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by moduspol 55 days ago
IMO it is valuable because it suggests the primary value was in the harness and not the LLM.

That's not too surprising for those of us who have been working with these things, either. All kinds of simpler use cases are manageable with harnesses but not reliably by LLMs on their own.
1 comments
otterley 55 days ago
What if Mythos didn't need the narrowing harness? That's still the burning question that has yet to be answered. Anthropic suggested very strongly that Mythos did not need it.
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moduspol 55 days ago
I don't think it matters. Even if it didn't need it, all that implies is that it better handles a larger context window. A larger context window is not necessary to solve the problem.

We're being told that Mythos is such a big step change in capability that it needs to be kept secret and carefully controlled because a wide release could threaten cybersecurity everywhere. That does not really hold water if a barely simpler harness can do the same stuff at a lower price and is available to all of us.

The burning question to me, at least, is how many false positives each approach generated, and the degree of their falseness (e.g. "valid but not exploitable" vs. "not valid"). It's not super useful if it's generating way more noise than signal.

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solenoid0937 54 days ago
It can't do the same stuff and the fact that you think it can means that you aren't reading past the headlines of these posts!

Anthropic's own blogpost mentioned that Opus found many of the vulnerabilities as well. The difference is that Mythos developed working exploits end to end, autonomously.

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potsandpans 55 days ago
What if you could boil a pot of water with an f16 jet engine?

The harness discussion is relevant because it might be possible to achieve the same results at 1/20th the cost. IFF it is the case, these trillion dollar companies have less value than what is understood at the moment.

It's a lot easier to research harness optimizations without having to raise a billion dollars.

I'm personally very interested to know the answer. There are a lot of resources being expended (and a lot of big bets being placed) on running and training these frontier models.

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