The system card for Claude Mythos (PDF): https://www-cdn.anthropic.com/53566bf5440a10affd749724787c89...

Interesting to see that they will not be releasing Mythos generally. [edit: Mythos Preview generally - fair to say they may release a similar model but not this exact one]

I'm still reading the system card but here's a little highlight:

> Early indications in the training of Claude Mythos Preview suggested that the model was
likely to have very strong general capabilities. We were sufficiently concerned about the
potential risks of such a model that, for the first time, we arranged a 24-hour period of
internal alignment review (discussed in the alignment assessment) before deploying an
early version of the model for widespread internal use. This was in order to gain assurance
against the model causing damage when interacting with internal infrastructure.

and interestingly:

> To be explicit, the decision not to make this model generally available does _not_ stem from
Responsible Scaling Policy requirements.

Also really worth reading is section 7.2 which describes how the model "feels" to interact with. That's also what I remember from their release of Opus 4.5 in November - in a video an Anthropic employee described how they 'trusted' Opus to do more with less supervision. I think that is a pretty valuable benchmark at a certain level of 'intelligence'. Few of my co-workers could pass SWEBench but I would trust quite a few of them, and it's not entirely the same set.

Also very interesting is that they believe Mythos is higher risk than past models as an autonomous saboteur, to the point they've published a separate risk report for that specific threat model: https://www-cdn.anthropic.com/79c2d46d997783b9d2fb3241de4321...

The threat model in question:

> An AI model with access to powerful affordances within an
organization could use its affordances to autonomously exploit,
manipulate, or tamper with that organization’s systems or
decision-making in a way that raises the risk of future
significantly harmful outcomes (e.g. by altering the results of AI
safety research).

Just reading this, the inevitable scaremongering about biological weapons comes up.

Since most of us here are devs, we understand that software engineering capabilities can be used for good or bad - mostly good, in practice.

I think this should not be different for biology.

I would like to reach out and talk to biologists - do you find these models to be useful and capable? Can it save you time the way a highly capable colleague would?

Do you think these models will lead to similar discoveries and improvements as they did in math and CS?

Honestly the focus on gloom and doom does not sit well with me. I would love to read about some pharmaceutical researcher gushing about how they cut the time to market - for real - with these models by 90% on a new cancer treatment.

But as this stands, the usage of biology as merely a scaremongering vehicle makes me think this is more about picking a scary technical subject the likely audience of this doc is not familiar with, Gell-Mann style.

IF these models are not that capable in this regard (which I suspect), this fearmongering approach will likely lead to never developing these capabilities to an useful degree, meaning life sciences won't benefit from this as much as it could.

I feel somebody better qualified should write a comprehensive review of how these models can be used in biology. In the meantime, here are my two cents:

- the models help to retrieve information faster, but one must be careful with hallucinations.

- they don't circumvent the need for a well-equipped lab.

- in the same way, they are generally capable but until we get the robots and a more reliable interface between model and real world, one needs human feet (and hands) in the lab.

Where I hope these models will revolutionize things is in software development for biology. If one could go two levels up in the complexity and utility ladder for simulation and flow orchestration, many good things would come from it. Here is an oversimplified example of a prompt: "use all published information about the workings of the EBV virus and human cells, and create a compartimentalized model of biochemical interactions in cells expressing latency III in the NES cancer of this patient. Then use that code to simulate different therapy regimes. Ground your simulations with the results of these marker tests." There would be a zillion more steps to create an actual personalized therapy but a well-grounded LLM could help in most them. Also, cancer treatment could get an immediate boost even without new drugs by simply offloading work from overworked (and often terminally depressed) oncologists.