Why this post is here in the front page? The Qwen vector steering performed was a very selective operation that interested only specific layers and weights set, it is non trivial to identify and fix. This is remarkable. Also Experiment 3: no access to forward pass, so why here this post says the contrary of what the paper shows and everybody upvoted it? I don't understand.

▲

syllogism 3 hours ago | parent | next [-]

Experiment 3 doesn't involve reading the weights in as part of the prompt. It's a coding exercise. Here are the turns: https://github.com/RikiyaT/lm_understand-logs/tree/main/exp3...

At each point the model has to write code to read the weights in with Python and some other Python code to perform analysis, like this: https://github.com/RikiyaT/lm_understand-logs/blob/main/exp3...

My complaint is that the post presents a confusing mix of experiments that imply capabilities that are more than the sum of their parts. This experiment 3 has nothing to do with experiment 1 for instance. In experiment 1 the model does read in the raw weights like it's reading an image.

▲

nsagent an hour ago | parent | next [-]

If you don't like these experiments, then I can only assume you would complain about CRISPR as well, considering it's a general framework with many disparate parts that work together synergistically, similar to the work Takehi et al. describe. CRISPR was also famously rejected multiple times by reviewers.

Note that being able to both inspect raw weights and run Python code to perform additional analysis means the model has multiple possible techniques at its disposal (generally considered a positive, rather than a "confusing mix").

	▲	syllogism 37 minutes ago \| parent [-]
		I don't understand CRISPR well enough to comment on the analogy. The blog post claims to show that frontier LLMs can understand and craft neural network weights natively. The experiments where the model completes coding tasks are not relevant to this conclusion. Those experiments require the model to demonstrate an analytic understanding of how a network might behave under various conditions, along with reasoning skills, coding skills etc. As a coding task this is a significant challenge. But it's entirely different from "natively reading and writing neural network weights". Would you also consider it a benefit if there were experiments showing that a frontier model can download and run PyTorch, or write its own network and optimiser? That's another technique it has at its disposal to improve an ML model. It's just not one that's relevant to the paper's core claims.

▲

2 hours ago | parent | prev [-]

[deleted]

▲

SuperV1234 3 hours ago | parent | prev | next [-]

> Why this post is here in the front page?

You know exactly why... because anti-AI hysteria is quite popular on HN.

Any "critique" or "appeal to resist AI" rooted in emotion and inability to admit that intelligence is not a feat unique to humans anymore will make the rounds here.

	▲	bananaflag 2 hours ago \| parent \| next [-]
		But why? Shouldn't people who like CS be more open to the idea? I mean this is why computers were created, to think instead of people.
	▲	ex-aws-dude 39 minutes ago \| parent \| prev [-]
		Are you kidding? If anything pro-AI hysteria is way stronger here, just look at how many upvotes the original got Even any small model update a company puts out is guaranteed top of HN

▲

imtringued 3 hours ago | parent | prev | next [-]

If people have programmed programs to interpret neural networks, what is so special about the AI doing so? It's a forgone conclusion.

You can let the AI generate training code for the next architecture too.

It's not like the AI is randomly losing its ability to write software just because it involves AI.

▲

youareundersell 3 hours ago | parent | prev [-]

I am growing increasingly fond of the term "anti-AI psychosis". It's getting ridiculous sometimes.