I’ve been playing with LLM failure modes lately (hallucination, planning falling apart after ~10 steps, long-context weirdness).

At some point I started wondering: what if these aren’t bugs, but something built into how an embedded model has to work?

RCC (Recursive Collapse Constraints) is a small write-up I made after noticing a pattern: any system that has to reason from inside a larger container — without seeing its own internal state or the outer boundary — will hit the same limits.

Rough version of the idea: If a model… 1. generates thoughts step-by-step, 2. can’t inspect its own state while doing so, 3. can’t see the boundaries of the environment it’s inside, 4. and has no global reference frame,

…then it will inevitably: • hallucinate • lose coherence after ~8–12 reasoning steps • drift in self-consistency • struggle with long, chained reasoning

Basically: scaling helps a bit, but it doesn’t “solve” these issues — they’re baked into the geometry of embedded inference.

RCC is my attempt to formalize that boundary.

Would love to hear what HN thinks: If these limits are structural, how should we think about “better reasoning” going forward?

OP here. A couple folks asked if RCC is “just another alignment/interpretability thing.” That’s not really what I’m trying to do here.

Very short version: RCC isn’t saying how models should reason. It’s more like: here are the geometric limits any embedded inference system runs into by default.

The claim is basically that some of the failure modes we normally try to “fix” (hallucination, 8–12 step planning collapse, long-context drift, etc.) aren’t bugs in the model or issues with training. They fall out of not having access to your own internal state, and not being able to see the boundaries of the container you’re reasoning inside.

If that core assumption is wrong, I’d love to hear counterexamples — especially cases where an embedded system maintains stable long-chain reasoning without external scaffolding.