rekursiv.ai

Founded by Joshua V. Dillon

Run autonomous scientific experiments to discover new machine learning algorithms and methods

Pricing model

Demo

Rating

★ People love it

Time to value

Requires Expertise

You can use rekursiv.ai to automate scientific research and invent novel machine learning algorithms. Instead of manually writing code and testing ideas, you define a research problem and let autonomous agent teams take over. The system designs experiments, sets evaluation benchmarks, runs thousands of tests, and interprets the results automatically. Multiple agents collaborate by proposing hypotheses, reviewing each other's work, and tracing every claim back to verifiable experimental data. This allows technical teams to find breakthrough methods faster and at significantly lower computational costs.

What rekursiv.ai does

Formulate scientific hypotheses based on existing codebasesRun automated machine learning experiments across large clustersReview and critique experimental findings using dedicated evaluator agentsTrace scientific claims directly to raw benchmark resultsCompare algorithmic performance against public research leaderboardsModify code to test alternative training pipelines automaticallyDocument discovery steps and rationale in structured research reportsAutonomous hypothesis generation for complex machine learning tasksAutomated experiment design and execution across multiple nodesPeer review system where agent teams critique and verify research findingsDirect evidence tracing linking every discovered conclusion to experiment logsAutomated leaderboard tracking for competitive evaluation benchmarksMulti-agent coordination for running distributed exploratory searchesCost tracking to discover efficient algorithmic methods with lower compute budgets