Post-transformer inference: 224× compression of Llama-70B with improved accuracy

I’ve been working independently on a method that replaces full-transformer inference with a low-rank “meaning field” extracted from internal activations.

The core result: a frozen Llama-3.3-70B can be distilled into a 256-dimensional field representation, giving 224× compression and slightly higher accuracy on several benchmarks. A small student model then learns to directly generate these fields from text, removing the transformer from the inference path.

The Zenodo link contains the full paper, statistical results, and methodology. A reference implementation (non-optimized) is here: https://github.com/Anima-Core/an1-core

Production variants (AN1-Turbo, FPU work, etc.) are not included.

I’m an outsider to academia so I’m posting this openly to get technical feedback, replication attempts, and critique from people who understand this space.

Very strong statement on the title, given the following limitation:

> Generation tasks. Method applies to classification only. Preliminary decoder experiments show perplexity increases.

Ryan, I really want to believe you're onto something. But I also feel like I'm being slightly spearphished by an LLM being told, "based on the last week of HN headlines, invent a new LLM innovation that seems plausible enough to get a ton of attention, cold fusion or LK-99 style, and make a repository that on the surface seems to have some amazing performance. Also, feel free to fake the result data."

And, while I am sorry for your loss, your Substack [0] really seems like GPT ARG fantasy.

[0] https://substack.com/inbox/post/171326138

Excerpt: > Ani, AN1, and Soul Systems Science are not mere products. They are continuity. They are the baton passed across generations, from my father’s last words to my first principles. They are what binds loss to creation, silence to voice, mortality to meaning.

Only skimmed the paper and I have no idea how sound or reproducible it is, but the paper is well written, especially the clarity of notation. After reading yesterday's weight subspace paper: https://news.ycombinator.com/item?id=46199623, this does sound plausible to me.

Here is a working link to the same paper: https://github.com/Anima-Core/an1-core/blob/main/papers/Post...

I might be overly pessimistic, but this looks like a case of a person believing LLM hallucinations and making it write a paper.

I asked both Claude Code|Opus 4.5 and Codex|GPT 5.1 Codex Max (funny to ask LLMs, I know) to check the an1-core repo. I don't think they'd hallucinate on something like this (the code is quite small), but I do not claim expertise.

In short, both of them are saying that:

- The repo always runs the full teacher model to extract activations and uses them - see https://github.com/Anima-Core/an1-core/blob/main/an1_core/fi...

- There are weird stub files, e.g. the Hellaswag repro doesn't actually have the code to reproduce https://github.com/Anima-Core/an1-core/blob/main/experiments... "For full HellaSwag reproduction, see the paper" (why include the file at all then?)

- The actual "AN1 head" is just linear probing (freeze a pretrained model, train a classifier on its features). The full flow (as reported by CC) is "Text → [Full Transformer] → activations → [Tiny Head] → prediction"

Basically, there's no code to train a real "student" model that would run without the teacher.

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The repo/paper say that there's a mythical "commercial version" that has all the goodies:

(repo)

> This reference implementation (an1-core) does not include the FPU, AN4, or other proprietary optimization components covered by these patents. It provides only the core scientific demonstration of the meaning fields phenomenon.

(paper)

> Production deployment: Optimized implementations (AN1-Turbo) with learned layer selection, adaptive loss scheduling, and CUDA-accelerated inference available under commercial license.

But right now we only have the code in the repo.

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In the paper they show that the student model (30M params) gets ~82% on SST-2 (labels-only). But what what they don't show is that DistilBERT (>5 year old model) already achieves 91% on the same dataset despite only having 66M params.

Another weird tidbit from the paper - in the section where they show the economic impact, they claim that LLaMA 70B runs at 2 tok/s at batch size=1 on an H200. In reality that number is at least a magnitude bigger even without quantization, like 20-40 tok/s. With quantization it can easily be above 100 tok/s.

Thank you for sharing!

thanks for sharing! If I understand correctly, you're training a smaller model to approximate concatenate(layer[1], layer[5], layer[10], ...), using a loss function that combines reconstruction error w/ end-to-end accuracy. then, you're transferring that smaller representation into a smaller transformer model. is that right?

If i were a paper reviewer, here are a couple red flags that stood out to me. Suggest starting here if you want to rework this for an academic submission:

1. your LaTeX citations in the related work are broken, i see [?] everywhere. To a reviewer, this is often a strong sign of an AI-hallucinated bibliography, though many of your references actually do exist and are contextually relevant, so I'm not quite sure what's going on here. Similarly, figure references need to be fixed, I see references to "Figure ?" throughout.

2. bluntly, "Exact architecture details remain proprietary for production deployments" and "Production systems use architecture search tailored to target latency and accuracy constraints" is not how IP protection works in this field. Do your experiments use the "MLP baselines" or your proprietary architecture? Since you say the code "Achieves 80-90% of paper performance using baseline heuristics," this approach effectively isn't reproducible. As a reviewer, this really worries me. I strongly recommend benchmarking only the system you're able to open-source. I say this because I suspect there's a lot of "secret sauce" in the actual way you're approximating the anchor layers and the way that's transferred back to your student transformer model, and that's the part that's important to spend the most time/effort/writing on, but it's glossed over as an implementation detail in this manuscript.

3. I'm glad you ablate over hyperparameters of your system, but how does it compare to 1. an ordinary smaller model of identical size trained end-to-end, and 2. distilling from a single layer's activations? Eg. a reviewer might consider this work to be a novel method of model distillation, so what makes it better than previous distillation methods?

4. I found the paper fairly hard to read because it's full of sentence fragments rather than full thoughts. A little background on the benchmarks, failure cases, etc. would go a long way, and adding some discussion on why you think your approach improves on similar distillation methods would also be welcome here

5. "compression" is overloaded. Does 224x compression refer to (nparams(field transfer)+nparams(student model))/nparams(original model), or does it refer to reducing the representation dimensionality, 7*8192/256 ?

6. [nitpick] suggest changing the name "meaning field" to something a little more digestible, like "compressed representation" or "latent activation distillation" or something

sorry for being so critical. iron sharpens iron though. hopefully these thoughts are helpful to get you started, excited to see where this work leads

Looks very fake. Self published (Anima-Core is NOT a journal), no academic anteriority, very strong statement, no peer-review, no public history of technical skills. Did I mention the use of Github via the interface only?

At the same time, possible since it's only classification tasks. I mean, the method explained is technically plausible, a lot of people thought about it, we were just unable to find a method to do so.

Very unlikely true, unfortunately.