---

mode: computational
discipline: computer-science
title: "<Working title: a computational study>"
---

# Research brief — <working title> (computational)

## 1. Research objective and scope

- **Question.** <e.g., Does method M improve metric K over baseline B on task T under budget C?>
- **Hypotheses.** <H1…; include the null.>
- **Compute scope.** CPU-only Apple Silicon node (16 cores, ≤ 32 GB RAM), ≤ <N> hours; no GPUs. Size experiments accordingly.

## 2. Domain, mode and methodology

- Discipline: <AI/ML/systems/algorithms/optimization>. Primary mode: computational.
- Methodology: <benchmark design, baselines, metrics, statistical tests across seeds>.

## 3. Contribution and novelty

- <Expected finding; the closest prior methods/benchmarks to compare with.>

## 4. Literature and source rules

- Indexes: arXiv + Semantic Scholar + Crossref. Target ≥ 12 verified sources; must-read: <ids>.

## 5. Benchmark and data

- Datasets/tasks: <public, licensed; exact versions/URLs; splits>. Leakage controls: <…>.
- Baselines: <at least two, including a strong simple one>. Implementation sources (cite).

## 6. Procedure

1. Environment lock in `code/` (uv), deterministic seeds (≥ 5 seeds), fixed splits.
2. Implement method and baselines; unit-test core functions.
3. Run the pre-registered grid; log every run with commands, seeds, wall time, resources in `results/experiment-log.jsonl`.
4. Aggregate deterministically into `results/metrics.json`; produce tables/figures with CIs.
5. `code/reproduce.sh` regenerates everything.

## 7. Baselines, robustness and falsification

- Ablations: <components removed one at a time>. Sensitivity: <hyper-parameters, data size>. Report when the method loses.

## 8. Reproducibility and artifacts

- Pinned environment, seeds, logs, metrics, figure scripts, resource usage table.

## 9. Manuscript

- AutoScholar class; algorithm pseudo-code; results tables with mean ± CI over seeds; 8–16 pages.

## 10. Review and revision

- Reviewer focus: unfair baselines, seed variance, leakage, cherry-picked metrics, missing ablations.

## 11. Limitations, ethics, legal

- <Compute limits, dataset licenses, generalization.>

## 12. Acceptance criteria

- All runs logged and reproducible; ≥ 5 seeds; ≥ 2 baselines; ≥ 2 ablations; reported numbers traceable; ≥ 12 verified citations.