Models Are Getting Dumber on Purpose

After that Space colonization will come.

>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?

I wonder what’s tre latest in this field? Did we get a grip on this problem?

Part of the problem of this is likely that the deep meanings of words you might use in chat to describe a business problem or task that you wish to see implemented are essentially inseparable from scenarios in which they are used.

Putting aside the bouba/kiki effect and anything like it, complex words only have meanings from usage. That usage is built on grammatical structures that also emerged only from usage.

(This is something I was taught as a sort of fact but I gather it was basically abbreviated Wittgenstein? … who I cannot claim to have studied)

So what you're looking for is a language model where fundamental word meanings are encoded without the weight of knowledge of where they come from. This is plainly difficult, because complex words are used by extension and analogy, and these days, many are neologisms or portmanteaus, even ephemerally — developed and discarded within a single context.

Reasoning about language itself to its full meaning is quite hard.

Like my favourite word of the moment: "obscurantist". You see that and you have a glimmer of what it might convey. But why do you? How much of that comes from explicit grammatical knowledge of suffixes, and how much from simple experience of using words like obscured, informant, attendant, dentist, artist?

So a language model might be able to deduce what "obscurantist" logically means when applied to a tract or to a person. But without lots of parameters covering its use, could it properly grasp that in some circles it would be pejorative to the point of being deeply offensive?

I think the best hope for your pluggable knowledge base idea is model delegation: strong reasoning models that know how to dictate to smaller specialist models and draw conclusions from their responses. I find myself wondering if there's any way that can be done the same way that, say, Gemma 4 12B's integrated vision encoder works — within shared weights, somehow, without them to speak in some intermediate language, like a partitioned brain. But I find it difficult to believe that is pluggable at all.