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A TLDR, since most comments here seem to misunderstand the result.

The idea is: a model may only have 100M neurons or whatever. But there are infinite concepts in the world. Therefore, models have to "stuff" multiple concepts per neuron.

How do you do that? Theres two ways:

1. Each neuron handles similar concepts. One neuron for dogs, a separate neuron one for cars, etc.

2. Each neuron handles a bundle of unrelated concepts. One neuron handles dogs and cars. Another neuron handles cats and numbers. And so on.

Historically, people thought #2 was correct because many neurons were found that seemingly represented multiple concepts. #2 had a cool name (polysemanticity) and was promoted in OpenAI/Anthropic blogs so it had a lot of traction.

However, this blog says #2 is wrong. Actually, the dog/car neuron is just a "V shape neuron". So #2 was just methodological error, and #1 was right all along. This is all the blog is saying.

Claude code is 60-80 tps, codex varies by model but mostly 40-50 tps.

Claude Code/Codex charge the user for their extremely bloated one-size-fits-all system prompts (including safety instructions and other stuff users dont want).

In my experience if you're using OpenAI/Claude models and paying API costs, almost every other harness beats Claude Code/Codex in cost.

MIRI has one: https://intelligence.org/2026/05/12/summary-an-international...

The proof is that LLMs could barely solve arithmetic 3 years ago, but now surpass the best human mathematicians, and that this has all occurred from simple principles (RL + compute) that will continue to scale up by factors of millions in the coming years.

Also, advocating for slowing LLM progress does not benefit Anthropic or OpenAI.

This is increasingly the consensus I see also on the academic side of AI/safety research. Specifically that AI poses an existential risk to humanity.

This was a fringe belief until recently, but the progress of AI in research is impossible to ignore. Epecially in math, where not only has AI outstripped humans in generative ability, but is able to create scientific knowledge which is beyond the capacity of human comprehension.

There's clearly no intelligence task that AIs can't do due to some magic fundamental constraint. And it's hard to imagine a world where current limitations like poor sample efficiency or lack of continual learning won't eventually be solved.

Total AI compute is estimated to grow somewhere in the 1-10 million-fold range in the next decade. Please don't underestimate the phase change that's still coming.

Sure, maybe there's some plateau due to RL being fundamentally limited in some surprising way, but this is nothing but a hope.

Given how many private benchmarks they're using now, its likely they just tested different combos until they got the result they wanted.

Completely discredits the index if it just gets modified to match social media vibes.

Hmm, 61 on ArtificialAnalysis, effectively matching GPT-5.6 and trailing the new Meta model. How is that possible along with the other metrics they shared? Insanely jagged intelligence?

I believe its the same as free models in general on Openrouter, 1k requests per day for accounts that have some spend history.

The provider decrypts it and puts the decrypted reasoning into the model's context window. They prompt the model to repeat back the reasoning. So then the model echoes it back in plain text.

Super cool that this works. I'm surprised these companies re-use the same encryption key across models!

I wonder if you can use these for attacks, like this previous paper showing that if you know how a model reasons, you can "fake its thinking" to control it? https://news.ycombinator.com/item?id=48631888

Have you tried Novita? They're zdr and I find they usually have better cache hit rates than fireworks. No affiliation.

It's been known for several years that LLM activations encode future tokens ahead of time (e.g. https://arxiv.org/abs/2404.00859).

But this has only been shown on simple tasks, so I think this paper is still quite neat. The interesting thing is that they show "future horizon length" varies across models.

Considering the job description is unrelated to the company and the title, this is a scam post, not quite a "ghost job".

They're probably rapidly opening + closing new jobs to increase visibility, as matching models on job boards tend to prioritize new posts.

Given their pricing, I'd guess their models are just way bigger in parameter count. They've always underperformed in cost-per-performance.

They also target a cost-insensitive market (corporate/coding users) compared to Google/OpenAI which support massive amounts of free users.

Love your work on this, thanks for bringing the ggplot syntax to Python!

Yeah, the footnote/sidenote on the paper (the one labeled #2) mentions this as well so you can't type that directly

I believe they are trained for security now, but you're not wrong in that it's kind of stapled on top

https://arxiv.org/abs/2404.13208

A lot of open weight models don't understand intent well, they'll overfixate on a word in the prompt or just go off the rails trying to do much work.

GLM-5.2 actually has really good intent understanding though, on par with GPT-5.5 and Opus from my experience.

This works because Nex itself is a finetune of Qwen3.5 (https://huggingface.co/nex-agi/Nex-N2-Pro). It's merging Qwen3.5 with a Qwen3.5 finetune.

I don't believe this would work on two LLMs that have different pretraining. Even if it did you would need two LLMs that have exact same internal activation shapes, dimensions, expert counts, token vocabulary, realistically it would never happen outside of finetunes or academic experiments.

True, but coding agents have only been practically useful since early 2025. Jobs are flat or a bit up since then.

This paper has an major issue that they are not surfacing, these activations can just be correlated on a common latent. For example, both the original activation and the explanation could share a broad latent like "this is an adversarial scenario". That could make reconstruction loss look good without showing that the actual explanation was the correct cause for the LLM's response.

I find this rather disturbing. Anthropic has quite a habit of overclaiming on questionable research results when they definitely know better. For example, their linked circuits blogpost ("The Biology of LLMs") was released after these methods were known to have major credibility issues in the field (e.g., see this from Deepmind - https://www.lesswrong.com/posts/4uXCAJNuPKtKBsi28/negative-r...). Similarly this new blog is heavily based on another academic paper (LatentQA) and the correlation/causation issue is already known.

Shoddy methodology is whatever, but it feels like this is always been done intentionally with the goal of trying to humanize LLMs or overhype their similarities to biological entities. What is the agenda here?