> For the complete documentation index, see [llms.txt](https://docs.mosaic-program.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mosaic-program.org/2.-current-practice-and-limitations.md).
# 2. Current practice and limitations
The paper remains the only officially recognized unit of scientific contribution. No other form of work (such as the modular contributions discussed above) receives formal credit. This means the vast majority of intellectual labor that actually moves science forward is invisible to the system that allocates funding, hiring, and reputation.
This also has two important implications for AI for science: (1) Poor training data. Papers are neither machine-readable nor faithful records of how research actually happens; they bury the reasoning behind a polished narrative, so AI trained on them inherits a distorted view of science. (2) AI "slop science". Papers are cheap for agents to generate, and with no smaller, verifiable unit of contribution to anchor review, the system cannot tell real progress from plausible noise.
Meanwhile, researchers already do valuable real-time thinking on social platforms (see Appendix A for details), but too often this happens on closed networks where data is siloed and engagement is optimized over epistemic quality. And whether on open networks or not, this work is not recognized as a scientific contribution (e.g., as modular contributions like questions, critique, discussions, etc).
Existing institutions won't solve this. Academia lacks the coordination and engineering capacity for integrated infrastructure. Startups need near-term returns, limiting their capacity for collaboration. Both startups and incumbent platforms depend on moats and data enclosure. Open alternatives that resist this pattern exist (open protocols like ATProtocol or modular research tools) but operate on volunteer labor or small budgets that can't reach critical mass on their own.
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