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AI-Assisted Research Workflow

Status: 🔄 Ongoing
Type: Methodology Documentation

Overview

Systematic workflow for AI-assisted technical research combining structured problem solving with comprehensive documentation.

Core Principles

Human-AI Roles: - Human: Strategic thinking, domain expertise, experimental design
- AI: Systematic implementation, comprehensive analysis, documentation

Documentation Framework: - GitHub Issues: Problem tracking and experiment management - Pull Requests: Implementation documentation - Experiment Posts: Results and methodology

The Workflow

graph TD
    A[Problem Identified] --> B[Phase 1: Analysis]
    B --> C[Phase 2: Solution Design]
    C --> D[Phase 3: Implementation]
    D --> E[Phase 4: Experimentation]
    E --> F[Phase 5: Analysis & Iteration]


    %% Artifacts
    B --> I1[📋 GitHub Issue<br/>Problem Analysis]
    C --> I2[📋 GitHub Issue<br/>Solution Roadmap]
    D --> P1[🔀 Pull Request<br/>Implementation]
    E --> I3[📋 GitHub Issue<br/>Experiment Tracking]
    F --> E1[📝 Experiment Post<br/>Results & Analysis]

    %% Styling
    classDef phase fill:#e1f5fe
    classDef github fill:#fff3e0
    classDef post fill:#f3e5f5
    classDef monitor fill:#e8f5e8

    class B,C,D,E,F phase
    class I1,I2,I3,P1 github
    class E1 post
    class J1 monitor

Phase 1: Problem Analysis

Process: Human identifies problem, AI performs systematic root cause analysis
Artifact: GitHub Issue with problem statement and analysis
Example: Mode collapse → Issue #18

Phase 2: Solution Design

Process: Collaborative solution design with implementation roadmap
Artifact: Updated GitHub Issue with solution plan
Example: Training improvements design

Phase 3: Implementation

Process: AI implements solution with human oversight
Artifact: Pull Request with comprehensive documentation
Example: PR #19 - 200+ lines training infrastructure

Phase 4: Experimentation

Process: Structured experiments with real-time monitoring
Artifact: GitHub Issue tracking experiment + status updates
Example: Issue #20 - 100-epoch validation

Phase 5: Analysis & Iteration

Process: Collaborative analysis and next steps identification
Artifact: Experiment Post with results and methodology
Example: Success/failure analysis → next iteration

Key Benefits

Research Velocity: Parallel processing (AI systematic work + human strategy)
Quality Assurance: Comprehensive testing and documentation
Knowledge Building: Pattern recognition across experiments

Mode Collapse Case Study

  1. Problem: 1000-epoch model generates only punctuation
  2. Analysis: AI identified token bias, LR instability, loss imbalance
  3. Solution: Training improvements (LR scheduling, regularization, validation)
  4. Implementation: PR #19 - comprehensive training overhaul
  5. Validation: Issue #20 - 100-epoch test with real-time monitoring

Related: - Mode Collapse Prevention - Learned Rounding Implementation - Issues: #18 (Analysis), #20 (Experimentation) - Pull Request: #19 (Implementation)