ablation-planner

name ablation-planner description Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission. argument-hint [method-description-or-claim] allowed-tools Bash(*), Read, Grep, Glob, Write, Edit, mcp__codex__codex, mcp__codex__codex-reply Ablation Planner Systematically design ablation studies that answer the questions reviewers will ask. Codex leads the design (reviewer perspective), CC reviews feasibility and implements. Context: $ARGUMENTS When to Use Main results pass /result-to-claim with claim_supported = yes or partial User explicitly requests ablation planning /auto-review-loop reviewer identifies missing ablations Workflow Step 1: Prepare Context CC reads available project files to build the full picture: Method description and components (from idea-stage/docs/research_contract.md , legacy docs/research_contract.md , or project CLAUDE.md) 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 CLAUDE.md server config, if present) Step 2: Codex Designs Ablations mcp__codex__codex: model: gpt-6-astra config: {"model_reasoning_effort": "xhigh"} prompt: | 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/replaceable components] Current results: [key metrics from experiments] Claims: [what we claim and current evidence] For each ablation, specify: - name: what to change (e.g., "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: - coverage_assessment: what reviewer questions these ablations answer - unnecessary_ablations: experiments that seem useful but won't add insight - suggested_order: run order optimized for maximum early information - estimated_compute: total GPU-hours estimate Step 3: Parse Ablation Plan Normalize Codex response into structured format: ## Ablation Plan ### Component Ablations (highest priority) | # | Name | What It Tests | Expected If Matters | Priority | |---|------|---------------|---------------------|----------| | 1 | remove module X | contribution of X | performance drops on metric Y | 1 | | 2 | replace X with simpler Z | value of learned vs fixed | drops, especially on dataset A | 2 | ### Hyperparameter Sensitivity | # | Parameter | Values to Test | What It Tests | Priority | |---|-----------|---------------|---------------|----------| | 3 | lambda | [0.01, 0.1, 1.0] | sensitivity to regularization | 3 | ### Design Choice Comparisons | # | Name | What It Tests | Priority | |---|------|---------------|----------| | 4 | joint vs separate matching | whether joint adds value | 4 | ### Coverage Assessment [What reviewer questions these ablations answer] ### Unnecessary Ablations [Experiments that seem useful but won't add insight — skip these] ### Run Order [Optimized for maximum early information] ### Estimated Compute [Total GPU-hours] Step 4: CC Reviews Feasibility Before running anything, CC checks: Compute budget: can we afford all ablations with available GPUs? Code changes: which ablations need code modifications vs config-only changes? Dependencies: which ablations can run in parallel? Cuts: if budget is tight, propose removing lower-priority ablations and ask Codex to confirm Step 5: Implement and Run Create configs/scripts for each ablation (config-only changes first) Smoke test each ablation before full run Run in suggested order, using descriptive names (e.g., ablation-no-module-X ) Track results in EXPERIMENT_LOG.md After all ablations complete → update findings.md with insights Rules Codex leads the design. CC does not pre-filter or bias the ablation list before Codex sees it. Codex thinks like a reviewer; CC thinks like an engineer. Every ablation must have a clear what_it_tests and expected_if_component_matters . No "just try it" experiments. Config-only ablations take priority over those needing code changes (faster, less error-prone). If total compute exceeds budget, CC proposes cuts and asks Codex to re-prioritize — don't silently drop ablations. Component ablations (remove/replace) take priority over hyperparameter sweeps. Do not generate ablations for components identical to the baseline (no-op ablations). Record all ablation results in EXPERIMENT_LOG.md, including negative results (component removal had no effect = important finding).