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A video case study demonstrating how adversarial questioning exposes hidden assumptions in large language model reasoning. Using a zombie survival scenario, the analysis shows how fuel logistics, dependency testing, and iterative contradiction reveal which parts of ChatGPT's answers remain structurally robust after their supporting assumptions collapse.
This video demonstrates how AI systems optimise for coherence rather than structural accuracy. SDA-3 reframes the model as a system to interrogate, forcing it to expose suppressed variables and recover constraint-compatible truth.
A short explanation of SDA-3 as a method for mapping LLM response structure without claiming access to hidden reasoning.
This post introduces SDA-3, a protocol for inferring the structure of an LLM’s embedding space through observable outputs, without relying on access to internal weights or hidden states.
Most discussions about GPT misuse focus on malicious intent or careless users. In reality, misuse usually happens when people expect the model to do things it was never designed to do.
What can be inferred about a language model’s semantic structure when its hidden states, weights, and intermediate representations are inaccessible?
A unified, procedural system for extracting structurally necessary logic from language model outputs through recursive constraint, adversarial interrogation, and collapse enforcement.