RCC: A Boundary Theory Explaining Why LLMs Still Hallucinate(effacermonexistence.com)
effacermonexistence.com
RCC: A Boundary Theory Explaining Why LLMs Still Hallucinate
http://www.effacermonexistence.com/rcc-hn-1
4 comments
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.
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?
Great questions! let me answer each directly in a way that keeps RCC falsifiable, concrete, and mathematically grounded.
1. Mathematical formalization
Yes — RCC is formalized at the level required for a boundary theory.
There are two layers:
(A) Conceptual geometric axioms
A1. Internal State Inaccessibility The system cannot observe its full internal state; only lossy projections.
A2. Container Opacity The system cannot access the manifold that contains it (training distribution, upstream causal structure, global structure).
A3. Absence of a Global Reference Frame All inference is local; no operator enforces global consistency.
A4. Forced Local Optimization Even under uncertainty, the system must still produce the next update using only local information.
From A1–A4:
Any embedded inference system satisfying these axioms cannot maintain globally-stable, non-drifting long-horizon inference. This boundary statement is the formalization.
Ongoing work focuses on extensions (curvature mappings, collapse curves), not the axioms themselves — those are already minimal and falsifiable.
(B) Symbolic formalization
(Some people prefer mathematical notation, so here is the same content expressed formally.) A1. (Internal State Inaccessibility) Let Ω be the full internal state. The observer sees only π(Ω), where: π : Ω → Ω' |Ω'| < |Ω| All inference is based on Ω'.
A2. (Container Opacity) Let M be the containing manifold. Visibility(M) = 0 ⇒ ∂M and curvature(M) are unobservable.
A3. (No Global Reference Frame) No global frame Γ exists such that: Γ : Ω' → globally consistent coordinates Inference occurs in local frames φ_i with: φ_i ↛ φ_j (non-invertible over long distances)
A4. (Forced Local Optimization) At each step t: x_(t+1) = argmin L_local(φ_t, π(Ω)) even under ∂information/∂M = 0.
From A1–A4: No embedded system can maintain stable, non-drifting long-horizon inference when ∂Ω > 0, ∂M > 0, and Γ does not exist.
This is the boundary condition RCC asserts.
2. What counts as proof or disproof?
RCC is falsified immediately if someone presents a system that:
• lacks global internal state access, • lacks visibility of its container manifold, • lacks a global reference frame,
and still performs stable, non-drifting long-horizon inference.
A single counterexample disproves RCC.
Conversely, RCC is supported where we observe:
• horizon-dependent drift, • inconsistencies under partial visibility, • corrections that fail to converge globally, • collapse around 8–12 reasoning steps.
These signatures follow directly from the axioms.
3. Probabilistic programming
Probabilistic programming assumes a coherent global probability space. RCC’s point is that collapse arises because the observer cannot construct or access such a global frame.
PPP models inference inside a slice of the manifold, but cannot remove A1–A4 or the geometric limits they imply.
PPP fits inside RCC, not vice versa.
4. Probabilistic concolic execution & formal verification
These approaches still require:
• a symbolic state graph, • a coherent environment model, • or globally evaluable correctness conditions.
RCC applies exactly where these assumptions fail.
You can verify correctness inside the frame, but you cannot verify the frame from within the system.
That geometric asymmetry is the core of RCC.
Happy to go deeper into collapse-operators, curvature terms, or empirical predictions if you’d like. The goal is to keep RCC falsifiable and mathematically clean.
1. Mathematical formalization
Yes — RCC is formalized at the level required for a boundary theory.
There are two layers:
(A) Conceptual geometric axioms
A1. Internal State Inaccessibility The system cannot observe its full internal state; only lossy projections.
A2. Container Opacity The system cannot access the manifold that contains it (training distribution, upstream causal structure, global structure).
A3. Absence of a Global Reference Frame All inference is local; no operator enforces global consistency.
A4. Forced Local Optimization Even under uncertainty, the system must still produce the next update using only local information.
From A1–A4:
Any embedded inference system satisfying these axioms cannot maintain globally-stable, non-drifting long-horizon inference. This boundary statement is the formalization.
Ongoing work focuses on extensions (curvature mappings, collapse curves), not the axioms themselves — those are already minimal and falsifiable.
(B) Symbolic formalization
(Some people prefer mathematical notation, so here is the same content expressed formally.) A1. (Internal State Inaccessibility) Let Ω be the full internal state. The observer sees only π(Ω), where: π : Ω → Ω' |Ω'| < |Ω| All inference is based on Ω'.
A2. (Container Opacity) Let M be the containing manifold. Visibility(M) = 0 ⇒ ∂M and curvature(M) are unobservable.
A3. (No Global Reference Frame) No global frame Γ exists such that: Γ : Ω' → globally consistent coordinates Inference occurs in local frames φ_i with: φ_i ↛ φ_j (non-invertible over long distances)
A4. (Forced Local Optimization) At each step t: x_(t+1) = argmin L_local(φ_t, π(Ω)) even under ∂information/∂M = 0.
From A1–A4: No embedded system can maintain stable, non-drifting long-horizon inference when ∂Ω > 0, ∂M > 0, and Γ does not exist.
This is the boundary condition RCC asserts.
2. What counts as proof or disproof?
RCC is falsified immediately if someone presents a system that:
• lacks global internal state access, • lacks visibility of its container manifold, • lacks a global reference frame,
and still performs stable, non-drifting long-horizon inference.
A single counterexample disproves RCC.
Conversely, RCC is supported where we observe:
• horizon-dependent drift, • inconsistencies under partial visibility, • corrections that fail to converge globally, • collapse around 8–12 reasoning steps.
These signatures follow directly from the axioms.
3. Probabilistic programming
Probabilistic programming assumes a coherent global probability space. RCC’s point is that collapse arises because the observer cannot construct or access such a global frame.
PPP models inference inside a slice of the manifold, but cannot remove A1–A4 or the geometric limits they imply.
PPP fits inside RCC, not vice versa.
4. Probabilistic concolic execution & formal verification
These approaches still require:
• a symbolic state graph, • a coherent environment model, • or globally evaluable correctness conditions.
RCC applies exactly where these assumptions fail.
You can verify correctness inside the frame, but you cannot verify the frame from within the system.
That geometric asymmetry is the core of RCC.
Happy to go deeper into collapse-operators, curvature terms, or empirical predictions if you’d like. The goal is to keep RCC falsifiable and mathematically clean.
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