Problem framing
Translate a phenomenon, mechanism intuition, or target claim into a research object with clear scope.
Projects
Each project outlines its substantive question, current research stage, related themes, and participating collaborators.
AI-native workflow
The workflow is based on a preliminary, under-review manuscript about human-in-the-loop agentic research for theory-oriented economics. It is presented here as a project overview rather than as a public manuscript release.
Its purpose is not to automate paper writing end to end. Instead, it helps researchers turn early ideas into inspectable models, branch across alternative assumptions, revise arguments under internal checks, and prepare drafts for referee-style scrutiny.
Translate a phenomenon, mechanism intuition, or target claim into a research object with clear scope.
Generate candidate assumption packages and model branches that can support a disciplined economic argument.
Check logic, derivations, literature fit, boundary conditions, and whether conclusions outrun assumptions.
Weaken claims, substitute mechanisms, refine assumptions, and preserve promising alternatives for comparison.
Let the researcher select, prune, or retain branches based on originality, taste, and scholarly accountability.
Expose draft research objects to referee-style critique, then convert feedback into structured revision targets.
Active
Accepted at the ICML 2026 Workshop on Human-AI Co-Creativity. A human-in-the-loop agentic workflow for moving economic research from an initial question to defensible assumptions, model structure, argument revision, and referee-style critique.
Team: Heikichi Hayashi
Design
A work-in-progress project on the impact of super high school group-based schooling on students' human capital, developed in collaboration with Beijing National Day School.
Team: Heikichi Hayashi