Isn't that already on the tax return? Your dependent count would increment from the prior year. The IRS can also distinguish births vs adoptions and step children by the checking for novel SSNs.
What do you make of this article? They used an auto-regressive genomic model to perform in-context learning experiments compared to language models. This showed that ICL behavior is not exclusive to language models. https://arxiv.org/html/2511.12797v1
Another great example of this working is the genomic variant calling models from Deepmind "DeepVariant". They use the "alignment pile-up" images which are also used by humans to debug genomic alignments, with some additional channels to further feature engineer the CNN.
> What was true last year may be false today. For instance, ...
Good example of a medical QA dataset shifting but not a good example of a medical "fact" since it is an opinion. Another way to think about shifting medical targets over time would be things like environmental or behavioral risk factors changing.
Anyways, thank you for putting this dataset together, certainly we need more third-party benchmarks with careful annotations done. I think it would be wise if you segregate tasks between factual observations of data, population-scale opinions (guidelines/recommendations), and individual-scale opinions (prognosis/diagnosis). Ideally there would be some formal taxonomy for this eventually like OMOP CDM, maybe there is already in some dusty corner of pubmed.
I have experience reading and implementing state of the art deep learning papers within the domains of object detection, image generation and text understanding. I'm open to any job tangentially related to machine learning.
I am a recent graduate from Georgia Tech. I have experience with machine learning workflows and I have deep knowledge of image generation methods. I am open to learning new technologies and domains as needed to support your business objectives.