Ablation Planner

# Ablation Planner

Systematically design ablation studies that answer the questions reviewers will ask. The reviewer agent leads the design; the local executor reviews feasibility and implements.

## Context: $ARGUMENTS

## When to Use

- Main results pass `/result-to-claim` with `claim_supported = yes` or `partial`
- The user explicitly requests ablation planning
- `/auto-review-loop` identifies missing ablations

## Workflow

### Step 1: Prepare Context

Read available project files to build the full picture:

- Method description and components (from `docs/research_contract.md`, project notes, or method docs)
- Current experiment results (from `EXPERIMENT_LOG.md`, `EXPERIMENT_TRACKER.md`, or W&B)
- Confirmed and intended claims (from `/result-to-claim` output or project notes)
- Available compute resources (from server notes, run configs, or user-provided budget)

### Step 2: Codex Designs Ablations

```text
spawn_agent:
  model: gpt-5.5
  reasoning_effort: xhigh
  message: |
    You are a rigorous ML reviewer planning ablation studies.
    Given this method and results, design ablations that:

    1. Isolate the contribution of each novel component
    2. Answer questions reviewers will definitely ask
    3. Test sensitivity to key hyperparameters
    4. Compare against natural alternative design choices

    Method: [description from project files]
    Components: [list of removable or replaceable components]
    Current results: [key metrics from experiments]
    Claims: [what we claim and current evidence]

    For each ablation, specify:
    - name: what to change (for example, "remove module X", "replace Y with Z")
    - what_it_tests: the specific question this answers
    - expected_if_component_matters: what we predict if the component is important
    - priority: 1 (must-run) to 5 (nice-to-have)

    Also provide: