Paper Reproduction

Paper Reproduction

Six-step automated paper reproduction pipeline. One orchestrator (you) plus six specialized sub-agents. Agents communicate only through structured markdown files — they do not share conversation context.

Scope (v1)

v1 positioning: Python = full 6-agent reproduction with numerical comparison. R / MATLAB / Julia = minimal viable — identify, install, smoke-test, honest feedback. This is a deliberate scope choice: the Python path must be rock-solid before expanding to languages where error diagnostics are harder for AI to resolve.

Language Support

Detection is two-phase:

Minimal Path (R / MATLAB / Julia)

When a non-Python language is detected, do NOT block. Instead, write paper_reproduction_output/<paper_slug>/reduced_scope.md:

# Reduced Scope — Non-Python Repository

## Detected Language: R / MATLAB / Julia
## Evidence
- (dependency files found, README commands, etc.)

## What This Means
This paper's code is in {language}, which is outside Python — the skill's
full reproduction path. The skill will proceed on a **minimal path**:
1. ✓ Identify the language environment
2. ✓ Install packages (best effort)
3. ✓ Run a smoke test (does the code start?)
4. ✗ Full numerical reproduction is NOT guaranteed
5. ✗ Result comparison against paper claims is NOT guaranteed

## What the User Gets
- Honest feedback on whether the code runs
- Known blockers documented with concrete error messages
- TaskLog entries for all failures — reusable across papers

Then proceed to Step 1 normally. The orchestrator sets an internal flag language_scope: minimal and passes it to all agents. Agents adapt:

• Agent C (EnvironmentBuilder): Instead of creating a Python venv, install packages in the detected language (R: install.packages() from CRAN mirror; MATLAB: check ver for required toolboxes). Verify each package loads.
• Agent D (SmokeTester): Run a minimal "hello world" in the detected language (R: source("main.R") or equivalent; MATLAB: run('main.m')).
• Agent E (FullRunner): Attempt to run what's possible. Write a wrapper if needed. Report honestly what ran and what didn't.
• Agent F (ResultComparator): Note the reduced scope. List which paper claims could be checked and which couldn't. Do NOT fabricate comparisons.

Dataset Requirements

Paper must have a public dataset (Zenodo, Kaggle, Hugging Face, UCI, GEO, CELLxGENE, ArrayExpress, etc.). If no public dataset is found, mark it in the resource map and continue on a best-effort basis.

Two Reproduction Modes (Python only)

The orchestrator auto-detects which mode applies after Step 2. Only relevant for Python repositories — non-Python repos always use the minimal path above.

Mode detection is automatic — read "Step 2.5 — Detect Reproduction Mode" below. The orchestrator passes reproduction_mode: tool|experiment to Agent C and Agent E so they adapt their behavior.

Quick Reference: Six Agents

Read each reference file only when launching that agent. Do not load all six into context at once.

Orchestration Pipeline

Step 0 — Setup

1. Confirm the paper identity. If the user gave a PDF path, use it. If only a title, Agent A will search for the PDF.
2. Language pre-flight check: If the user provided a GitHub URL or package name, do a quick pre-flight before launching agents:
   • pip install X / PyPI mention / conda install → Python → full path
   • devtools::install_github() / install.packages() / BiocManager::install() / CRAN / Bioconductor → R → minimal path (write reduced_scope.md, set language_scope: minimal, continue)
   • .jl files / Julia mention / Pkg.add() → Julia → same minimal path
   • .m files / MATLAB / addpath() → MATLAB → same minimal path
   • No code repo / theory paper only → block (write scope_violation.md, STOP)
   • Unknown (no URL given, only a paper title) → proceed; Step 2 will confirm
3. Generate paper slug. From first-author-lastname + year: bergen2020, nitzan2024, etc. Strip diacritics, lowercase, no spaces. If year unknown, use 0000. This slug namespaces ALL downstream output so running multiple papers never overwrites each other's artifacts.
4. Create the output directory tree in the working directory (not inside the skill directory). The skill itself (SKILL.md + references/) should remain clean — paper_reproduction_output/ is a runtime artifact that lives alongside the skill, not inside it.

<working_directory>/
├── paper-reproduction/          ← skill body (SKILL.md + references/)
└── paper_reproduction_output/   ← runtime outputs (created below)
    └── <paper_slug>/            ← ONE SUBDIRECTORY PER PAPER
        ├── paper_reading/
        ├── env_setup/
        ├── run_outputs/
        │   └── outputs/
        ├── resources/
        ├── reproduction_report.md
        ├── reproduction_manifest.md
        ├── paper_reference_mismatch.md   (only if mismatch detected)
        ├── scope_violation.md            (only if non-Python detected)
        ├── reduced_scope.md              (only if R/MATLAB/Julia detected)
        ├── data_unavailable.md           (only if ALL data sources dead)
        └── degraded_mode.md              (only if web tools unavailable)

The orchestrator passes <paper_slug> to all agents so they write to the correct subdirectory. Each paper gets a fresh slug — if the same paper is re-tested after skill improvements, reuse the SAME slug so artifacts accumulate under one namespace.

5. Web availability check. Before launching Agents A or B, verify web tools are functional with a 2-second sanity check:

   Use the WebFetch tool (not a shell command) to probe GitHub: WebFetch https://github.com --prompt "Does this page load?"

   If web tools are available → proceed to Step 1.

   If web tools are unavailable → degraded mode. Write paper_reproduction_output/<paper_slug>/degraded_mode.md:

   # Degraded Mode — Web Tools Unavailable
   
   ## What happened
   WebSearch and/or WebFetch returned errors in this session (HTTP 400,
   ECONNREFUSED, or timeout). The reproduction pipeline itself
   (Steps 3-7) is unaffected, but the paper/code discovery phase
   (Steps 1-2) requires the user to provide inputs manually.
   
   ## What still works
   - Environment setup (Step 3)
   - Smoke test + time benchmark (Steps 4-4.5)
   - Full run (Step 5)
   - Result comparison (Step 6)
   - Merge and presentation (Step 7)
   
   ## What does not work
   - Automatic paper PDF search (Agent A's search path)
   - Automatic GitHub repo search (Agent B's search path)
   - Data URL discovery from paper references
   

   Present the degraded mode to the user:

   Web tools are currently unavailable. The reproduction pipeline itself
   is unaffected, but I cannot search for the paper PDF or GitHub repo.
   How would you like to proceed?
   
   A) I'll provide the PDF path and GitHub URL — continue in degraded mode
   B) Retry the web check (maybe a transient failure)
   C) Abort — try again when web tools are restored
   

   • If A: Ask the user for the paper PDF path (or accessible URL) and the GitHub repository URL. Pass these directly to Step 1 — Agent A reads the provided PDF, Agent B verifies the provided repo URL.
   • If B: Re-run the web check once. If still failing, fall through to A/C.
   • If C: BLOCKED. Document the situation and exit.

6. Determine the working directory. Prefer a dedicated paper_repro/ under the project root, or ask the user where.

Step 1 — Parallel Launch: PaperReader + ResourceFinder

Launch Agent A and Agent B simultaneously in one message with two Agent tool calls. Each agent receives only the context it needs — do not pass the full conversation history.

Agent A context to pass:

• PDF path (if given) or paper title
• Instructions from references/agent-paper-reader.md
• Target output: paper_reproduction_output/paper_reading/extracted_methodology.md

Agent B context to pass:

• Paper title and authors
• Any GitHub URLs already visible in the paper metadata
• Instructions from references/agent-resource-finder.md
• Target output: paper_reproduction_output/paper_reading/resource_map.md

These two agents are independent — Agent B does not need Agent A's output.

Step 2 — Validate A+B Outputs

After both agents complete:

1. Read extracted_methodology.md. Verify it contains:

   • Method description
   • Dependency list or requirements.txt reference
   • At least one claimed numerical result (for later comparison)
   • "Potential pitfalls" section

2. Read resource_map.md. Verify it contains:

   • GitHub repository URL (or explicit "not found")
   • Dataset download URL(s) (or explicit "not found")
   • Dependency installation notes

3. If either output is critically incomplete, re-launch that agent once with specific feedback on what's missing.

4. Paper-Repo cross-validation: Verify the paper and repo are about the same tool.

   • Read extracted_methodology.md → "Reproducibility Notes from Paper" → does the paper claim a code URL?
   • Read resource_map.md → does the repo's README/description cite the same paper?
   • Match: The paper title appears in the repo README, or the repo URL appears in the paper's code availability statement → proceed normally.
   • Mismatch or unverified: The paper claims repo X but the resource map found repo Y, or the repo README cites a different paper. Write a one-line warning to paper_reproduction_output/paper_reference_mismatch.md with the evidence (what the paper claims vs what the repo claims). Do not block reproduction — the user may have intentionally paired a paper with an alternative implementation. Continue with the repo that was actually found and flag the mismatch for Agent F.
   • Only one source has a URL: If only the paper or only the repo has a citation, note the asymmetry and proceed. Many papers pre-date their code release, and many repos don't cite the paper in their README.

5. Repo selection (when 2+ candidates available): If ResourceFinder found multiple GitHub repositories for the same paper, the orchestrator selects one using the following criteria, applied in priority order:

   Priority	Criterion	How to check
   1	Framework match	Does the repo use the same framework the paper describes? (PyTorch vs TensorFlow, scikit-learn vs homegrown). Read the repo README and requirements.txt / pyproject.toml.
   2	Dependency file present	requirements.txt, pyproject.toml, or setup.py exists — signals the environment can be reproduced.
   3	CI / verified badge	Green CI badge on README, or README says "reproduced" or "verified". Signals the code has been tested recently.
   4	Recent commit	Last commit within 2 years. A 6-year-old untouched repo may have bit-rotted dependencies.
   5	Original authors	Repo by paper authors preferred over third-party reimplementation — but only if criteria 1-4 are comparable.

   Document the selection in a one-line file: paper_reproduction_output/repo_selection.md with all candidates, scores per criterion, and final choice with rationale. Flag any deselected repos for Agent F so it can note that results may differ between implementations.

   If no repo passes criteria 1-3 → flag as a risk but continue. The user may know something we don't (e.g., the original TF repo still works even if it's 6 years old and has no CI).

   Example: GAT paper has 3 repos. Diego999/pyGAT scores highest (PyTorch=✓, requirements.txt=✓, green CI=✓, updated 2024=✓, third-party=✗). PyG built-in scores second (PyTorch=✓, no requirements.txt=✗). Original TF repo scores lowest (TF=✓ but 5 years stale=✗, no CI=✗). Select Diego999/pyGAT.

6. Language confirmation gate: Read resource_map.md → "Dependency Files Found" checklist. Confirm the language matches the pre-flight from Step 0. If the pre-flight was unknown (only paper title given), this is the definitive detection.

   • R signals: only DESCRIPTION + NAMESPACE (no setup.py/requirements.txt), README shows install.packages(), devtools::install_github(), library(), repo file extensions are .R/.Rmd/.rda
   • Julia signals: Project.toml with [deps] section (no pyproject.toml), README shows using PackageName, Pkg.add(), .jl files only
   • MATLAB signals: .m files only, no Python files detected If non-Python detected → write reduced_scope.md if not already done in Step 0. Set language_scope: minimal. Continue — do NOT block. If no code at all (theory paper) → write scope_violation.md and STOP.

Step 2.5 — Detect Reproduction Mode

Before launching Agent C, determine the reproduction mode by reading resource_map.md:

Experiment mode — the repo is structured as an experiment launcher:

• Contains main.py, train.py, run.sh, scripts/, or config files (.yaml, .jsonnet)
• README shows commands like python train.py --config ... or bash scripts/run.sh
• No pip install or python setup.py install as the primary usage path

Tool mode — the repo is a pip/conda-installable package:

• Root has setup.py, pyproject.toml with [project.scripts], or setup.cfg
• README shows pip install X or conda install -c conda-forge X as the first step
• Usage is Python API calls (import X; X.train()) rather than shell command experiments
• Repo may have a demo.py, tutorial.ipynb, or API docs but no main.py with --config

Record the decision in a one-line file: paper_reproduction_output/reproduction_mode.txt — content: tool or experiment.

Pass this mode to Agent C and Agent E so they adapt. Agent C needs it for install strategy (pip install -e . vs pip install -r requirements.txt). Agent E needs it for run strategy (write an API-calling reproduction script vs find and execute experiment shell commands).

Step 2.6 — Pre-Environment Gate Checks

Before launching Agent C, check two blocking conditions:

Gate A: Agent A found no usable paper content. If extracted_methodology.md contains ## PDF Status: NOT FOUND or the claims section is empty with no fallback content → degraded path.

• Write paper_reproduction_output/<slug>/degraded_mode.md documenting what was tried and why PDF acquisition failed.
• Continue with whatever content IS available (PMC summary, web search, etc.). The Key Claimed Results section may be sparser than normal — Agent F will note this in its comparison.
• Do NOT block — a partial reproduction is better than none.

Gate B: Agent B found NO usable data sources. If the resource map shows ALL datasets marked as [UNREACHABLE] or [UNVERIFIED] with zero working download URLs:

• Write paper_reproduction_output/<slug>/data_unavailable.md:

  # Data Unavailable
  ## Paper: {title}
  ## Status: BLOCKED
  All datasets for this paper could not be reached. This is likely a network
  infrastructure issue (GEO blocked, Zenodo timeout, etc.), not a skill bug.
  ## Attempted Sources
  | Dataset | URL | Error |
  |---------|-----|-------|
  

• STOP. Do not launch Agent C, D, E, or F. There is nothing to reproduce without data. Present the data_unavailable.md to the user and ask whether to proceed on a best-effort basis or pick a different paper.
• If at least ONE dataset has a working URL → proceed normally.

Step 3 — EnvironmentBuilder

Launch Agent C with:

• extracted_methodology.md content
• resource_map.md content
• reproduction_mode.txt content (so it knows install strategy)
• Instructions from references/agent-environment-builder.md
• Target output: paper_reproduction_output/env_setup/setup_report.md

Retry policy: Agent C now has a built-in structured dependency conflict resolution loop (see Agent C reference §4c). The agent diagnoses each import failure by category (ABI / version conflict / missing transitive dep / build failure / system library), applies a targeted fix to only the failing package, and re-verifies — up to 5 rounds internally. The orchestrator no longer needs to re-launch Agent C for routine dependency conflicts.

If Agent C still reports unresolved failures after 5 diagnosis rounds (or if it crashes/exceeds timeout), re-launch it once with the full failure log and the diagnosis-rounds table from setup_report.md. The re-launched agent should try a fundamentally different strategy (e.g., switch from venv+pip to conda, or use Docker if available).

If the re-launch also fails → reproduction failed path. Write reproduction_report.md with the section "## Reproduction Status: FAILED" explaining exactly which packages couldn't be installed, what was tried in each round, and why.

Step 4 — SmokeTester

Launch Agent D with:

• setup_report.md content (to know the venv path and installed packages)
• resource_map.md content (to know the repo structure)
• Instructions from references/agent-smoke-tester.md
• Target output: paper_reproduction_output/run_outputs/smoke_test_report.md

If smoke test fails → reproduction failed path. Write reproduction_report.md with "## Reproduction Status: FAILED — Smoke Test" and the error details.

Step 4.5 — Quick Benchmark (Time Estimation)

Before committing to a full run, estimate how long it will take. This prevents the worst reproduction failure mode: a silent multi-hour hang.

Agent D (SmokeTester) should include this as the last step of its smoke test. If Agent D already completed without a benchmark, the orchestrator runs it directly.

1. Run a 1-unit benchmark on the smallest dataset from the paper's experiments:

2. Read the paper to determine total work — total epochs, folds, datasets. If the paper doesn't state exact numbers, read the experiment script's defaults.

3. Estimate:

total_estimated = benchmark_time × total_units × dataset_count × 1.2 (I/O overhead)

4. Decision gate:

5. Output: Append to smoke_test_report.md:

## Time Estimate
- Benchmark: N.Ns for 1 [unit] on [dataset_name] (H:MM:SS wall clock)
- Total work: E epochs × D datasets = T total units
- Estimated full run: ~N min (range: Nmin–Nmax depending on dataset)
- Decision: [proceed / warn-user / offer-scale-down]

If the benchmark itself fails (code crashes during first epoch), log the error and proceed to Step 5 on a best-effort basis. A failed benchmark does not block reproduction — it just means we go in blind.

Step 5 — FullRunner

Launch Agent E with:

• All prior outputs (methodology, resource map, setup report, smoke test report)
• reproduction_mode.txt content — so it knows which strategy to use
• Instructions from references/agent-full-runner.md
• Target output: paper_reproduction_output/run_outputs/full_run_report.md and actual output files in paper_reproduction_output/run_outputs/outputs/

Agent E should record wall-clock time for the full run. If the run takes longer than 1 hour, it should checkpoint intermediate results.

In experiment mode, Agent E finds and runs the paper's experiment scripts (main.py, train.py, shell scripts).

In tool mode, Agent E writes a reproduction script that imports the installed package and calls its API to reproduce each claimed result. The script goes in paper_reproduction_output/run_outputs/reproduction_script.py. See Agent E's reference file for the full instructions for each mode.

If full run fails partway → reproduction failed path. Document which step failed and any partial results obtained.

Step 6 — ResultComparator

Launch Agent F with:

• extracted_methodology.md (paper's claimed values)
• full_run_report.md (actual values from reproduction)
• tasklog.jsonl (accumulated structured log from all prior agents — Agent F uses this to detect systematic patterns, undiagnosed failures, and broken fix strategies; see Agent F reference §6)
• Instructions from references/agent-result-comparator.md
• Target output: paper_reproduction_output/reproduction_report.md

This is the final synthesis step. Agent F produces the definitive report.

Step 6.5 — Generate reproduction_manifest.md

After Agent F completes, the orchestrator (you) generates the manifest. This is a simple markdown table listing every artifact produced — it does NOT require launching a new agent.

Read the <paper_slug> output directory and produce:

# Reproduction Manifest — {paper_title}

| File | Generated By | Size | Description |
|------|-------------|------|-------------|
| paper_reading/extracted_methodology.md | Agent A | X KB | Extracted methodology and claims |
| paper_reading/resource_map.md | Agent B | X KB | Code repos, datasets, dependency files |
| env_setup/setup_report.md | Agent C | X KB | Environment setup log + API surface |
| run_outputs/smoke_test_report.md | Agent D | X KB | Smoke test result + time benchmark |
| run_outputs/full_run_report.md | Agent E | X KB | Full reproduction run log |
| run_outputs/outputs/* | Agent E | X MB | Reproduced numerical outputs |
| reproduction_report.md | Agent F | X KB | Paper claims vs reproduced values |

Write this to paper_reproduction_output/<paper_slug>/reproduction_manifest.md. This is the index the user opens first to navigate the results.

Step 7 — Merge and Present

Read reproduction_report.md. Present the top-line result to the user:

• Reproduced ✓: "≥80% of key metrics match within tolerance. The reproduction is usable as a baseline."
• Data Mismatch ⚠: "Code runs but metrics deviate from paper claims. See the detailed comparison table."
• Failed ✗: "Reproduction stopped at step [N]. See the failure analysis."

Then point the user to reproduction_report.md for the full report and reproduction_manifest.md for a file inventory.

TaskLog Protocol

Every agent MUST write structured JSONL entries to .claude/tasklog/YYYYMMDD-<session-slug>.jsonl for key actions. This turns "一段描述" into machine-analyzable, cross-paper reusable root cause records.

Schema reference: .claude/tasklog/SCHEMA.md (read only if you need the full field spec). Agents use a simplified subset below.

When to write

Minimum fields per entry

{
  "task_id": "kebab-case-id",
  "step": "what was attempted",
  "expected": "what should happen",
  "actual": "what actually happened",
  "deviation": {"type": "none|error|blocker|surprise|slow_path|wrong_turn|missing_context"},
  "root_cause": null,
  "fix": null,
  "fix_worked": null
}

Full field spec and deviation type definitions are in .claude/tasklog/SCHEMA.md. Agents: do not read SCHEMA.md unless you need the complete reference — the subset above is sufficient for routine entries.

Where to write

All agents write to the same file: .claude/tasklog/YYYYMMDD-<session-slug>.jsonl. Append-only, one JSON object per line, UTF-8. Do NOT overwrite previous entries.

Orchestrator's TaskLog responsibility

After each agent completes, the orchestrator reads the new entries in tasklog.jsonl and checks:

• Any deviation.type: "error" with root_cause: null → the agent left a failure undiagnosed. Ask the agent to fill in the root cause.
• Any fix_worked: false → the fix strategy failed. Do not retry the same fix.
• Three or more entries with the same root_cause → pattern detected. Flag for the final reproduction_report.md.

This replaces the previous "一段描述" failure feedback with structured data that Agent F can use to write a more precise deviation analysis.

Three Outcome Paths

Reproduced ✓

• ≥80% of key claimed metrics are within acceptable error (default ±5% for accuracy-like metrics, ±0.05 for AUC-like metrics; override per domain).
• Write final status as "Reproduced" with a confidence assessment.
• The output directory is ready to serve as a baseline for future work.

Data Mismatch ⚠

• Code runs to completion but one or more key metrics deviate significantly.
• Report: which metric, paper value, reproduced value, absolute and relative deviation, the first point in the pipeline where the deviation was observed.
• Flag whether the deviation direction (better/worse) is consistent or inconsistent across metrics.

Failed ✗

• Any of Steps 3-5 cannot complete after retries.
• Report: which step failed, full error trace, what was tried, and a concrete suggestion for what the user can try manually.
• Partial outputs (if any) are preserved in the output directory.

Platform-Specific Handling

These are baked into Agent C and Agent E's instructions. You don't need to handle them at the orchestrator level, but verify they were addressed:

Reference Files

Read each reference file only when you are about to launch the corresponding agent. Pass the full content as part of the agent's task description.