Hallucination, drift, and long-horizon reasoning failures are usually treated as
engineering bugs — issues that can be fixed with more scale, better RLHF, or new
architectures.
RCC (Recursive Collapse Constraints) takes a different position:
These failure modes may be structurally unavoidable for any embedded inference system
that cannot access:
1. its full internal state,
2. the manifold containing it,
3. a global reference frame of its own operation.
If those three conditions hold, then hallucination, inference drift, and
8–12-step planning collapse are not errors — they are geometric consequences of
incomplete visibility.
RCC is not a model or an alignment method.
It is a boundary theory describing the outer limit of what any inference system can
do under partial observability.
If this framing is wrong, the disagreement should identify which axiom fails.
I’m the author.
If anyone thinks the core claim is wrong, I’d love to know which axiom fails.
RCC doesn’t argue that current LLMs are flawed —
it argues that any embedded inference system, even a hypothetical future AGI,
inherits the same geometric limits if it cannot:
1. access its full internal state,
2. observe its containing manifold,
3. anchor to a global reference frame.
If someone can point to a real or theoretical system that violates the axioms
while still performing stable long-range inference,
that would immediately falsify RCC.
Happy to answer technical questions. The entire point is to make this falsifiable.
The ideas sound intriguing at first sight, so I have some questions:
1 Do you have a mathematical formalization and/or why not (yet)?
2 What would be necessary as data/proof to indicate/show either result?
3 Did you try to apply probabilistic programming (theories) to your theory?
4 There is probablistic concolic execution and probablistic formal verification. How do these relate to your theory?
Hallucination, drift, and long-horizon reasoning failures are usually treated as engineering bugs — issues that can be fixed with more scale, better RLHF, or new architectures.
RCC (Recursive Collapse Constraints) takes a different position:
These failure modes may be structurally unavoidable for any embedded inference system that cannot access: 1. its full internal state, 2. the manifold containing it, 3. a global reference frame of its own operation.
If those three conditions hold, then hallucination, inference drift, and 8–12-step planning collapse are not errors — they are geometric consequences of incomplete visibility.
RCC is not a model or an alignment method. It is a boundary theory describing the outer limit of what any inference system can do under partial observability.
If this framing is wrong, the disagreement should identify which axiom fails.
Full explanation here: https://www.effacermonexistence.com/rcc-hn-1
I’m the author. If anyone thinks the core claim is wrong, I’d love to know which axiom fails.
RCC doesn’t argue that current LLMs are flawed — it argues that any embedded inference system, even a hypothetical future AGI, inherits the same geometric limits if it cannot: 1. access its full internal state, 2. observe its containing manifold, 3. anchor to a global reference frame.
If someone can point to a real or theoretical system that violates the axioms while still performing stable long-range inference, that would immediately falsify RCC.
Happy to answer technical questions. The entire point is to make this falsifiable.
The ideas sound intriguing at first sight, so I have some questions: 1 Do you have a mathematical formalization and/or why not (yet)? 2 What would be necessary as data/proof to indicate/show either result? 3 Did you try to apply probabilistic programming (theories) to your theory? 4 There is probablistic concolic execution and probablistic formal verification. How do these relate to your theory?