As a solo researcher working on a cross-disciplinary theory (DCS - Dynamics of Causal Structure), I have developed a practical methodology for using AI as a research collaborator. I organize my AI tools into four roles: Literature Reviewer:...
As a solo researcher working on a cross-disciplinary theory (DCS - Dynamics of Causal Structure), I have developed a practical methodology for using AI as a research collaborator.
The AI Virtual Research Institute
I organize my AI tools into four roles:
Literature Reviewer: Scan cross-disciplinary literature, extract relevant arguments, mark controversies.
Logic Reviewer: Check argument chains, mark logical jumps and circular reasoning.
Devil Advocate: Construct the strongest counterarguments.
Writing Assistant: Organize materials, generate first drafts.
What AI Cannot Replace
Core theory construction: The central thesis emerged from human insight.
Philosophical judgment: What is an important question vs a superficial analogy?
Commitment to truth: AI generates plausible arguments, but only humans admit when wrong.
Practical Tips
Always ask AI to cite sources, then verify.
Use AI for breadth, human for depth.
Keep a contradiction log - when AI finds contradictions, do not dismiss them.
Version control your theory with git.
The Result
This methodology produced DCS: ~200,000 words of theory, ~400,000 words of research materials, published on 6 academic platforms with DOI and ORCID.
Paper: https://zenodo.org/records/22709952
DOI: 10.5281/zenodo.22709952
Contact: contact@mindas.me / +86 18826562299
Global Online Release Event
Join the "Finding the First Principle of Evolution" global online release on September 16, 2026:
English session (Beijing time): 09:00 - 11:30
Chinese session (Beijing time): 19:30 - 22:00
This is a non-commercial, theory and science communication event. The full DCS framework - from causal sets and causal emergence, through life and brains, to civilization and AI - will be presented across 130+ presentation slides.
Website: mindas.me
Contact: contact@mindas.me
The goal is not to claim ultimate truth, but to open a question space: can we use one causal language to study why new layers of complexity emerge again and again across 13.8 billion years? And if the next layer is human+AI, where do we stand right now?