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
This is great, thanks for the link. IMHO it actually supports the broader claim: if ICL emerges in both language models and genomic models, it suggests the phenomenon actually is about structure in the data, not something special about neural networks or transformers per se.
Genomes have statistical regularities (motifs, codon patterns, regulatory grammar). Language has statistical regularities (morphology, syntax, collocations). Both are sequences with latent structure. Similar architectures trained on either will repeat those structures.
That's consistent with my "instrumentation" view: the transformer is revealing structure that exists in the domain, whether that domain is English, French, or DNA. The architecture is the microscope; the structure was already there.
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.
Yeah. This comment is profound to me. The internet works differently with these tools.
I haven't used the deep research features much but their ability to hash out concepts and build knowledge or even provide an amplified search experience is something...
I get why this comment was downvoted but I also get where you're coming from - yes, these models are becoming increasingly intelligent at understanding the nuance and where to look without knowing what to begin searching for.
But the downside is, you end up digging in the wrong direction if you leave it to a generalist system instead of a professional community in some cases which is counter productive.
Getting burnt is a good way to learn not to sometimes though...
> 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.
Interesting, always wanted to try AR for office work because I travel a lot but the glasses part has always been a killer. Do they work with Linux at all?
Actually historically they are pretty finicky about resolution and refresh rate - it's only with the last firmware update or two that they work with RPi reliably for example - I couldn't come up with a working X config for a long time.
Just note that you will also need the Xreal Beam if you want the virtual screen to stay fixed in space while you move your head (it's too uncomfortable to use without it IMO).
