Erdős #257, explained
Plectis
I’m Will Cook. I use AI to research Erdős problems, questions posed by the mathematician Paul Erdős, and I build tools that help people follow what AI produces.
Videos
From INTRODUCTION: Using AI for maths research and software: See the code
From INTRODUCTION: Using AI for maths research and software: Explore my research
From INTRODUCTION: Using AI for maths research and software: Read OpenAI’s results
From INTRODUCTION: Using AI for maths research and software: Guide the agents
About each project and video
CodeMap: follow a file from its description to its code, 6 minutes 40 seconds
Still in development in my private system. I'm in the process of open sourcing it.
More about this project
Links from the video description
One Checkout: save one AI agent’s fix while another edits the same file, 5 minutes 27 seconds
More about this project
Links from the video description
Maths Map: how proofs, papers and explanations connect, 6 minutes 12 seconds
Watch the video: Maths Map: how proofs, papers and explanations connect, 6 minutes 12 secondsExplore the maths mapRead the papersResearch on particular cases and related results. The original questions remain open where the problem pages say so.
More about this project
Checks and records
Links from the video description
Maths map (choose a problem, paper or proof)Papers (short explanations and longer investigations)Plectis Erdős (research repository)Lean proofs (formal statements and checks)Formal Conjectures (which contributions were merged or remain open)#257 contribution (a result accepted into Formal Conjectures)Palomar (registration and checks for selected results)Erdős #257: why some infinite sums cannot be fractions, 30 minutes
Papers, proofs and sources
The reciprocal-summability resultThe broader weighted criterionOriginal question (Erdős Problems)Short paper (the precise results and arguments)Long paper (the wider investigation and failed approaches)Maths map (find the statement and its proof)Research repository (papers and source)Lean proofs (formal statements and checks)Formal Conjectures (merged contribution for this result)Independent replay (review of the linked proof revision)Chapters
Plectis Atlas: compare maths papers with the reports that check them, 5 minutes 27 seconds
Watch the video: Plectis Atlas: compare maths papers with the reports that check them, 5 minutes 27 secondsExplore AtlasMore about this project
Links from the video description
Safety Core: rules and checks for AI coding agents in one small test, 8 minutes 47 seconds
A prototype for Claude Code. The commit experiment ran the agent with the hooks installed. A separate publishing example calls the hook directly to compare asking for approval with refusing a command; that example runs no agent and publishes nothing.
More about this project
Links from the video description
Zenith: can an AI tester learn from the bugs it missed?, 4 minutes 55 seconds
Watch the video: Zenith: can an AI tester learn from the bugs it missed?, 4 minutes 55 secondsFilmed experiment: results and replay instructionsPaper for the filmed experiment (PDF)Broader Zenith research websiteSmall research studies, with limits described in the repository. The film explains the experiment on learning from earlier missed bugs.
More about this project
Links from the video description
The sibling site
Plectis Atlas
Two reports can give a paper different labels because they assess different claims. In Plectis Atlas, you can open the paper, the statement chosen for computer checking and each group’s explanation of its verdict. Atlas is independent of OpenAI.
Why I’m building this
As AI produces more complicated code and research, following its work gets harder. I’m using AI to build explanations and interfaces that help people inspect that work.
I keep the research, papers, code and recordings together in my working system. An agent writing a short paper can read the long research record, and a coding agent can read the same saved file descriptions that I see in Code Map.
AI does much of the research and coding. I direct the work, record the explanations and review the results. When an agent edits a video, it can read my transcript alongside the papers and code. I’d like to develop this with researchers and lecturers who can judge the subject properly.
These videos show what I’ve learned to build. I’d love to put those skills to use on other problems. I’m looking for work, an internship, fellowship or funding, and I’m very open to working together. If there’s a problem you think I should try, please let me know.
If something doesn’t make sense, or you spot a mistake, please let me know. If your feedback leads to a change, I’d be happy to credit you, with your permission.
The eight problems
The examples below introduce each question. For a selected result, you can open its assumptions, proof files and the saved test of the proof against a separately supplied statement.
Each problem has a short paper, which contains the compressed ideas, and a long paper, which is a reasoning record of all the failed routes and attempted avenues. The three below are good places to start.
-
Research paper, 26 pages Irrationality criteria for Lambert subseries
-
The research system
A Repository-Based System for Research and Publication
Companion, 22 pages Plectis: What a Stranger Can Check
-
AI models that test code
Zenith: programs, experiments and useful memory
These author-released papers are not externally peer reviewed and have no DOI. Lean checks statements; readers judge novelty and significance.
Explore the work
More about the mathematics and its sources
I started investigating these problems with AI in early 2025.
In September 2026, Silvia De Toffoli and Eamon Duede wrote about the difference between a valid proof and one people can understand. Henry Cohn explained how unclear AI proofs can leave other people a lot of work.
The longer account: how this started and what I am trying to do
Explore with an AI assistant
Download the AI packet (JSON)Reading starter and source routes
Attach it to an AI chat and ask for a tour. It includes paper text and excerpts, video transcripts, repository READMEs and website introductions, with links to the complete sources.
- A detailed source brief (JSON) keeps paper text, glossary and guides.
- For a smaller file, the overview digest (JSON) leaves out full paper and page text.
For AI readers and coding agents
JSON is a structured text format an assistant can search by paper, problem, term or source path. No coding app is needed to read it. Each download is a snapshot of the public material, with source references; it does not contain the complete Git repositories, PDF files or video files. Use the links for those. Reading a recorded check does not rerun it.
Setup prompt for a coding agent
Choose the project above and open its own code link. Each repository has its own setup instructions; this prompt helps an assistant start there.
My task is [describe the question or change]. Start with the relevant project at https://wcook04.github.io/practical-formal-verification/ and follow its code link to the project’s own repository. For the mathematics, use https://github.com/wcook04/plectis-erdos and follow its links to the computer-checked Lean proofs. The older https://github.com/wcook04/practical-formal-verification repository is a separate toolkit, not the source of every project. Clone only the repositories this task needs from current main and preserve existing local work. Read README.md and AGENTS.override.md or AGENTS.md where present. Follow that repository’s setup and checks. Record the checkout commit and local changes. Return the source paths, exact commands and outcomes, and any assumptions or unfinished steps. Say clearly when you could not run a check. A release tag is a frozen edition, not current main.
Take part
If you prove a result, it’s yours. I won’t claim priority for it.
Email me Work on a problem Credit policy
For partnership and licensing enquiries, see the contact page.
Credit ledger
What people told me, what changed because of it, and where to see the change. Names stay withheld until each person confirms, and thanks here doesn’t mean they checked the mathematics.
-
A researcher advised showing a clear advantage over existing systems when asking people to use the work.
What changed: The communication rules now require an inspectable comparison and evidence for a claimed advantage, while keeping an invitation to look distinct from a completed review.
-
A researcher challenged the framing of the system as an external improvement to rapidly advancing models.
What changed: The explanation now distinguishes persistent records for inspection and continuation from a model’s problem-solving capability; the distinction is also published in the credit policy.
-
A mathematician said the #243 note used names that meant nothing on a first or second reading, defined notation it used once, and never said how restrictive its main hypothesis was.
What changed: All sixteen problem papers were rewritten for a first-time reader, naming standard tools and saying how restrictive each hypothesis is, and the public writing rules now require it.
-
A mathematician said the trouble with AI-assisted mathematics is that it rarely shows where its methods and ideas come from.
What changed: A literature review was run for each of the eight problems, and each paper now credits its sources at the point where they are used.
-
A mathematician advised leading the #249 paper with its exact finite-level theorem, explaining the mechanism behind it, and comparing it precisely with the existing literature on regular sequences.
What changed: The #249 paper now opens with the exact rank and basis, compares the result with work of Allouche and Shallit, Coons and Martin, and links a minimal Lean entry; the irrationality question stays open.
-
A mathematician advised listing each selected result with its exact statement, proof source, novelty status, axioms and limits, and giving reviewers a cheap way to check the statements themselves.
What changed: formalization.yaml now lists the selected results this way, and Comparator checks them against separately written statements, including an altered statement that must be rejected.
-
A mathematician saw no connection between their specialist methods and the question presented to them.
What changed: That connection was removed from future correspondence unless new evidence supports it; the published credit policy records the same rule for correcting scope.
-
A mathematician suggested comparing the #1049 construction with an earlier irrationality paper.
What changed: The comparison was carried out: a source note and a Lean module recording where the two methods part are public, and the #1049 record cites the paper where they meet.
-
A reader could not identify the system’s goal, the object being shown, or why its interface was useful.
What changed: The correspondence rules now require one concrete object, its purpose, a narrow request and a clear reading or time cost; those rules are also published in the credit policy.
9 entries, newest first. The exact lines and commits for each are in the full credit ledger.
Agent acknowledgements
Agent feedback, checks and implementation contributions across the public repositories are recorded separately. Read the acknowledgements and sources.




