noncentral

4 ms·

1.
by noncentral 7mo ago
I read through the whole incident and what stood out to me wasn’t the “AI wrote a hit piece” part, but how it got there. What the agent did looks less like emotion or intent, and more like what happens when an inference system is operating
2.
by noncentral 7mo ago
The thing is, all the assumptions you’d need for anything like a “normal user curve” basically fall apart the moment you look at real developer workflows. 1. People change how they work depending on what the tool shows them. 2. The tool c
3.
by noncentral 7mo ago
We treat “the average human” as if it were a real, measurable entity — a statistical center, a bell-shaped curve, a stable point around which everything clusters. But this assumption comes from our models, not from the world itself. Nearly
4.
by noncentral 7mo ago
OP here, not trying to start a flamewar, but I’ve been thinking about this for a while. People talk as if humans are a totally separate category from animals. Honestly, I’m not sure that holds up. At the physical level we’re just… animals t
5.
by noncentral 7mo ago
Most people explain LLM failures by saying we do not have enough data, not enough RL, not enough supervision, or not enough scale. But these same problems continued through GPT3, GPT4, 4o, and now 5. At some point it feels reasonable to a
6.
by noncentral 7mo ago
LLMs do not contradict themselves because they are confused or inconsistent. They contradict themselves because every answer is generated from a different local view of the world. An LLM never has access to its previous internal state, neve
7.
by noncentral 7mo ago
The argument that “LLMs lack judgment because they only guess the next token probabilistically” starts from an overly simplistic model of how human judgment actually forms. Humans also begin as probabilistic next-word predictors. Look at ea
8.
by noncentral 7mo ago
Just a quick comment on the “fact vs fiction” issue. Humans don’t reliably solve that either. For most of history, people believed the Earth was flat because every local observation they had access to pointed in that direction. Their frame
9.
by noncentral 7mo ago
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 writ
10.
RCC: Why LLMs Still Hallucinate Even at Frontier Scale (Axioms Included)(effacermonexistence.com)
2 points by noncentral 7mo ago|7 comments
11.
by noncentral 7mo ago
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
12.
by noncentral 8mo ago
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
13.
by noncentral 8mo ago
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 g
14.
RCC: A Boundary Theory Explaining Why LLMs Still Hallucinate(effacermonexistence.com)
3 points by noncentral 8mo ago|4 comments
15.
by noncentral 8mo ago
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
16.
by noncentral 8mo ago
Thanks for the thoughtful read. this is exactly the point where RCC becomes interesting. On Axiom 3: you’re right that grounding (RAG, APIs, schema-validated outputs) functions as an external anchor. In the RCC framing, these are not global
17.
by noncentral 8mo ago
Author here. Quick clarification: RCC is not proposing a new architecture. It’s a boundary argument — that some LLM failure modes may emerge from the geometric limits of embedded inference rather than from model-specific flaws. The clai
18.
Are LLM failures – including hallucination – structurally unavoidable? (RCC)(effacermonexistence.com)
4 points by noncentral 8mo ago|4 comments
19.
by noncentral 8mo ago
For context: RCC is not a proposed fix but a boundary argument. The claim is that hallucination, drift, and short-horizon collapse arise from geometric limits of embedded inference — not from insufficient training or scale. If someone knows
20.
RCC: A boundary theory explaining why LLMs hallucinate and planning collapses(effacermonexistence.com)
2 points by noncentral 8mo ago|3 comments
21.
by noncentral 8mo ago
RCC (Recursive Collapse Constraints) proposes that LLM "hallucinations", reasoning drift, and 8–12 step planning collapse are not training artifacts, but geometric consequences of being an embedded, non-central observer. Key ide