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...
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.
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.
> 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
Providing not just any a baseline, but a correct and useful one, is ever more important the less the model is grounded in world knowledge – misunderstandings probably compound faster if there is no general grasp of (broadly) “life on earth”, or computers, or whatever.
And secondly, I think (consumer-oriented) search becoming worse and worse is a challenge that’s mostly solvable (but far from solved!) for the big labs: (Mostly) trusted or even editorialized/reviewed sources like published work, Wikipedia, etc. is something they could index internally, it doesn’t need to come from a random blog site on the public internet. Furthermore, there’s a whole slew of companies specializing in crawling-for-LLM (i.e., bypassing bot protections) now as well.
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)
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
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)
For example I prefer Kimi K2.6 1T parameter to Flash V4 0731 230B parameter, even if it is less intelligent.
In terms of the value proposition of AI replacing knowledge workers, all value is in coding agents (coding agents as general agents).
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.)
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.
Maybe I'm missing something, but on the page of SimpleQA there are only 34 models, none from Anthropic, the most recent one from OpenAI is GPT 4.1 (or O3 mini?), no Deepseek V4, no GLM.. So it turns out that in a choice of small or outdated models, Gemini 2.5 flash shines. Hardly a starting point for an argument.
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.
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
There are "experts" which do divide parts of the model that are found to activate together for specific tasks, so they can be processed in parallel to join the result at the end, but it's nowhere near the granularity of a SwiftUI expert and a python expert. The difference in those things is so trivial from an abstract point of view that it would make no sense. They would be 99% the same.
Distillations also come into this but I'm highly skeptical you could make one guaranteed to only know programming and only in one programming language (especially with as small a sample set as SwiftUI relative to something like C) without its efficacy being hobbled by tunnel vision. Reminiscent of the SpongeBob episode where he empties his mind of everything except fine dining and breathing, then can't remember his name and goes insane. Beyond the basic concepts of general coding and the trivia of syntax, getting anything done requires a large intersection of disparate world knowledge and the ability to apply it to new situations.
Instead we ended up with no finetuning. We give audio snippet to 2 AsR models, take 3 best transcriptions and ask the LLm to pick the best based on the context. That produced significantly higher accuracy in how an agent understands the users.
If I had to guess, the weights necessary to encode "how to program" are much larger than the final step of "output python."
ie what everyone asking for this fails to immediately realize.
This is roughly what multi-agent systems are built for.
This is possible with models too, but "making one on the fly" is much easier with agent coordination rather than model weights, since they all speak the same language.
There is an IBM Mainframe vs Google Distributed system division here. Like Seymour Cray said - two oxen or 1024 chickens.
Chickens are harder to harness, so a lot of my work is in sled-dog territory for agent harnesses & command structures.
I think I disagree. For some things, maybe that works - but think of a multi-agent system where one agent understands the code, and passes it off to the reasoning agent to figure out what the bug is. This system is going to suck. Because encoding enough info to figure out what the bug is would just be dumping every single line of the code.
So say agent 1 (reasoning) asks agent 2 (swift) to explain what is happening in File.swift. Anything agent 2 passes to agent 1 short of the entire code is a lossy transfer - and then the bug gets missed.
Another approach would be to have basic coding and reasoning model and then load specification for language and libraries into context, it could work for self-hosted models, but I don't want whole specification of the language to be send to API and waste tokens on that.
Hearing has volume, direction, pitch, it's spacial processing etc
THe original MoE paper from Noam Shazeer et al. is worth a read on this bit, though the paper is admittedly pretty dense. But TL;DR is that each expert layer is learning highly abstract, localized structural and syntactic patterns in the data to minimize the loss function, and its doing this token-by-token (which in some cases may have some domain clustering, but that's just incidental).
When you start batching your queries, even if they all seem like theyre in a single domain, if you visualized the activations you'd notice that most if not all of the network is lighting up on the batched forward pass.
And so far even the biggest model doesn't seem to have a working Make No Mistakes module, so maybe that's not needed
Aren't you describing RAG or even MCP servers? Heck, nowadays you get that also with agent skills and specialized tool calling.
I think having unused or rarely used weights doesn’t influence the results as poorly as RAG injecting irrelevant facts.
It sounds to me like some sort of “dynamic MoE” where you can add/create or remove experts on the fly.
I think what you’re describing is the closest approximation we reasonably have right now though.
Definitely not MCP, as that pulls info into the context. Unless contexts become REALLY big so that I can add 10B in swift knowledge, that's not gonna help me.
Possible RAG? I don't know enough about how that works, but I think that's not quite it either. I don't want to import facts like "the swift standard library contains a reverse array function", i more want to import knowledge - e.g. the parameters used to generate the text to reverse an array in swift.
Tool calling wouldn't do it either. You'd have to encode every single possible bit of useful info into the tool call, and the tool response would have to encode every piece as well (variable names, function scopes, types defined in other files, etc). E.g. how does it find a bug, if you have to pass understanding back and forth between the brain that understands debugging and the brain that understands THIS code?