You mean it's state of the art for this subset of benchmarks you're targeting? No? Well surely it's state of the art for models of a certain size category? Huh, what about models that are available for local inference? O, state of the art for open models of a certain size made in the US specifically. Uh. Neat!
> Introducing Ripple-65B Preview, a Frontier Open-Weight Model
It's not "frontier", it's not "frontier" even in the category of open-weight models (though usually that phrasing would mean frontier over all, and also open weight)... it's only "frontier" in the small sub-category of US made open-weight models.
Being at the Pareto frontier means that you can't get something better on one aspect without compromising on some other aspect. The trick is that it is easy to be at the Pareto frontier by having enough dimensions.
Here, we have 4: score, number of parameters, origin (US) and open-weight. You can get a better score or a lower number of parameters, but you have to compromise on at least one of the other aspects. That's a pretty weak frontier if you ask me.
(You could say that “ability to be trusted by US government/military users” is quantifiable, but I would argue that by that metric, this model falls behind non-US-originated open-weight models that have more eyes on them!)
By that logic, any model is a frontier model. My GPT-2 clone is a frontier model when you compare it against models that were trained on my homelab.
I guess it makes sense - there is a segmentation where users may want to run on domestic "frontier LLMs."
[Edit i think the preferred combination for those who actually want some data security at the moment and arent at the scale of self hosting llms is chinese open weight models served in germany or France where theres some better data privacy/security]
For one, you might not trust that the weights do not contain ideological biases (e.g. ask DeepSeek about tibet) or that the methods used to impose these biases may not have contaminated the model in some other way.
For another, you might not trust that the code the model generates won't be backdoored or attempt to leak information through your sandboxes.
For a third, you may not want to build your stack on a partner who you feel is a supply chain risk, who might become unaligned with your interests.
By far the biggest though, is that many industries will be forbidden from doing so by the government in an attempt to protect the US model industry (and thus create the demand for some model like this one).
I'm always excited to see new entrants to this open-models race, no matter why they're running.
And your speculation about future protectionist regulation is just that, not an actual fact.
If you are a US based critical infrastructure provider, I'd argue that you have already accepted a baked-in threat of ideological misalignment from the US, but you have not accepted it from farther away countries like China.
>a supplier of open (as opposed to hosted) model weights is not an active ongoing partner with whom the idea of supply chain risk from possible future divergence of interests is coherent
I disagree. Models are improving so quickly that you'd probably not want to freeze yourself on an older model forever. You likely want to continue to upgrade models. The best way to do this is to build a sufficiently powerful and representative benchmark that you can confidently move to _any_ model, but you might expect that future models from the same providers that you accepted before will show less divergence than totally new providers. Similarly, if you build your inference around one of their custom tools (e.g. DeepInfer rather than vLLM), you might find substantial switching costs in moving away to another provider.
>And your speculation about future protectionist regulation is just that, not an actual fact.
It's not a done deal for sure, but you can already see a lot of signs of this in recent actions from the government. I can tell you based on personal experience that some customers will automatically rule out any Chinese models.
I don't disagree with you on the severity of the concerns, but given the vast amounts of code that must have been generated in the past six months by the likes of GLM5.2, GLM5.3, deepseek v4 flash 0731 and newer, qwen 3.5-122, 3.6 and 3.8 series models, I would like to see any concrete examples of it building backdoors/intentional vulnerabilities/phone-home-to-china or similar unprompted. If it's been doing this and anyone with even a modicum of a clue has been watching it, surely someone has caught a model doing this? At least once?
Surely a model doing something like writing intentionally obfuscated code that obscures its actual purpose from a code reviewer, in the service of something malicious/backdoor-like, would have been seen and noticed by now.
The question is only whether they overlap with your own.
Weights aren’t source that can be inspected for nefarious tricks that would impact use, so, to the extent that there is a dinension of trust associated with nation of origin, being open weights doesn’t obviate the concern (I would argue, though, that there is very little reason to for nation of origin to be a significant factor in trust alone, though a finer grained consideration of origin might be sensible.)
Well, you might want to know what propaganda/censorship you are subjected to. Case in point, a regional government in Austria just learnt that their AI doesn't know about the Tiananmen Square massacre.
Yep, you’re right. Quite easy to dilute the term.
A tinyurl pic linked by randoms over the internet?
It was already meaningless when US labs used it.
It means new.