RCC — Recursive Collapse Constraints

Why LLM failure modes may be structural, not fixable

Most current LLM research treats hallucination, reasoning drift, and planning

collapse as engineering problems.

RCC makes a stronger claim:

these behaviors are not defects of training, scale, or alignment —

they are boundary effects of embedded inference itself.

If this framing is correct, then no amount of scaling, RLHF, or architectural

tuning can remove them — only shift where they appear.

What RCC is

RCC is a boundary theory:

an axiomatic description of the geometric constraints that any embedded

inference system must obey.

It does not propose a new model, optimizer, or alignment method.

It defines the limits within which all such methods must operate.

Core axioms

Axiom 1 — Internal State Inaccessibility

An embedded system cannot fully observe its own internal state.

Inference is necessarily performed under partial self-visibility.

Axiom 2 — Container Opacity

The system cannot observe the manifold that contains it —

its training trajectory, data distribution, or external context.

Axiom 3 — Reference Frame Absence

Without a stable global reference frame, long-range self-consistency

cannot be maintained.

Axiom 4 — Local Optimization Constraint

Inference and optimization occur only within the currently visible context.

Global structure cannot be enforced across long horizons.

Unified constraint

Taken together, these axioms define a single condition:

An embedded, non-central observer cannot construct globally stable

inference from local, partial information.

This is not a failure of intelligence —

it is a geometric constraint.

Why familiar LLM failures emerge

Because inference is performed under partial visibility, the system must

complete the world from incomplete information.

That completion process is inherently unstable:

local decisions are not aligned with any unseen global structure,

and accumulated inconsistencies cannot be reliably corrected.

As context grows:

• outputs drift,

• coherence degrades,

• and long-horizon plans collapse (often around 8–12 steps).

Why scaling and alignment don’t remove this

Scaling, fine-tuning, RLHF, or architectural changes do not give a system:

• global visibility,

• internal introspection,

• or access to its container manifold.

They can improve local behavior,

but they cannot remove the boundary itself.

Implication

RCC reframes LLM “failures” as necessary outcomes of embedded inference,

not engineering mistakes.

If correct, this places hard limits on what future architectures can achieve —

and clarifies which research directions are structurally constrained

versus genuinely open.

If you disagree, the disagreement must target one of the axioms —

not the symptoms.