Oof - it's not like goodfire - we have quantitative results and a clearly defined method!
But yes, we might be bad at marketing. Internally the term we use more often is "concept elements".
The intuition is that if you take a complex treatment of a topic (like an academic paper), you should be able to break it down into some more abstract self-standing ideas. Then, critically, these units are composable and easily embeddable (i.e. they don't have proper names and numbers).
Here's a couple examples of these idea atoms pulled from our paper https://lacuna.tiptreesystems.com/work/the-alien-space-of-sc...: "Scientific ideation often conflates the logical coherence of a research direction with its likelihood of being discovered by the current community. This oversight makes it difficult to identify research paths that are scientifically sound yet unlikely to be discovered due to the existing distribution of expertise and collaboration networks."
Another example: "A generative model estimates the plausibility of concept combinations by treating conceptual units as tokens and calculating their length-normalized log-likelihood. By training on random orderings of these units, the model learns to identify which components are likely to co-occur in a valid research contribution regardless of their sequence."
But yes, we might be bad at marketing. Internally the term we use more often is "concept elements".
The intuition is that if you take a complex treatment of a topic (like an academic paper), you should be able to break it down into some more abstract self-standing ideas. Then, critically, these units are composable and easily embeddable (i.e. they don't have proper names and numbers).
Here's a couple examples of these idea atoms pulled from our paper https://lacuna.tiptreesystems.com/work/the-alien-space-of-sc...: "Scientific ideation often conflates the logical coherence of a research direction with its likelihood of being discovered by the current community. This oversight makes it difficult to identify research paths that are scientifically sound yet unlikely to be discovered due to the existing distribution of expertise and collaboration networks."
Another example: "A generative model estimates the plausibility of concept combinations by treating conceptual units as tokens and calculating their length-normalized log-likelihood. By training on random orderings of these units, the model learns to identify which components are likely to co-occur in a valid research contribution regardless of their sequence."