Why Modular Research

Is the scientific paper still a fraud?

How we write scientific papers does not reflect how we do science. Their formal structure infers a pre-ordained linear process rather than reflecting the messy creativity of research. This matters in the AI age because it masks the human in the process.

Przemek Chojecki | PC on Twitter / X

The Growing Map of Open Mathematical Problems.We mapped 15,000+ conjectures from UnsolvedMath to show potential links between concepts.It also shows how under formalized the frontier is (less than 10%). pic.twitter.com/nm0PCpXVPf— Przemek Chojecki | PC (@prz_chojecki) August 28, 2026

Przemek Chojecki | PC on Twitter / X

The Growing Map of Open Mathematical Problems.We mapped 15,000+ conjectures from UnsolvedMath to show potential links between concepts.It also shows how under formalized the frontier is (less than 10%). pic.twitter.com/nm0PCpXVPf— Przemek Chojecki | PC (@prz_chojecki) August 28, 2026

OSF

The new way we’ll do science

Papers should become human-readable views over a graph of data, tools, results, and certificates.

We argue badly, and nothing accumulates. How could we do better? | Reason Commons — Issue Trees & Logical Thinking Process

Millions of people argue every day about the things that matter most — climate change, what to do about AI, how we might build a better world. Some of it is sharp, even insightful. And almost none of it accumulates.

Do we actually need to write research papers at all?

If we are going to lean on the creativity and the cross-domain thinking of human researchers, then surely that should be where the human time goes.

#predictingthefuture #newfutureofwork | Jaime Teevan

🌱 Prediction: Knowledge will outgrow publication. We’re already seeing academic publication start to buckle under AI, sometimes absurdly. I still publish research more or less the way Darwin did. I run a study, write it up, a few other scientists check it over, and the result gets filed away as a document with my name on the front. Faster than Darwin, with better figures, but the same basic shape. I predict that shape won’t last another decade. Academic authors are starting to slip hidden instructions into papers to flatter the AI that might review them. Reviewers are spending time checking whether citations exist or were hallucinated. Researchers asking AI to tell them about a paper instead of reading it directly. These are signs that the creation of new knowledge is outgrowing the articles that used to contain it. An academic paper serves many purposes at once. It makes an argument legible. It lets strangers check one's reasoning. It assigns credit and responsibility. It records who knew what and when. A paper was the only container we had for these different jobs, so it carried all of them together. With AI, they can be separated. My guess is that means the unit of publication will get smaller. Much of my research has focused on microproductivity, developing the idea that large accomplishments can be built from many small contributions. Publication will start to become a form of microproductivity. Instead of holding onto a result until it can be wrapped in a narrative large enough to justify a paper, researchers will publish it the moment it’s solid. Each finding, method, or negative result will be citable and carry its own provenance, so credit and reasoning travel with it. Reviewing will shrink to match, so claims get checked as they’re made instead of in one verdict at the end. But more than changing publication, the deeper change will be to how research itself is done. You may have heard the term “compound engineering,” where every bug fixed, evaluation written, workflow documented, or lesson learned becomes part of the system’s memory. I predict we’re about to see “compound science,” where every experiment, evaluation, insight, artifact, and learned capability becomes a reusable asset for future discovery. Findings will become evidence. Methods will become building blocks. Failed approaches will become constraints. For centuries, science has relied on humans to navigate an ever-growing body of knowledge. Soon that body of knowledge will help navigate itself. Scientists will spend less time searching for hypotheses and more time deciding which opportunities to pursue. AI systems will propose explanations, design experiments, run analyses, and explore many possibilities in parallel. Every discovery will become a part of the machinery that produces the next one. Papers ten years from now will look less like my current papers than my current papers look like Darwin’s. If they exist at all. #PredictingTheFuture #NewFutureOfWork

GainForest — Biodiversity Observations & Nature Projects

Explore field observations, biodiversity records, and nature projects from communities and organizations using GainForest.

Coordination Tech in Science: Letters, Journals, and Whatever Comes Next | shishyko!

To modernize our scientific infrastructure, we need new contextualization and coordination technologies that decouple trust from legacy branding — shifting from gatekeeping on write to algorithmic contextualization on read.

Where Should Science Go Next

Where Should Science Go Next? Prashant Garg, April 2026 Every researcher has to choose what to work on next. Science has formal procedures for judging answers, but no comparable procedure for comparing questions. Einstein said in 1918 at Max Planck's sixtieth birthday: "There is no logical path t...

Lego Brick Commons & Spontaneous Collaboration - Wesley's notes

A funner way to talk about nerdy stuff like Semble and modular science

Science as Collective Sensemaking

What is Science?

The case for a validity layer in life science research

When OpenAI recently announced that one of its models had disproved a long-standing conjecture in discrete geometry, the scientific community took notice...

The Micro-Paper: Towards cheaper, citable research ideas and conversations

Academic, peer-reviewed short papers are a common way to present a late-breaking work to the academic community that outlines preliminary findings, research ideas, and novel conversations. By comparison, blogging or writing posts on social media are an unstructured and open way to discuss ideas and start new conversations. Both have limitations in the proliferation of research ideas. The short paper format relies on the conference and journal submission process while blogging does not operate within a structured format or set of expectations at all. However, at times the demand exists for late-breaking ideas and conversations to arise in a raw form or with urgency but should still be archived and recorded in a way that promotes citational honesty and integrity. To address this, I present: The Micro-Paper, as a micro-paper itself. The Micro-Paper is a small, cheap, accessible, digital document that is self-published and archived, akin to a pre-print of a short paper. This meta micro-paper discusses the context, goals, and considerations of micro-paper authoring.

Michael 英泉 Eisen on Twitter / X

ALSO WHY THE FUCK ARE WE STILL JUST CITING PAPERS INSTEAD OF SPECIFIC PIECES OF DATA OR CLAIMS? https://t.co/dngS9d8nOW— Michael 英泉 Eisen (@mbeisen) May 16, 2026

The Last Human-Written Paper: Agent-Native Research Artifacts

Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural costs: a Storytelling Tax, where failed experiments, rejected hypotheses, and the branching exploration process are discarded to fit a linear narrative; and an Engineering Tax, where the gap between reviewer-sufficient prose and agent-sufficient specification leaves critical implementation details unwritten. Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work. We introduce the Agent-Native Research Artifact (ARA), a protocol that replaces the narrative paper with a machine-executable research package structured around four layers: scientific logic, executable code with full specifications, an exploration graph that preserves the failures compilation discards, and evidence grounding every claim in raw outputs. Three mechanisms support the ecosystem: a Live Research Manager that captures decisions and dead ends during ordinary development; an ARA Compiler that translates legacy PDFs and repos into ARAs; and an ARA-native review system that automates objective checks so human reviewers can focus on significance, novelty, and taste. On PaperBench and RE-Bench, ARA raises question-answering accuracy from 72.4% to 93.7% and reproduction success from 57.4% to 64.4%. On RE-Bench's five open-ended extension tasks, preserved failure traces in ARA accelerate progress, but can also constrain a capable agent from stepping outside the prior-run box depending on the agent's capabilities.

Resilient Data Futures — Discourse Graph

A living, content-addressed, contributable form of the SciOS Resilient Data Futures whitepaper.