Area 01
AI & Computational Science
The lab's core. Cognition infrastructure that lets systems reason across domain boundaries — and, a layer beneath it, the study of how a model's architecture shapes what it can learn.
What this area is
This is the methodological engine of the lab, and it works on two layers. The first is cognition infrastructure: systems that reason across domain boundaries and produce answers no single corpus or model would generate alone. The second sits a layer beneath — the architecture of the models themselves: how output heads, tokenization, and training objective determine what a network can actually learn, especially when data is scarce.
The two layers feed each other. The cross-domain work tells us what kind of reasoning we want; the architecture work tells us what the substrate can be made to support. Both are held to the same standard — operationalized, measured, and tested against the strongest baseline we can build.
Lines of work
Cross-Domain Bridging
Operationalizing and measuring whether models can produce structurally cross-domain answers at materially higher rates than current published synthetic-data practice. Five generating models, three vendor families, cross-family blind judging.
Read moreModel Architecture & Language Models
How the shape of a model — its output heads, its tokenization, its objective — determines what it can learn from limited data. A hands-on study of transformer and sequence-model architecture, run on controlled prediction substrates where the trade-offs can actually be measured.
Detail page forthcomingCausal Cartography
A discovery and causal-reconstruction apparatus for issue-centered matters. Traverses schedules, communications, events, and the full document record of a complex effort to recover what actually happened and to surface what the same evidence implies going forward. First applied in construction; in active use on a live matter.
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