Phase 1: an agentic research assistant

Phase 1 is a set of open-source agent tools that any lab can run on Claude, ChatGPT, Gemini or another model. The lab chooses where the model runs: a commercial model under its institution's data agreement, or an open model run locally for data that can't leave the lab. Nothing is hosted centrally; a shared, hosted record comes only with the optional run log in phase 2.

Much of this exists already. Frontier models read papers, code and modest datasets, and literature tools such as scite, Consensus and PaperQA2 already search and weigh the literature. What's new, and not built yet, is the condensed matter knowledge behind the tools and one workflow a researcher runs before submitting. Building that is phase 1.^1,2,3

The research assistant

• Reads what the researcher gives it: the paper, the figure data and the analysis code.
• Lists the known lookalikes for the claimed signal (the trivial explanations and artifacts that can produce the same signature), which ones the paper doesn't rule out, and which control would test each, with related work.
• Asks how many devices were measured, how many showed the effect, and why the others were set aside.
• Packages and tags the data behind the paper, so sharing it later takes one step.
• Writes a report for the researcher. It stays theirs: it goes to a journal or referee only if they send it, or, later, if a journal runs the assistant itself.

The referee assistant

A sibling tool for referees, hosted by the journal in its own approved deployment. Many journals prohibit referees from uploading manuscripts to AI tools, to protect confidentiality, so the referee assistant depends on journals offering it. I expect that to change.

How they know the field

Through an open library of known lookalikes and curated literature that anyone can extend. It starts in whichever subfield is most open to trying it, seeded from that subfield's public sources. I use topological transport as the example on this site because its lookalikes are well documented: there, the seed would be Frolov and co-authors' 2026 Science paper on four 'smoking gun' signatures and their non-topological explanations, the Pittsburgh report, and a 2020 review of Andreev and Majorana bound states. The library grows with each subfield that uses it.^4,5,6

Who curates that library is a question I want to raise. Beyond review articles, the field has no curated list of lookalikes, and curating data, not just papers, is work nobody is clearly paid or credited for. One path is a review of known lookalikes in a major journal, which gives its authors credit, kept current as a living review, the way Living Reviews in Relativity has authors update their articles. Longer term, journals could curate data as well as articles, as the American Economic Association's Data Editor already checks the data and code behind its papers.^7,8

Why start here

My hypothesis is that the software is cheap to build and asks nothing of a lab beyond the paper it already has. The library is the real work, which is why who curates it matters. Every use adds to it, and to the tagged data that phases 2 and 3 need.

What it costs

Philanthropy pays to build the tools and curate the library, researchers pay for their own model use, and a journal that offers the referee assistant pays for its own deployment. Since many of the pieces exist, the open question isn't whether this can be built. It's whether researchers want it and whether it can be kept going. They already use AI widely: in a 2025 survey of 2,430 researchers across fields, 84% used AI tools in their work, up from 57% a year earlier.⁹

My hypothesis is that running the assistant costs little next to current journal fees; a nonprofit certifier in France already reruns a paper's code and data for 500 euros. The larger cost is people's time, mostly answering the assistant's questions, so the goal is tools automated enough that this takes minutes.¹⁰