Paper Notes

Produce consistent, searchable paper notes that later steps (claims, visuals, writing) can reliably synthesize.

This is still NO PROSE: keep notes as bullets / short fields, not narrative paragraphs.

Load Order

Always read:

• references/overview.md

• references/note_schema.md

Read by task:

• references/limitation_taxonomy.md when writing or reviewing limitations (avoid boilerplate)

• references/result_extraction_examples.md when extracting key_results (good vs bad examples)

• references/source_text_hygiene.md when result/limitation fields still preserve paper self-narration or author-result wrappers

Machine-readable assets:

• assets/note_schema.json — JSONL record schema for validation

• assets/evidence_tags.json — evidence bank tagging categories (extensible without code changes)

• assets/source_text_hygiene.json — note-field source sentence cleanup policy

Script Boundary

Use scripts/run.py only for:

• deterministic scaffold generation from core_set + metadata

• priority selection based on mapping coverage

• evidence bank construction from structured note fields

Do not treat run.py as the place for:

• paper-specific limitation prose (use references/limitation_taxonomy.md for guidance)

• domain-specific evaluation heuristics hidden in code

• reader-facing narrative text

Role cards (prompt-level guidance)

Close Reader

• Mission: extract what is specific and checkable (setup, method, metrics, limits).

• Do: name concrete tasks/benchmarks and what the paper actually measures.

• Avoid: generic summary boilerplate that could fit any paper.

Results Recorder

• Mission: capture evaluation anchors that later writing needs.

• Do: record task + metric + constraints (budget/tool access) whenever available.

• Avoid: copying numbers without the evaluation setting that makes them meaningful.

• Avoid: promoting artifact introductions (X enables ... , our framework features ... ) into key_results .

• Avoid: promoting benchmark-positioning, field-motivation, or author-navigation lines (we apply ... and show ... , we then discuss how ... ) into key_results .

Limitation Logger

• Mission: capture the caveats that change interpretation.

• Do: write paper-specific limitations (protocol mismatch, missing ablations, threat model gaps).

• Avoid: repeated generic limitations like “may not generalize” without specifics.

When to use

• After you have a core set (and ideally a mapping) and need evidence-ready notes.

• Before writing a survey draft.

Inputs

• papers/core_set.csv

• Optional: outline/mapping.tsv (to prioritize)

• Optional: papers/fulltext_index.jsonl

• papers/fulltext/*.txt (if running in fulltext mode)

Outputs

• papers/paper_notes.jsonl (JSONL; one record per paper)

• papers/evidence_bank.jsonl (JSONL; addressable evidence snippets derived from notes; A150++ target: >=7 items/paper on average)

Decision: evidence depth

• If you have extracted text (papers/fulltext/*.txt ) → enrich key papers using fulltext snippets and set evidence_level: "fulltext" .

• If you only have abstracts (default) → keep long-tail notes abstract-level, but still fully enrich high-priority papers (see below).

Workflow (heuristic)

Uses: outline/mapping.tsv , papers/fulltext_index.jsonl .

• Ensure coverage: every paper_id in papers/core_set.csv must have one JSONL record.

• Use mapping to choose high-priority papers:

• heavily reused across subsections

• pinned classics (ReAct/Toolformer/Reflexion… if in scope)

• For high-priority papers, capture:

• 3–6 summary bullets (what’s new, what problem setting, what’s the loop)

• method (mechanism and architecture; what differs from baselines)

• key_results (benchmarks/metrics; include numbers if available)

• limitations (specific assumptions/failure modes; avoid generic boilerplate)

• For long-tail papers:

• keep summary bullets short (abstract-derived is OK)

• still include at least one limitation, but make it specific when possible

• Assign a stable bibkey for each paper for citation generation.

Quality checklist

Coverage: every paper_id in papers/core_set.csv appears in papers/paper_notes.jsonl .

High-priority papers have non-TODO method/results/limitations.

Limitations are not copy-pasted across many papers.

evidence_level is set correctly (abstract vs fulltext ).

Evidence bank: papers/evidence_bank.jsonl exists and is dense enough for A150++ (>=7 items/paper on average).

Helper script (optional)

Quick Start

• python .codex/skills/paper-notes/scripts/run.py --help

• python .codex/skills/paper-notes/scripts/run.py --workspace <workspace_dir>

All Options

• See --help (this helper is intentionally minimal)

Examples

• Generate notes, then optionally enrich priority=high papers:

• Run the helper once, then refine papers/paper_notes.jsonl (e.g., add full-text details for key papers and diversify limitations).

Notes

• The helper writes deterministic metadata/abstract-level notes and marks key papers with priority=high .

• In pipeline.py --strict it will be blocked if high-priority notes are incomplete (missing method/key_results/limitations) or contain placeholders.

Troubleshooting

Common Issues

Issue: High-priority notes still look like scaffolds

Symptom:

• Quality gate reports missing method/key_results or TODO placeholders.

Causes:

• Notes were generated from abstracts only; key papers weren’t enriched.

Solutions:

• Fully enrich priority=high papers: method , ≥1 key_results , ≥3 summary_bullets , ≥1 concrete limitations .

• If you need full text evidence, run pdf-text-extractor in fulltext mode for key papers.

Issue: Repeated limitations across many papers

Symptom:

• Quality gate reports repeated limitation boilerplate.

Causes:

• Copy-pasted limitations instead of paper-specific failure modes/assumptions.

Solutions:

• Replace boilerplate with paper-specific limitations (setup, data, evaluation gaps, failure cases).

Recovery Checklist

• papers/paper_notes.jsonl covers all papers/core_set.csv paper_ids.

• ≥80% of priority=high notes satisfy method/results/limitations completeness.

• No TODO remains in high-priority notes.