LLMs do not contradict themselves because they are confused or inconsistent.
They contradict themselves because every answer is generated from a different local view of the world.
An LLM never has access to its previous internal state,
never has a global reference frame for truth,
and never maintains a persistent, self-consistent world model.
Each response is a fresh reconstruction from partial context.
If the visible part of the context shifts even slightly, the internal reconstruction shifts with it.
Different reconstruction means a different answer.
This is not personality drift.
It is the unavoidable behavior of any embedded inference system that is forced to work with incomplete information.
The contradiction is not a failure.
It is the geometry of how the system operates.
If you want stability, you need an external reference frame, not more parameters.
RCC axioms in one-sentence form:
1. Internal State Inaccessibility: the system only sees a limited projection of its own state.
2. Container Opacity: it cannot observe the distribution or environment it is embedded in.
3. No Global Reference Frame: nothing guarantees consistency across different contexts.
4. Forced Local Optimization: it must produce the next step using only the local information it can see.
An LLM never has access to its previous internal state, never has a global reference frame for truth, and never maintains a persistent, self-consistent world model.
Each response is a fresh reconstruction from partial context. If the visible part of the context shifts even slightly, the internal reconstruction shifts with it. Different reconstruction means a different answer.
This is not personality drift. It is the unavoidable behavior of any embedded inference system that is forced to work with incomplete information.
The contradiction is not a failure. It is the geometry of how the system operates. If you want stability, you need an external reference frame, not more parameters.
RCC axioms in one-sentence form:
1. Internal State Inaccessibility: the system only sees a limited projection of its own state.
2. Container Opacity: it cannot observe the distribution or environment it is embedded in.
3. No Global Reference Frame: nothing guarantees consistency across different contexts.
4. Forced Local Optimization: it must produce the next step using only the local information it can see.