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

Great article.

> 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

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)

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)

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.)

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.

> 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

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.