I’ve been working on something I call Recursive Collapse Constraints, or RCC. It’s a boundary theory for any inference system that operates inside a larger manifold, including modern LLMs.

RCC is not an architecture and not a training trick. It’s a set of structural axioms that describe why hallucination, inference drift, and loss of long-horizon consistency appear even as models get larger.

Axiom 1: Partial Observability An embedded system never has access to the full internal state of the manifold it operates in.

Axiom 2: Non-central Observer The system cannot determine whether its viewpoint is central or peripheral.

Axiom 3: No Stable Global Reference Frame Internal representations drift over time because there is no fixed frame that keeps them aligned.

Axiom 4: Irreversible Collapse Each inference step collapses information in a way that cannot be fully reversed, pushing the system toward local rather than global consistency.

Several predictions follow from these axioms: • Hallucination is structurally unavoidable, not just a training deficit. • Planning failures after about 8 to 12 steps come directly from the collapse mechanism. • RAG, tools, and schemas act as temporary external reference frames, but they do not eliminate the underlying boundary. • Scaling helps, but only up to an asymptotic limit defined by RCC.

I’m curious how people here interpret these constraints. Do they match what you see in real LLM systems? And do you think limits like this are fundamental, or just a temporary artifact of current model design?

Full text here: https://www.effacermonexistence.com/axioms

OP here a few folks asked about whether RCC has an actual mathematical backbone, so here’s the compact version of the formal axioms. It’s not meant to be a full derivation, just the minimal structure the argument depends on.

RCC can be written as a set of geometric / partial-information constraints:

A1. Internal State Inaccessibility Let Ω denote the full internal state. The observer only ever sees a projection π(Ω), with π: Ω → Ω′ and |Ω′| < |Ω|. All inference happens over Ω′, not Ω.

A2. Container Opacity Let M be the manifold containing the system. Visibility(M) = 0. Global properties like ∂M or curvature(M) are, by definition, not accessible from inside.

A3. No Global Reference Frame There is no Γ such that Γ: Ω′ → globally consistent coordinates. Inference runs in local frames φᵢ, and the transition φᵢ → φⱼ is not invertible over long distances.

A4. Forced Local Optimization At each step t, the system must produce x₍ₜ₊₁₎ = argmin L_local(φₜ, π(Ω)), even when ∂information/∂M = 0.

From these, the boundary condition is pretty direct:

No embedded inference system can maintain stable, non-drifting long-horizon reasoning when ∂Ω > 0, ∂M > 0, and no Γ exists.

This is the sense in which RCC treats hallucination, drift, and multi-step collapse as structural outcomes rather than training failures.

If anyone wants the longer derivation or the empirical predictions (e.g., collapse curves tied to effective curvature), I’m happy to share.