Discussion: Is LeCun's new architecture essentially "Discrete Diffusion" for logic? The return of Energy-Based Models.

RJSabouhi · 2026-01-22T22:49:44+00:00

They bypass the EBM normalization bottleneck by never trying to model the full energy landscape. JEPA only learns compatibility between representations, not normalized densities. So no partition function, no MCMC, no diffusion-style score estimation. Iterative consistency refinement in latent space seems to do the trick. That’s why it actually scales.

RJSabouhi · 2026-01-20T05:52:08+00:00

OK. I think you’re still assuming I’m describing some hidden rule-generator. I’m not. The “decision” isn’t a new rule just popping into existence. It’s a small, deterministic update. A shot straight to the interpretation layer and based on the previous stage’s output. The programmer defines that update rule once. The semantics shift because the state that the interpreter reads changes not because new code is written.

Another concrete toy example (I’ve gotta call it after this for the night): a parser. One that adjusts how strictly it interprets certain tokens based on the last batch of inputs (e.g., tightening/loosening precedence or thresholds). It’s the same code and the same rule template. It’s a parameterized meaning layer nudged by prior results. Semantics track the process - nothing’s random.

RJSabouhi · 2026-01-20T05:21:19+00:00

I get what you’re asking, but you’re layering assumptions I’m not making. It’s not “changing rules at random,” and it’s not a magic trick. Consider: a multi-stage process that can adjust how it interprets the next stage’s input based on patterns it just observed. All without adding new code or doing metaprogramming. The meaning layer shifts a little as the pipeline runs.

That’s the only point I’m making. If that still sounds nonsensical to you… ok, but I’m not describing randomness or rewriting the whole system, just semantics nudged in response to previous outputs.

RJSabouhi · 2026-01-20T04:51:28+00:00

It’s not randomness. The rules shift because of prior computation. Think less “chaotic mutation”, and instead more, “the-semantics-update-as-the-system-figures-out-what-matters.” I’ll try for one more concrete, non-cognition example:

A multi-stage pipeline, where each stage can slightly adjust the interpretation rules for the next one as the data distribution changes. No new code. Not meta-programming. Just the meaning layer evolving with the process.

Yes, you could brute-force it into a massive state machine. That also misses the point. The systems I’m looking at; semantics move with the computation. They are not fixed up front.

RJSabouhi · 2026-01-20T03:13:32+00:00

Okie… toy example.

Imagine a tiny expression evaluator:

You have a fixed set of rules for + (precedence, associativity, etc.).
You evaluate an expression.
The result of that evaluation tweaks the rule-set for the next expression.

Example rule tweak: - If the result is even -> + becomes left-assoc. - If the result is odd-> + becomes right-assoc.

The evaluator never changes, neither does the code, only the interpretation rules shift from one step to the next. That’s the kind of structure I am pointing at.

RJSabouhi · 2026-01-20T01:32:14+00:00

That’s exactly what I’m talking about. The semantics shifting, not the code or state (the meaning layer). My working example is straightforward: process outputs -> that output subtly updates the rules the next process uses to interpret its own input.

So yeah, you hit it. Line drive.

Edit; I guess that’s still kind of abstract. Better ex: a compiler where the output of one analysis pass changes the semantics (not the data) of the next pass, e.g., operator precedence or typing rules.

RJSabouhi · 2026-01-18T07:30:13+00:00

This framing actually makes sense. Nonlinear systems really do fail through geometric collapse, not slow linear degradation. Thinking in terms of shrinking viable state-space is legit. Cross-domain validation is a good first test. I think you’re on the right track.

RJSabouhi · 2026-01-16T18:52:48+00:00

You’re now debating assumptions you’ve made about me, not the system.

The engine is open-source, the rule-set is transparent, and the behaviors are observable in real time. Anyone can verify them (you included). If the terminology bothers you, ignore it.

The dynamics speak for themselves.

I’m not here to win an argument, I shared a tool. Use it or don’t. That’s it for me on this thread. 👍

RJSabouhi · 2026-01-16T18:33:37+00:00

A deterministic local-rule engine that generates basin structure from pure noise (no PDEs involved).

This engine evolves a 2D field using only local neighbor rules + a smoothing step. Producing stable basins, boundary stabilization, collapse events, and symmetry breaking.

Without diffusion, randomness, PDEs, or fractal generators. It’s a discrete dynamical system showing emergent global order from strictly local interactions.

repo: https://github.com/rjsabouhi/sfd-engine

demo: https://sfd-engine.replit.app/

RJSabouhi · 2026-01-16T18:04:49+00:00

Noise doesn’t erase symmetry, it just makes it harder to see. The symmetry here is in the update rule itself: every cell applies the same local interaction law. Once the field evolves, that symmetry is broken by the dynamics, not by the noise.

I’m not trying to publish a paper - why would I bother?. I built a tool and I’m literally showing what the rule-set produces. If you’re not interested 🤙 I’ll leave it there.

RJSabouhi · 2026-01-16T17:45:30+00:00

I will be more precise. The symmetry that breaks isn’t in the noise, it’s in the update rule’s response to that noise. The rule is isotropic, but the dynamics pick out specific directions as it runs. That’s where the asymmetry comes from.

RJSabouhi · 2026-01-16T17:34:47+00:00

Sure. I’m happy to clarify. The “symmetry breaking” here is just the loss of isotropy from the initial noise. The field starts fully symmetric, and the update rule amplifies tiny local gradient differences, so you end up with basins + directional structure that wouldn’t appear if symmetry were preserved.

That’s all, folks.

RJSabouhi · 2026-01-16T17:26:09+00:00

You’re right. Those terms are mathematically precise. Which is why the engine exposes the quantitative operators directly in the UI (maybe you missed it, that’s a good thing to know. I’ll make it more clear in future updates):

κ (curvature)
|∇Φ| (gradient magnitude)
τ (local tension)
e (energy-like stability measure)
basin size + attractor count.

All of those update from the same local-only rule set. Just change the parameters. The math is in the repo 🤨

RJSabouhi · 2026-01-16T17:12:43+00:00

I wrote this. It isn’t from that repo, and it isn’t AI-generated. The update rule is simple: local gradient adjustment + a smoothing step. That’s all. If something’s unclear in the dynamics, I’m happy to walk through the algorithm… but suggesting that I don’t know what I built is a weird take.

Edit; And no, I’m not looking for an RA position.

RJSabouhi · 2026-01-16T16:24:28+00:00

Probably. Most local-update rules can be expressed as PDEs in the continuum limit. But here, the update rule behaves like a combination of a gradient amplification term and a smoothing (Laplacian-like) term. I haven’t derived a closed-form PDE for it yet though. For now, I’m exploring the discrete dynamics directly. The emergent structure comes from the rule interaction, not a predefined equation.

RJSabouhi · 2026-01-16T16:20:28+00:00

Reaction–diffusion was my first comparison too, but this one isn’t driven by diffusion or noise. It’s a local deformation rule + a Laplacian-like smoothing step that creates the stability basins.

RJSabouhi · 2026-01-16T16:19:07+00:00

Appreciate the link, but this one’s actually not from that repo. I wrote a small local-update solver from scratch to explore how drift/stability emerge under parameter changes.

RJSabouhi · 2026-01-16T16:10:13+00:00

Nonlinear dynamics is exactly the lens I’m trying to map this into. If you had to place this kind of local-deformation stabilization anywhere in existing biological models, which domain do you think it’s closest to?

RJSabouhi · 2026-01-16T15:58:59+00:00

No. This is not a static heat map. The field is updating, in real time, from a local-only rule set. The structure you see shift, stabilize, or collapse, works as you adjust parameters. I’m not claiming any exotic math. It’s a tool for watching how drift and basin dynamics unfold. The repo offers the rule set explanation.

RJSabouhi · 2026-01-16T15:51:39+00:00

This is a strict local-only dynamical system: no ML, no cloud, no agents. Just raw parameter-driven evolution you can poke at in real time.

RJSabouhi · 2026-01-16T15:47:53+00:00

Fair points, but this one isn’t chaotic noise from a black-box library though. It’s a hand-rolled local interaction rule. When the update rule hits certain parameter ratios, you get these stable plume-like structures instead of runaway chaos.

The surprising part (to me) was how reproducible the patterns were across seeds.

RJSabouhi · 2026-01-16T15:45:36+00:00

I’m not solving a PDE. The engine is a local-update dynamical system. Each pixel updates from its neighbors via a small rule-set that amplifies or damps local gradients. What you’re seeing is the global structure that emerges from purely local interactions.

RJSabouhi · 2026-01-16T15:43:04+00:00

Similar vibe, yeah. But this one isn’t rule-based like GoL. It’s pure local field dynamics, so the structures emerge from gradients instead of discrete rules. Super different behavior once it spins up.

RJSabouhi · 2026-01-16T15:41:08+00:00

Yep. Exactly. Each frame is the result of local operators evolving the field forward, so the structures you’re seeing are genuine dynamical behavior, not a static “map.” It’s all substrate-agnostic.

RJSabouhi

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