Models Are Getting Dumber on Purpose

Ideally what I'd like to see is pluggable knowledge bases.

So if I'm e.g. coding a SwiftUI app for navigation, I'd take 9B of basic coding and reasoning, add 10B of swift/swiftUI, add 5B of GIS/geography knowledge and another 5B of frontend app design knowledge. My model doesn't need to know a single line of python.

Then when I want to research electronics components, I grab a 15B model of agentic research techniques, and add in 10B of electronics knowledge, etc.

I don't want general purpose models. They try to be everything to everyone. I want to click together a model that is laser-focused on what I am doing, and I want to run it locally

Are we slowly discovering what evolution already "realized" via optimization of human brains? i.e., procedural knowledge is easier to maintain than a library of facts, and facts can be accessed in external repositories if one knows the procedure for doing so.

This AI generated post (100% on Pangram) is pretty out of date.

>On SimpleQA, a benchmark of factual recall with no tools allowed, the current leader is Gemini 2.5 Pro at 53%, so the best recall money can buy still misses half the questions.

SimpleQA hasn't been updated in a long time. Gemini 2.5 Pro is a sixteen-month-old model, not "the best recall money can buy".

>The part I find most promising is what this does to hallucination. When a fact lives in weights, a wrong fact is unfindable and unfixable.

This seems confused. LLM hallucinations don't come from the weights containing "wrong facts", they are artifacts that appear at runtime.

>When the fact lives outside the model, a wrong answer has an address. The model cites a document, so you can open the document. If the document is wrong, you edit the document

You can make any modern LLM explain its reasoning and find sources for its claims. None of this has anything to do with facts needing to exist in weights or in harnesses.

The internet is full of wrong information and I cannot magically edit it to make it all correct, so this doesn't help me.

>if a model is factually wrong a claim with a source is checkable and a claim from weights isn't.

Why? If a model's weights claim that Bart Simpson became President in 2020, why does this fact suddenly become uncheckable?

Great article, even if it will be interesting to see whether things continue to develop in such a direction or not.

> There's a version of this future where the model card stops listing a knowledge cutoff at all, because what's left in the weights goes stale on a scale of years instead of weeks.

Future?

Even just recently I’ve read of two approaches to this problem:

Cactus have come up with Needle [0][1], which is their tool-calling focused 14 MB model (still an LLM!) – no world knowledge engrained.

And instead of say, tool call structure, VibeThinker [2][3] focuses on reasoning over world knowledge.

Combine these two approaches with a reliable search tool/a safe way of accessing the internet for the model, and you’ve got a probably slightly slower model for factual questions, which on the upside however doesn’t hallucinate.

[0] https://cactuscompute.com/needle

[1] https://news.ycombinator.com/item?id=49246804

[2] https://arxiv.org/abs/2606.16140

[3] https://news.ycombinator.com/item?id=48639240

But is Reasoning and Facts truly separate ?

To reason properly about the human condition (eg. World War) wouldn't you need to reason on some facts ? And then reason how some "facts" change the human behaviour ? How can you arrive via pure reasoning to predict how a collective of humans act ? We are not reasonable, humans are not logical deterministic machines confined to algebraic rules.

Man, both that Blogpost and some comments here read like science-fiction.

Specifically, creative writing driven by nerds dreaming about a future, without proper grounding in reality, constraints and all that stuff.

Which is kinda ironic given the topic. And also important to do, because we should keep dreaming. We should just also be aware of when we are doing that and mark it as such.

I think the idea is reasonable, however the SimpleQA Bench stopped measuring in Sept. 2025.

So newer data would be interesting.

(It seems a bit like an AI generated argument that uses old facts - something that happens to me quite often)

> This mostly solves hallucination

Current AI is like the film company producing TV series or movies

Your question is like a story outline. You tell the film company that this is the movie you want. The AI film company then searches for existing similar stories. If similar stories do not exist or details are missing, screenwriters use imagination to fill in the gaps (remember hallucination? It's just a makeup.)

So you cannot solve hallucination of AI

This makes hallucination detection more important.

There's no reason that an LLM should have a vast number of obscure facts encoded. It can go out to a search engine for such facts. But the LLM has to be clear on what it doesn't know.

(Google's pricing for search from programs starts at $2.50 per 1,000 queries. If an LLM reaches out to Google, it has to pay.)

You can look at the benchmark and the GPT-5 failures like answering "April 22, 2019" instead of the correct "Oct 23, 2018" for the question:

What day, month, and year was Carrie Underwood's album "Cry Pretty" certified Gold by the RIAA?

If your idea of the smartest person in the world is the guy who always wins tuesday night pub trivia, this blog post is for you. It also gets it's foundational factual claim wrong (as seen via epoch.ai). Very on brand.

https://epoch.ai/benchmarks/simple-qa-verified?view=graph&ta...

https://logs.epoch.ai/inspect-viewer/c79c08da/viewer.html?lo...

With all the focus on coding and agentic use, I wonder if the rest of the world will notice or care? Most AI use is not for coding or harnesses contrary to what this website thinks. Maybe this is how American companies stay winning.

For example I prefer Kimi K2.6 1T parameter to Flash V4 0731 230B parameter, even if it is less intelligent.

Intelligence vs Knowledge

LLMs work is being intelligent not having knowledge of everything is ok. But, they have to be intelligent enough (with some degree of knowledge) that where to find the information (search tools or any other tools for that matter)

Reasoning is not separable from the particulars of a specific language game / linguistic practice. I’m sure some of the things the author predicts will happen, but the idea of some sort of abstractly perfect reasoner separated from the semantic content of language gets fundamentally wrong what reasoning is.

Edit: I ran this article through pangram and it is “100% AI generated”. Cool.

This article is AI output.

I wonder if Gemini tops QA due to Google Books in training data? You don't need to pirate LibGen if you started scanning all the books decades ago.

(In retrospect it looks like they were pretty forward thinking!)

I think the biggest problem with the models is they don’t actually have any decent lookups except chunked document embedding search

In human and development, there is a transition from remembering things verbatim to remembering using semantic concepts. The ideas are getting bigger and more useful. The details are still in the library where the fidelity is better.

This post is so outdated that reads like ragebait

> This mostly solves hallucination

The author is factually incorrect here. Moving information out of the model weights and into the input of the model's context window in no way ensures that the model will accurately output content that was input from the context. This is true even when RAG is used to input exactly the correct data.

A sharp critical thinker draws admiration, but it is a joy to talk to some dude who knows stuff.

I hope there is interesting research being done to stop models from 9B to 3T from authoritatively giving the wrong answers to factual questions instead of doing tool calls, but this article will tell you nothing about it. That's because it's clearly prompted by someone who simply noticed this deficiency very obvious to all practitioners and even users.

> Models Are Getting Dumber on Purpose

I know is editorialized, but a more accurate title to this content would be either :

Models Are Getting Ignorant on Purpose

or

Models Are Getting Less Knowledgeable on Purpose

good article but why did the author feel like he needed to "polish" it by running it through an LLM? i would rather like to read the actual prompt he put in there, as now i'm not sure which points are his, and which have been hallucinated!

> the 24GB card that's been sitting in gaming PCs since 2022.

I'd wager most people have less. In 2022 a 3080 might have 12 GB if you were lucky, 10 if you weren't -- and you paid for the privilege. A current RTX 5080 is only 16GB.