1. Does a green manifest check prove reproducibility?
参考解答と評価観点
No. It checks internal declarations. Verify code, data versions, environment, execution and agreement with the target protocol.
論文再現ワークベンチ
Record a falsifiable hypothesis, inspect controls and export a reviewable experiment report.
ローカル計算と合成データです。有料 API や実モデルのベンチマークは使用しません。技術本文は英語と中国語に対応しています。この言語では英語の技術本文を表示します。
Study guide & lesson outline
LEARNING CONTRACTUnderstand → implement → investigate
Goal: explain the mechanism in this lesson, reproduce one example and test a failure case. Reading alone does not mark mastery.
- Understand: state assumptions, shapes/units and a counterexample.
- Implement: reproduce a numerical example and inspect boundaries.
- Investigate: compare one alternative under a matched budget.
01 · 理解と導出
前提知識 · Experimental controls, versioning, paired metrics.
A reproduction claim needs a traceable relationship between the paper, implementation, data and observations. The workbench checks declared fields, budget equality and top-level changed configuration keys. It never certifies that the declarations are true.
Text / 原始公式
Hypothesis → baseline → one-factor intervention → paired runs → uncertainty → limitations Required: paper, hypothesis, metric, direction, threshold, datasetVersion, splitHash, baseline, treatment, runs
Step-by-step derivation and interpretation
- Choose one falsifiable paper claim and define what observation would contradict it. Separate exact reproduction, approximate reproduction with declared deviations, and testing generalization.
- Freeze dataset/split identifiers, implementation commit, environment, evaluation script, checkpoint selection and search budget. Record failures and exclusions as well as successful runs.
- A manifest validates declared consistency, not provenance. Matching budget numbers cannot establish matching compute, and one changed top-level JSON key may hide multiple interventions.
Common pitfall: Do not mark a study as successfully reproduced because a checklist is green. Compare the actual protocol and observations with the original claim.
02 · 理解を確認
A baseline and treatment share seed IDs [1,2,3,4,5]. How many paired run observations enter the analysis?
導出を表示
Five pairs, not ten independent paired observations.
03 · 対照実験を実行
実験課題
Replace the demo metadata with actual identifiers, paste paired seed results, then introduce a second configuration change or budget mismatch. Export the report and explain which concerns remain manual.
Controlled experiment protocol
- Replace demo paper and metadata with the actual protocol. Use real dataset versions and source commit identifiers; retain the explicit metric direction.
- Paste independent paired run means. Change budget or a second top-level configuration key to inspect review flags; explain any justified deviations in the report.
- Export JSON and Markdown. Attach raw logs separately and document claim, method, controls, uncertainty, failure cases and the narrowest conclusion supported by your evidence.
Deliver: input configurations, exported results, controlled variables, observations, alternative explanations and the next validation step.
ヒントを表示
Equal step counts need not imply equal compute. Hashes and commit IDs are only declarations here. Read the paper and inspect the actual split membership before claiming successful reproduction.
04 · 根拠を確認
実行すると結果が表示されます。
条件を統制して実行結果を比較してください。出力に入力と手法を含みます。
05 · 推論を説明
自己評価：仕組みと前提を説明し、仮説を区別できる実験を提案してください。LLM 自動採点はありません。
2. How should a negative reproduction result be reported?
参考解答と評価観点
State the attempted protocol, deviations, budget, uncertainty and failed hypotheses; do not hide null results or assume the original claim is false.
3. What separates reproducibility from generalization?
参考解答と評価観点
Reproducibility repeats a result under a specified setup; generalization tests transfer to different data, environments or tasks.
前提と限界
No paper fetch, cloud training or provenance verification. The included runs are synthetic. Nested configuration differences are counted by top-level key; several causal changes can hide inside one key. Manual review remains required.
参考文献
arXiv:1709.06560 · 2017
Deep Reinforcement Learning that Matters
Henderson et al. · Year denotes first arXiv submission
Read: Sources of experimental variability and reporting recommendations.
Reading checkpoint: Separate randomness from implementation and tuning differences. RL findings motivate controls; do not blindly transfer its numerical results to LLMs.
BibTeX
@misc{arxiv170906560,
title = {Deep Reinforcement Learning that Matters},
year = {2017},
eprint = {1709.06560},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/1709.06560}
}arXiv:1909.03004 · 2019
Show Your Work: Improved Reporting of Experimental Results
Dodge et al. · Year denotes first arXiv submission
Read: Expected validation performance under a computational budget; model selection.
Reading checkpoint: Report search space, trial count and selection rule alongside the final score.
BibTeX
@misc{arxiv190903004,
title = {Show Your Work: Improved Reporting of Experimental Results},
year = {2019},
eprint = {1909.03004},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/1909.03004}
}Assumptions, implementation & acceptance
No paper fetch, cloud training or provenance verification. The included runs are synthetic. Nested configuration differences are counted by top-level key; several causal changes can hide inside one key. Manual review remains required.
Objective, derivatives, worked examples and implementation reviewed. A passing finite-difference or manifest check is not a paper reproduction certificate.
Before running: predict a boundary case
Replace the demo metadata with actual identifiers, paste paired seed results, then introduce a second configuration change or budget mismatch. Export the report and explain which concerns remain manual.
Hint: isolate the assumption
Keep the baseline input fixed, change one parameter or boundary, and compare both the numerical result and the claimed units. A passing example verifies this fixture only.
Minimal executable example
Save the example and linked modules in one folder. Use Node.js 22 or newer, then run node paper-reproduction.mjs. No third-party runtime dependencies.
import {runPhd} from './phd-core.mjs';
const params={
"manifest": "{\n \"paper\": \"https://arxiv.org/abs/2106.09685\",\n \"hypothesis\": \"Increasing rank improves a held-out metric at matched training budget.\",\n \"metric\": \"accuracy\",\n \"direction\": \"higher\",\n \"threshold\": 0.01,\n \"datasetVersion\": \"toy-v1\",\n \"splitHash\": \"demo-split-not-a-real-hash\",\n \"baseline\": {\n \"config\": {\n \"rank\": 2,\n \"learningRate\": 0.1\n },\n \"budget\": 1000,\n \"budgetUnit\": \"optimizer steps\"\n },\n \"treatment\": {\n \"config\": {\n \"rank\": 4,\n \"learningRate\": 0.1\n },\n \"budget\": 1000,\n \"budgetUnit\": \"optimizer steps\"\n },\n \"environment\": {\n \"codeCommit\": \"fill-with-real-commit\",\n \"runtime\": \"record exact runtime\",\n \"hardware\": \"record device\"\n },\n \"runs\": [\n {\n \"seed\": 1,\n \"a\": 0.72,\n \"b\": 0.75\n },\n {\n \"seed\": 2,\n \"a\": 0.74,\n \"b\": 0.76\n },\n {\n \"seed\": 3,\n \"a\": 0.71,\n \"b\": 0.75\n },\n {\n \"seed\": 4,\n \"a\": 0.75,\n \"b\": 0.76\n },\n {\n \"seed\": 5,\n \"a\": 0.73,\n \"b\": 0.76\n }\n ]\n}"
};
console.log(runPhd('paper-reproduction',params));Test and transfer
Replace the demo metadata with actual identifiers, paste paired seed results, then introduce a second configuration change or budget mismatch. Export the report and explain which concerns remain manual.
Record input, code version, expected result and observed result in the notebook below. Then construct a counterexample that violates the assumption above and explain why the original conclusion no longer follows.
Download complete kit (.tar.gz) · Complete reference kit and regression checksPredict, test and keep evidence
Before revealing an answer, predict one result. After the exercise, record the discrepancy and a transfer test. Saved locally across language editions.
Open notebook / export / clear →The mathematical review scope is recorded per lesson. It does not establish empirical paper reproduction; report suspected errors in your notes. Review scope