u/EducationalCicada
GPT-5.6 Sol Ultra Produces Proof Of The Cycle Double Cover Conjecture
cdn.openai.comThermodynamic Measure Of Intelligence
arxiv.orgAnthropic Walks Back Claude's Silent Sandbagging When It Detects Users Are Working On Frontier LLM Research
https://www.lesswrong.com/posts/sSyLyc3KDQzboQGWS/thoughts-on-claude-fable-s-silent-safeguards
Note, they walked back the silent part, not the sandbagging part.
For context, when Claude Fable was released, the following lines were buried in the model's system card, but unmentioned in the blog post (emphasis added):
>In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.
>Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT).
As you can see, these "safeguards" don't just involve dropping to an older model as is the case in cybersecurity and biotech queries, but what looks like (if an uncharitable person were to squint) active sabotage.
As pointed out in the quoted section, it's actually in Anthropic's terms of service that you can't use Claude to develop rival models, but they're enforcing it in the most underhanded and morally dubious way imaginable.
I'm sure the justification they'd give would be something, something, AI safety, but surely even they can see that AI safety and protecting their moat in the lead-up to IPO look virtually identical to a random outsider. And imagining the EA-flavored moral reasoning that brought them to this decision doesn't give me a good feeling about the future.
Chariots Of The Gods, Erich Von Daniken (1990 Souvenir Press Edition, Artist Unknown)
We present a genetic algorithm framework for automatically discovering deep learning optimization algorithms.
Our approach encodes optimizers as genomes that specify combinations of primitive update terms (gradient, momentum, RMS normalization, Adam-style adaptive terms, and sign-based updates) along with hyperparameters and scheduling options.
Through evolutionary search over 50 generations with a population of 50 individuals, evaluated across multiple vision tasks, we discover an evolved optimizer that outperforms Adam by 2.6% in aggregate fitness and achieves a 7.7% relative improvement on CIFAR-10.
The evolved optimizer combines sign-based gradient terms with adaptive moment estimation, uses lower momentum coefficients than Adam ( =0.86, =0.94), and notably disables bias correction while enabling learning rate warmup and cosine decay.
Our results demonstrate that evolutionary search can discover competitive optimization algorithms and reveal design principles that differ from hand-crafted optimizers.