An AI Interface for Research Papers - by Justin Ross
Capital & Capitol
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An AI Interface for Research Papers<br>The Research Paper Isn't Dead (Yet!)
Justin Ross<br>May 22, 2026
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I have a new working paper out (with Whitney Afonso and Denvil Duncan) that does something I haven’t seen before in a research paper: a Model Context Protocol (MCP) that provides a structured way of interacting with the paper via a Large Language Model. I’m going to show you first what I did, and then tell you what I think the opportunities are available at present for scientific research papers.<br>Without specifics, our paper introduces a pair of randomized treatments (Priority and Performance) to elicit people’s stated preferences. We do all the normal things of a research paper: collect data about the survey respondents, show various tables, figures, and regression results. For regression results, we show you our “preferred” main specifications and selected robustness checks.<br>Thanks for reading Capital & Capitol! Subscribe for free to receive new posts.
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What is new is that I’ve built a MCP to allow users to interact with the data using natural language to run other regressions we either didn’t show in the paper or didn’t even think of. It also allows you to create new plots of the data, subset it for other balancing tests, and a bunch of other things (see the model notes for more).<br>To get a sense of this, suppose you go to the paper’s Github, download the data and server files, and follow the steps to connect the MCP to your favorite LLM. The MCP is built knowing what we have done in the paper, and with the flexibility to do new things.<br>You can now ask your LLM questions about the data and code. For example, “What’s the mean budget allocation by treatment arm in the practitioners sample?”
Here is a new regression specification that we did not think of until after I built the MCP, what if we interacted one of the treatment variables with the dummy variable on respondent sex? Here:
Now you know, and so do I.<br>What Does This Mean For the Research Paper?
Good research papers do a lot of important work. A paper states the research question. It forces the author into a coherent argument. It explains the institutional context, identification strategy, data, results, and limitations. It gives readers a canonical version of what the author believes has been shown, and what questions are reasonable to ask of the evidence.<br>Tyler Cowen recently asked whether AI will kill the research paper. Instead of reading a fixed PDF, a reader might press a button and update the paper with new data, rerun it under five alternative specifications, or turn it into a “meta-paper” that answers nearly any question about the subject.<br>I think something like that may eventually happen, but I worry it loses advantages offered by the research paper. Would literatures where motivated reasoning is strong (e.g. maybe minimum wages?) really improve here? I don’t know. In any case, a more immediate opportunity feasible right now is building a MCP as I depicted above. I think it offers efficient opportunities in replication and investigation, while retaining many of the advantages of the research paper design.<br>AI-Powered Replication
Empirical papers sit on top of a large apparatus of raw data, cleaned data, code, model choices, robustness checks, judgment calls, false starts, and dozens or hundreds of alternative specifications that never make it into the published tables.<br>Even when authors post replication packages, using them usually requires a reader to know the programming language, have compatible software and version control, understand the directory structure, install dependencies, decode the author’s conventions, and infer how the published tables relate to the files.<br>That is an interface problem: there are high costs to engaging with the research paper.<br>A MCP is an open standard that allows AI applications to connect to external data sources, tools, and workflows. The official MCP documentation describes it as something like a USB-C port for AI applications: a standardized way for an AI assistant to communicate with databases, files, APIs, and specialized tools rather than relying only on whatever is pasted into a chat window.<br>For research papers, this means the paper can come with a companion server. The server exposes the underlying data and analysis pipeline in a structured way. The reader does not need to learn the author’s coding conventions before asking a substantive question. The reader asks in ordinary language, and the AI assistant translates that question into verified analytical operations.<br>In my new paper, the reader can ask via their LLM “Replicate Table 1 for the mTurk sample.”<br>Or:<br>“Does the framing effect differ by respondent gender?”<br>The AI assistant then routes the request to the appropriate tool in the MCP server, executes the analysis, and returns the result. The local server we have built already exposes...