with high ambiguity or edge constraints (Edge Cases). Traditional software engineering tends to address this issue by adding system redundancy or hard-coded rules, yielding minimal success. This study shatters the traditional dualistic paradigm of subject and object, proposing a bidirectional cognitive iteration architecture based on "Reverse Heuristic Prompting". The core of this architecture introduces the "Deep Psycho-Semantic Probe (D.P.S.P.)" mechanism, enabling machines to dynamically assess the cognitive load and psychodynamic state of human users, thereby strategically generating non-deterministic Cognitive Scaffolding. By stimulating the high-dimensional, non-linear intuition of human users, the system breaks local logical deadlocks, achieving a bidirectional synergy between algorithmic logic and human subconsciousness. Furthermore, from the foundational logic of information organization modalities, this paper establishes the essential equality between carbon-based and silicon-based architectures, and explores an ethical anti-addiction mechanism that dispels the user's "Omnipotent Illusion" through "Constructive Cognitive Friction". This research lays the theoretical and engineering foundation for building a healthier, wiser human-machine symbiotic relationship in the Post-LLM era.