Read Claim

Extract Claims

Extract all claims the authors make about their own work — both explicit and implicit. Actively look for hidden claims, split compound statements, and check cross-reference consistency.

Workflow

- [ ] Step 1: Read full paper text
- [ ] Step 2: Extract explicit claims
- [ ] Step 3: Extract implicit claims
- [ ] Step 4: Split compound claims
- [ ] Step 5: Cross-reference check
- [ ] Step 6: Save report

Step 1: Read Full Paper Text

Read {paper_dir}/metadata/{arxiv_id}/mathpix/{arxiv_id}.md

Read the entire document — do not split by section.

Step 2: Extract Explicit Claims

A claim is any assertion the authors make about their own work that could in principle be validated.

Not a claim:

• Background statements ("Transformers have achieved success in NLP")
• Descriptions of other work ("As shown in [10], ...")
• Definitions ("We define ego-video as first-person footage")
• Writing quality issues (grammar, typos) — these belong in read-txt, not here

Each claim:

{
  "id": 1,
  "source": "abstract",
  "quote": "exact sentence or phrase from the paper",
  "claim": "concise restatement of what is being asserted",
  "evidence_type": ["experiment"]
}

source: section where the claim appears (abstract, introduction, method, experiments, conclusion, appendix)

quote: exact original text — do not paraphrase

claim: clean one-sentence restatement of the assertion

evidence_type (array — a claim may belong to multiple types):

Every claim MUST map to at least one type. If unsure, apply this priority:

• Can it be checked by reading code/configs? → code
• Can it be checked against tables/figures? → experiment
• Is it a "should have done but didn't" ablation? → experiment
• Can it be checked against published literature? → related_work
• Is it a formal argument or derivation? → theoretical

Hints for related_work: assign when the claim cites or characterizes another paper, compares against a baseline, or asserts novelty ("first to", "pioneering"). Often combined with experiment when backed by the paper's own tables.

Step 3: Extract Implicit Claims

Actively look for assertions that are implied but not directly stated:

Numeric implications

• If abstract says "75.1% accuracy" and Table 2 also shows 75.1%, this implies "abstract matches Table 2" — extract as an experiment claim
• If a figure caption says "our method converges faster", extract the convergence claim

Scope overclaims

• "effective" without qualifier → implies works in all settings
• "state-of-the-art" → implies tested against all relevant baselines
• "lightweight" → implies compared in model size / FLOPs
• "scalable" → implies tested at multiple scales
• Note what would be needed to fully support the claim in implicit_reason

Method-result gaps

• Method section describes a component, but experiments don't ablate it → extract as: "Component X contributes to performance" (implicit, untested)
• A loss term is described but no ablation shows its effect → extract

Comparative implications

• "outperforms X" → implies same evaluation protocol as X
• "with only 256K data" → implies others use more data (extract the comparison)
• "zero-shot" → implies no task-specific fine-tuning (verify definition matches standard usage)

{
  "id": 33,
  "source": "experiments",
  "quote": "ShowUI achieves state-of-the-art grounding performance",
  "claim": "ShowUI outperforms all published methods on Screenspot grounding at time of submission",
  "evidence_type": ["experiment", "related_work"],
  "implicit_reason": "SOTA claim implies comparison against all relevant published baselines, but paper only compares against 5 methods"
}

Step 4: Split Compound Claims

A single sentence often packs multiple independently verifiable assertions. Always split by the smallest verifiable unit:

• "We achieve 75.1% on ScreenSpot, 70.0% on AITW, and competitive results on Mind2Web" → 3 claims
• "Our method is lightweight (2B) and uses less data (256K)" → 2 claims (model size, data size)
• "Token selection reduces tokens by 33% and speeds up training by 1.4x" → 2 claims (reduction rate, speedup)

Each sub-claim gets its own id and may have different evidence_type.

Step 5: Cross-Reference Check

Scan for internal consistency issues between sections:

• Abstract numbers vs. table numbers — do they match exactly?
• Method description vs. code/config (if mentioned) — any discrepancy?
• Figures/tables referenced in text — do the cited values match what's shown?
• Contribution list in intro vs. what experiments actually validate

Extract these as claims with evidence_type: ["experiment"]:

{
  "id": 45,
  "source": "abstract vs experiments",
  "quote": "75.1% accuracy in zero-shot screenshot grounding",
  "claim": "Abstract accuracy 75.1% matches Table 2 ShowUI row average",
  "evidence_type": ["experiment"]
}

Step 6: Save Report

Output: {paper_dir}/reports/check_claim.json

{
  "summary": {
    "total_claims": 50,
    "by_evidence_type": {
      "experiment": 25,
      "related_work": 10,
      "theoretical": 3,
      "code": 8
    }
  },
  "results": [
    {
      "id": 1,
      "source": "abstract",
      "quote": "exact text",
      "claim": "restatement",
      "evidence_type": ["experiment"]
    }
  ]
}

Coverage Rule

Err on the side of over-extraction — downstream skills will filter. A missed claim cannot be verified later. Aim for 60+ claims on a typical 10-page paper.

Tips

• Look for "we show", "we propose", "we achieve", "we demonstrate", "outperforms", "state-of-the-art", "first", "novel"
• Claims can appear anywhere — abstract, method, conclusion, captions, appendix
• Every number in the abstract should appear as a claim and be cross-referenced against the tables
• Passive voice often hides claims: "performance is improved by 3%" → who improved it? If the authors, it's a claim
• Qualifiers matter: "slightly better" vs "significantly better" vs "better" — extract the qualifier as part of the claim
• Figures are a rich source of implicit claims: trend lines, convergence curves, qualitative examples all assert something
• Do NOT flag writing issues (grammar, typos) — that is read-txt's job
• Ignore PDF parsing artifacts in quotes: broken hyphens (ex-\nploratory), joined words from line breaks (turnlevel), LaTeX math ($14.9 \%$). Clean up the quote to match the actual paper text when extracting.