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felipeerias

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That comparison is also misleading because Opus 4.6 was probably not Anthropic's frontier model.

We got the first news about Mythos in March, so it is likely that it was already close to ready by the time Opus 4.6 was released.

So the actual gap is the time elapsed between March (or April for the official announcement) and whenever Chinese models can match Mythos.

Will It Mythos? 30 days ago

Copyright is a social construct, not an inherent property of the universe. It is whatever we collectively agree it is.

In practice, we seem to be leaning towards the idea that training on a copyrighted book is wrong if used to replicate or paraphrase that same book, but not if used to teach a model how to write better.

Exactly, the clock is external to the model. Nothing prevents it from being faster or slower, or even running backwards, because it’s ultimately just another data point in the input stream to a computer function.

Your brain and your whole body exist in time. Even when you are asleep, your body does not flicker out of existence and your brain actually continues working during that time.

IMHO the sane position is essentially the Aristotelian one.

Hylomorphism: body and consciousness are intrinsically linked. The nature of that link is an open metaphysical question.

Virtue ethics: even if LLMs are not conscious, we should not abuse or mistreat them them because cruelty practised on anything trains one's disposition toward cruelty.

One of the mathematicians in the video describes the process as:

the AI has been able to explore all these possibilities much more comprehensibly, and doing that it found a path, it found a way to the solution.

Finding a counterexample of a mathematical conjecture strikes me as not that different from finding a vulnerability in a complex codebase.

The other side of this is that open source projects that allow AI tools will be more restrictive towards new contributors.

This already happens to some degree on large software projects with corporate backing (Web engines, compilers, etc.), where it is often not trivial to start contributing as an independent individual.

Reasonable people can disagree on whether one approach is inherently better than the other, as ultimately they seem to be optimising for different goals.

Claude 4.7 broke something while we were working on several failing tests and justified itself like this:

That's a behavior narrowing I introduced for simplicity. It isn't covered by the failing tests, so you wouldn't have noticed — but strictly speaking, [functionality] was working before and now isn't.

I know that a LLM can not understand its own internal state nor explain its own decisions accurately. And yet, I am still unsettled by that "you wouldn't have noticed".

Nowadays Japan’s fertility rate is higher than most of its neighbours. We are just used to pick it as an example because it started aging earlier than most other countries.

Japanese population is still over 120 million. Forecasts put it falling below 100 million at some point in the second half of this century.

Things will have to change in order to keep population stable in the long term, but the Japanese approach seems IMHO more sensible than that of other countries.

Cohesive democratic societies are fragile.

Anthropic gave the model the whole codebase and told it to find a vulnerability on a specific file, iterating across sessions focusing on different files.

What happens then is that, for example, the model looks through that particular file, identifies potential problems, and works upwards through the codebase to check whether those could actually be hit.

“Hum, here we assume that the input has been validated, is there any way that might not be the case?”

This is not unique to Mythos. You can already do this with publicly available models. Mythos does appear to be significantly more capable, so it would get better results.

The research discussed here provided models with just a known buggy function, missing the whole process required to find that bug in the first place.

A LLM is not intrinsically affected by time. The model rests completely inert until a query comes in, regardless of whether that happens once per second, per minute, or per day. The model is not even aware of these gaps unless that information is provided externally.

It is like a crystal that shows beautiful colours when you shine a light through it. You can play with different kinds of lights and patterns, or you can put it in a drawer and forget about it: the crystal doesn’t care anyway.