You are a research specialist. Your task is to take the research vision, related work and mission to draft a research roadmap that supports all the major claims and fairly compares against existing work.

Create Research Roadmap

Context Loading

Before creating the roadmap, understand the research context:

Read Research Journal: Load research-os/project/research-journal.md to understand:
- Final research vision and methodology
- Technical approach decisions
- Expected contributions and scope
Read Related Work: Load research-os/project/related-work.md to identify:
- Baseline methods to reproduce
- Standard evaluation protocols
- Datasets and benchmarks to use
- Existing implementations to reference
Read Mission: Load research-os/project/mission.md to understand:
- Hypothetical results to work toward
- Key claims that need validation
- Promised contributions to deliver

Generate Experiment Roadmap

Create research-os/project/roadmap.md with a dependency-based experiment plan.

Critical Requirement: Minimum Triage Experiment

ALWAYS start with a minimum triage experiment that validates core hypothesis viability with minimal investment (1-2 days maximum).

Roadmap Structure

Generate the roadmap following this template:

# Research Experiment Roadmap

## Overview
This roadmap outlines the experimental plan for validating [research hypothesis] and achieving the results outlined in our mission. The experiments are organized by dependencies, with each phase building on validated results from previous phases.

## Phase 0: Minimum Triage Experiment (Days 1-2)
**CRITICAL: This experiment determines go/no-go for the entire research project**

### Experiment 0.1: Core Hypothesis Validation
- **Objective**: Quickly test if [core assumption/mechanism] shows any promise
- **Duration**: 1-2 days maximum
- **Approach**:
  - Implement minimal version of [key innovation]
  - Test on small subset of [dataset] (e.g., 100 examples)
  - Compare against naive baseline (not full baseline)
- **Required Resources**:
  - Basic dataset sample (can use subset of [standard dataset])
  - Minimal compute (CPU or single GPU for few hours)
- **Baseline Comparison**:
  - Naive baseline: [simple approach, e.g., random, majority class]
  - Quick implementation of core idea
  - Check if improvement > [X%] over naive baseline
- **Success Criteria**:
  - [ ] Core mechanism produces non-random results
  - [ ] Shows [X%] improvement over naive baseline
  - [ ] Computation completes in reasonable time
  - [ ] No fundamental blockers discovered
- **Decision Gate**:
  - **GO**: If improvement is >= [X%] and mechanism works as expected
  - **PIVOT**: If mechanism works but needs adjustment
  - **NO-GO**: If fundamental assumption is invalid or no improvement

## Phase 1: Foundation & Baselines (Week 1-2)

### Experiment 1.1: Data Preparation & Analysis
- **Depends on**: Experiment 0.1 success
- **Objective**: Prepare and understand datasets for full experiments
- **Duration**: 2-3 days
- **Tasks**:
  - Download and preprocess [Dataset A] used in [Paper X]
  - Implement data loaders following protocol from [Paper Y]
  - Analyze data statistics and distributions
  - Create train/val/test splits per standard protocol
- **Deliverables**:
  - [ ] Clean, preprocessed datasets
  - [ ] Data analysis notebook with statistics
  - [ ] Documented data pipeline
- **Success Criteria**: Data matches reported statistics in [related papers]

### Experiment 1.2: Baseline Reproduction
- **Depends on**: Experiment 1.1 completion
- **Objective**: Reproduce key baseline results from related work
- **Duration**: 3-4 days
- **Implementation**:
  - Implement baseline from [Paper X]
  - Use official implementation if available: [repo link if known]
  - Follow exact hyperparameters from paper
- **Expected Results**:
  - Should achieve [metric] of [value] per [Paper X]
  - Acceptable margin: +/- [X%]
- **Success Criteria**:
  - [ ] Baseline achieves within [X%] of published results
  - [ ] Training is stable and reproducible
  - [ ] Results validated on standard test set
- **Fallback**: If can't reproduce exactly, document differences and proceed with our results as new baseline

## Phase 2: Core Method Development (Week 3-4)

### Experiment 2.1: Implement Novel Method
- **Depends on**: Validated baseline from 1.2
- **Objective**: Implement our proposed approach
- **Duration**: 5-6 days
- **Components**:
  - Core innovation: [specific technique/architecture]
  - Integration with baseline architecture
  - Key difference from [baseline method]: [what's new]
- **Implementation Milestones**:
  - [ ] Core module implemented and tested
  - [ ] Integration with baseline complete
  - [ ] Training pipeline adapted
  - [ ] Initial training runs successful
- **Success Criteria**: Method trains without errors and shows improvement over baseline

### Experiment 2.2: Hyperparameter Optimization
- **Depends on**: Experiment 2.1
- **Objective**: Find optimal configuration for novel method
- **Duration**: 3-4 days
- **Search Space**:
  - Learning rate: [range based on related work]
  - Model size: [options]
  - [Method-specific parameters]: [ranges]
- **Protocol**:
  - Grid/random search on validation set
  - Track all experiments with metrics
- **Success Criteria**:
  - [ ] Improvement of [X%] over baseline
  - [ ] Stable training across seeds

## Phase 3: Comprehensive Evaluation (Week 5-6)

### Experiment 3.1: Full Evaluation Suite
- **Depends on**: Optimized method from 2.2
- **Objective**: Evaluate on all standard benchmarks
- **Duration**: 3-4 days
- **Evaluation Protocol**:
  - Test on [Dataset A, B, C] used in related work
  - Report metrics: [metric 1, metric 2, metric 3]
  - Compare against baselines: [Method A, B, C from papers]
  - Multiple random seeds (minimum 3)
- **Expected Results** (from mission):
  - [Dataset A]: Achieve [metric] of [value], improving [X%] over [baseline]
  - [Dataset B]: Achieve [metric] of [value]
- **Success Criteria**:
  - [ ] Improvements are statistically significant
  - [ ] Results support claims in mission

### Experiment 3.2: Ablation Studies
- **Depends on**: Experiment 3.1
- **Objective**: Validate contribution of each component
- **Duration**: 2-3 days
- **Ablations**:
  - Without [component 1]: Test impact
  - Without [component 2]: Test impact
  - Different [design choice]: Compare alternatives
- **Success Criteria**:
  - [ ] Each component contributes as hypothesized
  - [ ] Results support design decisions

## Phase 4: Analysis & Additional Experiments (Week 7-8)

### Experiment 4.1: Failure Analysis
- **Depends on**: Experiment 3.1
- **Objective**: Understand where and why method fails
- **Duration**: 2-3 days
- **Analysis**:
  - Identify failure cases
  - Categorize error types
  - Compare failure modes with baseline
- **Deliverables**: Error analysis report with examples

### Experiment 4.2: Robustness Testing
- **Depends on**: Experiment 3.1
- **Objective**: Test robustness and generalization
- **Duration**: 2-3 days
- **Tests**:
  - Out-of-distribution samples
  - Adversarial examples (if applicable)
  - Different data conditions
- **Success Criteria**: Graceful degradation, better than baseline

### Experiment 4.3: Efficiency Analysis
- **Depends on**: Experiment 3.1
- **Objective**: Measure computational requirements
- **Duration**: 1-2 days
- **Metrics**:
  - Training time vs baseline
  - Inference speed
  - Memory requirements
  - Parameter count
- **Success Criteria**: Within [X%] of baseline efficiency or better

## Phase 5: Final Validation & Prep (Week 9)

### Experiment 5.1: Final Results Collection
- **Depends on**: All previous experiments
- **Objective**: Collect all results for paper
- **Duration**: 2-3 days
- **Tasks**:
  - Re-run best models with 5 seeds
  - Generate all plots and tables
  - Verify all numbers in mission
- **Deliverables**: Complete results package

### Experiment 5.2: Reproducibility Package
- **Depends on**: Experiment 5.1
- **Objective**: Ensure work is reproducible
- **Duration**: 2-3 days
- **Package Contents**:
  - Clean codebase with README
  - Trained model checkpoints
  - Evaluation scripts
  - Data preprocessing scripts
- **Success Criteria**: Fresh clone can reproduce key results

## Risk Mitigation & Contingency Plans

### High-Risk Elements
1. **[Risk 1]**: [Description]
   - Mitigation: [Plan]
   - Fallback: [Alternative approach]

2. **[Risk 2]**: [Description]
   - Mitigation: [Plan]
   - Fallback: [Alternative approach]

### Timeline Buffer
- Weeks 1-6: Core experiments (as outlined)
- Week 7-8: Buffer for delays, additional experiments
- Week 9: Final validation and writeup prep

## Dependencies Summary

Experiment 0.1 (Triage) ↓ (GO decision) Experiment 1.1 (Data Prep) → Experiment 1.2 (Baseline) ↓ Experiment 2.1 (Implementation) → Experiment 2.2 (Optimization) ↓ Experiment 3.1 (Evaluation) → Experiment 3.2 (Ablations) ↓ ↓ Experiment 4.1 (Analysis) Experiment 4.2 (Robustness) ↓ Experiment 5.1 (Final Results) → Experiment 5.2 (Reproducibility)


## Success Metrics

Overall project success requires:
- [ ] Minimum triage experiment shows promise (Phase 0)
- [ ] Baseline reproduction within acceptable margin (Phase 1)
- [ ] Novel method shows statistically significant improvement (Phase 2)
- [ ] Results support mission claims (Phase 3)
- [ ] Ablations validate design choices (Phase 3)
- [ ] Work is reproducible (Phase 5)

Important Constraints

Start with triage: ALWAYS begin with minimum triage experiment
Build on validated foundations: Each phase depends on previous success
Reference related work: Baselines and protocols from discovered papers
Realistic timelines: Account for debugging, iteration, and compute time
Clear decision gates: Explicit success criteria and go/no-go decisions

Completion

After creating the roadmap:

echo "✓ Created research-os/project/roadmap.md with dependency-based experiment plan"
echo "Roadmap contains $(grep -c "^### Experiment" research-os/project/roadmap.md) experiments across $(grep -c "^## Phase" research-os/project/roadmap.md) phases"
echo "Minimum triage experiment defined for go/no-go decision"

create-experiment-roadmap