RCC — Recursive Collapse Constraints

Why LLM failures persist — even at trillion-parameter scale

A boundary theory argument that hallucination, drift, and planning collapse may be structurally unavoidable.

Most discussions treat hallucination, reasoning drift, and long-horizon collapse as engineering problems.

RCC (Recursive Collapse Constraints) makes a stronger claim:

These behaviors may not be fixable —

they may be geometric side-effects of embedded inference itself.

If this framing is correct, improvements in scaling, RLHF, or architectural tuning can shift where failures appear, but cannot eliminate them.

What RCC is

RCC is a boundary theory:

• not a new architecture

• not a training method

• not an alignment strategy

It is an axiomatic description of the structural limits that any embedded inference system must obey.

Any system that fits the axioms inherits the same constraints, regardless of model size or implementation.

The Four RCC Axioms

Axiom 1 — Internal State Inaccessibility

An embedded system cannot see its full internal state.

Inference is performed through a lossy, partial self-projection.

Axiom 2 — Container Opacity

The system cannot observe the manifold that contains it

(training distribution, trajectory, upstream context, etc.).

Axiom 3 — Absence of a Global Reference Frame

All inference is local to the currently visible context.

Long-range consistency cannot be guaranteed.

Axiom 4 — Forced Local Optimization

Even under uncertainty, the system must produce the next update

using only local information.

Unified Constraint

Putting the axioms together:

An embedded, non-central observer cannot construct globally stable inference from partial information.

This is not a deficit of intelligence.

It is a geometric limitation.

Why familiar LLM failures emerge

Under partial visibility, the system must complete unseen parts of the world.

That completion process is:

• underdetermined

• unstable over long ranges

• inconsistent with any unseen global structure

As context grows:

• outputs drift

• internal coherence degrades

• 8–12-step reasoning collapses

• corrections fail to restore global stability

These are not bugs — they are consequences of inference under incomplete information.

Why scaling and alignment don’t remove this

Scaling, fine-tuning, or RLHF do not give a model:

• global visibility

• perfect introspection

• access to its container manifold

These methods can improve local behavior,

but they cannot remove the underlying geometric boundary.

Implications

If RCC is correct:

• hallucination cannot be eliminated, only relocated

• drift cannot be removed, only dampened

• chain-of-thought collapse cannot exceed the boundary

• self-consistency cannot be globally guaranteed

This reframes “LLM failure modes” as structurally necessary outcomes of embedded inference.

It also suggests that some research directions may be fundamentally constrained, while others remain open.

If you disagree

Disagreement should identify which axiom is incorrect —

not just critique the symptoms observed in current models.