Oldest Papers
How a Forensic Biologist Exposed a DNA Lab Scandal That Shook Australia | The Scientist
Rebecca Roberts, PhD
the-scientist.com·2026
No abstract available
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View paper →Disability inclusion in health data systems and policy in Korea: Time for action
Jong Eun Park, So Young Kim, Ichiro Kawachi, Hannah Kuper, Jong Hyock Park
Health Affairs Scholar·2026
Abstract
Persons with disabilities experience significant health inequities globally, driven in part by the lack of disability-disaggregated data in national health surveillance systems. In Korea, despite having a well-established administrative infrastructure—most notably the Korean National Disability Registration System—disability-inclusive health data remain limited. The National Health Information Database, which links administrative and health insurance data, offers a rare example of integrated data enabling individual-level analysis by disability status, type, and severity. However, the absence of a legal mandate and limited policy recognition of disability inclusion have hindered the widespread use of disability identifiers across key national health datasets. Korea’s experience illustrates both the potential and the limitations of relying solely on administrative definitions, which often fail to reflect the broader spectrum of disability outlined in international frameworks such as the ICF. To advance equity in health systems, countries must integrate disability into population-based data using standardized, functional definitions. This commentary emphasizes the need for both technical integration and a conceptual shift in how disability is defined, measured, and operationalized in health data systems.
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View paper →To gain public trust, make art central to science communication
Kot, Palina
nature.com·2026
Letter to the Editor
https://www.nature.com/articles/nj7618-125a
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View paper →Deep learning as program synthesis
Zach Furman
LessWrong·2026
Epistemic status: This post is a synthesis of ideas that are, in my experience, widespread among researchers at frontier labs and in mechanistic interpretability, but rarely written down comprehensively in one place - different communities tend to know different pieces of evidence. The core hypothesis - that deep learning is performing something like tractable program synthesis - is not original to me (even to me, the ideas are ~3 years old), and I suspect it has been arrived at independently many times. (See the appendix on related work).
This is also far from finished research - more a snapshot of a hypothesis that seems increasingly hard to avoid, and a case for why formalization is worth pursuing. I discuss the key barriers and how tools like singular learning theory might address them towards the end of the post.
Thanks to Dan Murfet, Jesse Hoogland, Max Hennick, and Rumi Salazar for feedback on this post.
> Sam Altman: Why does unsupervised learning work?
>
> Dan Selsam: Compression. So, the ideal intelligence is called Solomonoff induction…[1]
The central hypothesis of this post is that deep learning succeeds because it's performing a tractable form of program synthesis - searching for simple, compositional algorithms that explain the data. If correct, this would reframe deep learning's success as an instance of something we understand in principle, while pointing toward what we would need to formalize to make the connection rigorous.
I first review the theoretic...
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