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Yep, we think this too. If we want superhuman performance, agents might need to learn from measurements of nature and not human graders.
What makes a biological reasoning task verifiable? Our team @tahoe_ai has spent the last few months on that question and landed on one conviction: for human-difficult tasks with verifiable rewards, the answer key has to come from ground-truth experimental data.
Along the way we built thousands of such environments, which we call TahoeMysts, and we use them to evaluate frontier models (among other things). Every so often a new model produces a big jump, and it is always exciting. Today is one of those days: @claudeai Mythos 5.2!
We will say more about this work in the future. In the meantime, take a look at our results today.View quoted post on X
When next-generation sequencing came out, the thing to do was to read all those Nucleic Acid Research papers from the 1990s looking at a single gene and extending the study to *all* genes. This sorta rhymes.
With this model release, Claude is beginning to feel like a very capable assistant for research. One exercise I can recommend is sharing an older paper or draft, and asking the model to reimplement or extend it in some way. Claude Mythos 5.2 excels on long-horizon research tasks.
I ask all our models to hypothesize about the function of vault particles now. Here’s what Mythos 5.2 thinks:View quoted post on X
I used to think one day a camera in your bathroom mirror might give a daily illness eval but one facing your front door or kitchen might be better
AI finds health signals in movement beyond age and body size
A model that predicts health from how someone moves might mostly be recognizing their age and body size. Those factors already explain many differences in health.
Gabet and colleagues put that possibility to the test. They recorded five movement tasks in 3,328 adults using a depth camera, then trained a masked autoencoder to reconstruct missing parts of the 3D skeletal motion. The model learned patterns of movement without needing health labels during pretraining.
The crucial comparison came afterward. Could those learned patterns improve health predictions when the baseline already knew each person’s age, BMI, height and abdominal fat? They evaluated males and females separately.
In male participants, the learned movement features significantly improved prediction for 823 of 3,210 measured traits, compared with 188 for conventional movement measurements such as cadence and stride length. The model was recovering information that those familiar summaries missed.
These were mainly associations with health measured at the same time, within one cohort. They do not yet establish that the model can predict future disease or work reliably in other populations.
For scientists developing new biomarkers, this is a useful test: give the baseline the obvious explanations first, then measure what your learned representation adds.
Gabet et al., Nature Medicine (2026). Journal license. nature.com/articles/s4159…View quoted post on X
More preprints! More public datasets!
@leashbio is about a dozen people and we dropped 3 papers in the last year. And the year's not over!
@leashbio is about a dozen people and we dropped 3 papers in the last year. And the year's not over!
I’m as AI pilled as anyone, but one of the most tedious parts of AI x Bio right now is almost a complete lack of and almost an active avoidance of scholarship.View quoted post on X
