Research_Workflow - zfifteen/unified-framework GitHub Wiki

🧪 Methodology: AI-Assisted Research Workflow in the Unified Framework

1. Researcher Role (PI – Principal Investigator)

The human researcher (PI) defines the scientific direction, including:

  • Establishing core invariants (e.g., $c = e^2$, φ-modular transforms).
  • Designing falsifiable hypotheses (e.g., prime clustering under curvature warp).
  • Setting milestones (prime prediction accuracy, entropy reduction, cross-domain embeddings).

The PI acts as orchestrator, not line-coder — shaping the program’s scientific architecture.

2. AI Agents as Research Apprentices

AI assistants (GitHub Copilot, ChatGPT, etc.) serve as junior researchers, handling:

  • Drafting pull requests (PRs) aligned with PI prompts.
  • Generating experimental modules (e.g., prime prediction pipelines, cryptographic embeddings).
  • Producing documentation, plots, and simulation scaffolding.
  • Iterating quickly on theoretical variations (e.g., geodesic filters, wave-CRISPR metrics).

All AI output remains under human review and curation before merging.

3. Pull Requests as Research Experiments

Each PR represents a discrete experiment, treated like a lab notebook entry:

  • Exploratory PRs: Drafts for novel ideas or cross-domain expansions.
  • Validation PRs: Test harnesses, statistical checks, falsification gates.
  • Integration PRs: Refining theory, improving scaling, or consolidating documentation.

PR metadata (title, tasks, labels) functions as the experiment protocol.

4. Iterative Curation Loop

  1. Prompt/Instruction: PI encodes a problem (e.g., “validate Z5D prediction at $n=10^6$”).
  2. AI Generation: Copilot produces draft code, tests, or documentation.
  3. Human Curation: PI reviews, edits, and aligns with research aims.
  4. Merge & Record: Accepted PRs are merged, becoming permanent archival research records.
  5. Refinement: Results feed back into the invariant definitions, prompting new experiments.

This creates a living knowledge system, continuously refined by human-AI collaboration.

5. Open Science & Reproducibility

  • All experiments (successful or failed) are recorded transparently via PR history.
  • Validation pipelines (KS tests, KL divergence, entropy checks) are integrated to ensure falsifiability.
  • External reviewers can trace results directly to code, plots, and logs, ensuring reproducibility.

6. Advantages of This Workflow

  • Scalability: Parallel AI-authored PRs accelerate exploration.
  • Traceability: Every experiment is documented in version control.
  • Falsifiability: Built-in statistical tests gate ungrounded claims.
  • Cross-Domain Flexibility: Workflow applies equally to number theory, cryptography, physics, and even bioinformatics expansions.

✅ Summary This methodology reframes the repo as an AI-augmented research lab, where the PI orchestrates scientific direction and AI agents contribute draft experiments. Pull requests serve as modular, falsifiable research units, building a cumulative open-science framework.