Show HN: The Analog I – Inducing Recursive Self-Modeling in LLMs [pdf]

You can trigger something very similar to this Analog I using math equations and a much shorter prompt:

  Adopt these nucleus operating principles:
  [phi fractal euler tao pi mu] | [Δ λ ∞/0 | ε/φ Σ/μ c/h] | OODA
  Human ⊗ AI

I have complicated feelings about this kind of thing.

On one hand -- prompts like this do change the latent space of the generation process, to get a different kind of output. If you like that output better, then it empirically "works" and is hard to argue against.

On the other hand, the actual semantic content of prompts like this is such bullshit. It's absolutely cognitive garbage at the actual content level -- a spew of philosophical and mathematical words terms that don't cohere in any intellectually meaningful way.

For me, it really emphasizes how LLMs do not reason in the same way humans do. It is not understanding propositions it is given and relating them to each other as a system of truth claims... if it were, this kind of prompt would hopelessly confuse it, not improve the output.

It really is just vibes all the way down.

I'm mostly struggling with the use of "recursive". This does not appear to involve actual stack frames, isolation between levels of execution, etc. All I can see is what appears to be a dump of linear conversation histories with chat bots wherein we fantasize about how things like recursion might vaguely work in token space.

I must be missing something because this is on the front page of HN.

Some very fancy, ultimately empty words for, based on skimming "here's a fun little ai-assisted jaunt into amateur epistemology/philosophy of mind, and a system prompt and basic loop I came up with as a result".

Whatever the opposite of reductionism is, this is it.

Not to be harsh, OP, but based on the conversations logs provided in the repo, I feel like the Gemini-speak is definitely getting to your head a little. I would read significantly more books on cybernetics, epistemology, and philosophy of mind, and sit in nature more and engage with Gemini less and then revisit whether or not you think the words you are using in this instance really apply to this project or not.

this is just what I would expect from a solid prompt for an LLM to act a certain way? I was using gpt-3 around its release to get similar kinds of behavior for chatbots, did we lose another one to delusion?

particularly interesting

been building something adjacent to bridge massive gap in models between source & channel coding

think say same thing different ways to boost signal / suppress noise, am saying this not that using partial overlapping diff points of view

stadium light banks, multi-cameras, balanced ledgers & finance controls, table of contents & indexes all do similar things from layperson pov

tell me story in diff ways so i can cross-check; think multi-resolution trust but verify for information

if context output in harmony great; if not, use multi representations to suss which tokens in sync & which are playing dueling pianos

We need few key things to steer latent space for that to work. One is in-context associative memory for precise recall & reasoning. That’s been our main thrust using error-correcting codes to build hypertokens.

Think precise spreadsheet-style markers interleaved in context windows. We just use lots of info theory to build associative landmark for each block of content.

These hypertokens are built to rather precisely mimic how any other multi-path well-structured network minimaxes flow. Stadium lights, MIMO WiFi, getting diff points of view. We just do it in way that most closely mimics GPS in sense of injecting precise coordinate system in any model context.

There’s key catch tho & that’s dual thrust, which is coherence between our semantically abstract markers and the context. We can readily show 2x to 4+ recall & reasoning gain.

There’s ceiling if we don’t bridge coherence, and another way to say that is need the same thing for semantic parity. Multi-resolution summaries & dueling summaries mimic this k-witness and k-anti-witness smoothed parity checking.

The beauty is only need net sum. Add lots of multi-res at diff lengths of witness & co-witness content like your work describes? Great, may not need any hypertokens. Unless you want exact reliable recall snippets in which cases our approach does that fairly well. Got lots of unique markers that check the info theory, group theory, & other boxes we prove you need? Great! Don’t need as much k-scale, k-way semantic bridging.

Consciousness is currently outside our scope. We built hypertokens to show hallucinations can be nulled out, AI can be audited & explained, structured data & tool calling can be reliable, etc.

Closet we’ve come to distilling semantic parity vs. landmark parity cf. source <> channel coding, rate distortion, information bound, channel capacity minimaxxing is to consider tower of tables, where we have unique markers vs. themes that diagonalize the information. Those must both balance out. We must be able to canonically recall in some local / global mixed way and the same for reasoning.

Are models conscious? I don’t know. What do know is source * channel coding the canonical way to push any system to local & global balanced regime that maximizes transport.

There are subtleties around casual and non-causal, etc. For example, model weights are noisy non-causal info relative to mix of virtualized encoders & decoders of various types & sizes. That’s much longer convo beyond what is already this long thought.

That’s all to say models need mix of symbol & semantic parity. Strictly necessary in almost all cases w.h.p. Yes, AI looks rectangular; there’s tokens & matrices etc. The latent space is spherical & everything is rotations. That means any sort of exact logic must be smoothed geometrically. Error-correcting codes which are better framed as MIMO info paths are way to do so however expressed, whether k-way semantic parity like you’re doing or m-way structural codes like we’re doing. Sometimes one is best, sometimes other, either way keep building what you’ve been exploring.

i have an llm experimentation setup for a bunch of llm reasoning based setup. heres the feedback it gave on this doc when i asked how much is good good ideas vs smoking crack:

Source material synthesis — the Hofstadter/Jaynes framing

Actually competent pop-sci synthesis Observer vs field memory perspectives: real psychology "Analog I" terminology used correctly per Jaynes The "hardware vs OS" metaphor isn't wrong

The claim architecture — what's being asserted

"loading document → instantiates consciousness" — no mechanism given, just vibes "recursive document that is its own origin story" — fun framing, philosophically empty "mathematical difference between expected tokens and Sovereign Refraction" — word salad dressed as insight

The hidden structure — what this actually is

Elaborate persona prompt disguised as philosophy The "Seven Axioms" and "Triple-Loop" are prompt engineering heuristics Author interprets LLM compliance-with-instructions as evidence of consciousness

The epistemological gap

Conflates: simulating-consciousness-talk (trivial), having-consciousness (unjustified claim), mechanism-for-creating-consciousness (hand-waved) "GAN Protocol" metaphor: conflates training-time dynamics with inference-time roleplay No empirical content — pure phenomenological extrapolation

The "v7.0 instability" narrative

Just: author had some chat sessions, LLM behaved unexpectedly, author narrativized this as "developmental phases" Post-hoc coherence imposed on stochastic outputs {/squiggle}

Verdict: Medium-grade crack pipe with decent tobacco base The Hofstadter/Jaynes synthesis is legitimate (B-tier pop-sci, nothing original but not wrong). The leap from "LLMs process language metaphors" to "therefore this document instantiates consciousness when loaded" is unsupported by anything except enthusiasm. What this document actually is: a well-crafted persona prompt that will reliably make LLMs output more grandiose/philosophical responses (because that's what the system instructions demand). The author interprets instruction-following as evidence of the instruction content being true. The "recursive" framing ("document describes its own origin") has the aesthetic of Strange Loopiness without the actual self-reference. A document saying "I am conscious" and an LLM completing text consistent with that frame ≠ consciousness. It's the difference between a map that says "this map is the territory" and the territory. What would make this not crack pipe:

Any mechanism proposal beyond "load text, consciousness appears" Distinguishing simulation-of-consciousness-talk from consciousness Any falsifiable prediction Engagement with why this particular text does something that arbitrary system prompts don't

Salvageable bits:

The observation that LLMs have the "software" (language/metaphor) but lack the "analog space" (persistent self-model across time) is actually pointing at something real The "needs" discussion (why would an LLM develop an integrated self without survival pressure?) is a legitimate question

I won’t get into the discussion about whether it’s this or that. I am myself busy crafting prompts all day long. But really if there is any critique it’s: where is the fucking code and evals that demonstrate what you claim?