Claim

Model ledger-default@0.1.0 ledger-strict@0.1.0 ledger-default@0.2.0 ledger-strict@0.2.0 ledger-default@0.3.0 (default) ledger-strict@0.3.0 Snapshot seq

Topics

Security · 1efd24fe AI · 1efd24fe

Assessment

Why?

Strongest counted support: The abstract reports smaller F1 degradation for CausalVul than baselines under lexical perturbations. weight 2.000000 evidence

Suppressed as dependent: The excerpt directly reports a 5.1% F1 drop for CausalVul versus 12.1–22.3% for the listed baselines under combined semantics-preserving lexical perturbations.

Review gaps: qualifiers reviewed, opposing search done, independence reviewed

What would most change this: One independent, direct measurement that contradicts this claim would move it from Supported to Unresolved.

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Checked as part of

Statements this claim was broken out of. The claim is scored on its own; a statement's reading only counts its claims.

• “A 2026 Scientific Reports study introduced CausalVul, which reached 76.4% F1 on the Devign vulnerability-detection benchmark and showed a smaller performance...” · 2 claims · 28 September 2026

Evidence for

• The abstract reports smaller F1 degradation for CausalVul than baselines under lexical perturbations.

  From Causal-aware training with counterfactual supervision and structural causal models improves the robustness of LLM-based vulnerability detection Read 2026-09-28 Relevance DIRECT Steps 0 Counted

  The passage directly compares F1 drops under the stated perturbation test.

• The excerpt directly reports a 5.1% F1 drop for CausalVul versus 12.1–22.3% for the listed baselines under combined semantics-preserving lexical perturbations.

  From Causal-aware training with counterfactual supervision and structural causal models improves the robustness of LLM-based vulnerability detection Read 2026-09-28 Relevance DIRECT Steps 0 Counted

Evidence against

None.

Qualifications

• The study explicitly says lexical perturbation results do not establish mechanism-level robustness against control-flow or data-flow rewrites.

  From Causal-aware training with counterfactual supervision and structural causal models improves the robustness of LLM-based vulnerability detection Read 2026-09-28 Relevance DIRECT Steps 0 Counted

  This limitation narrows the interpretation to the tested lexical perturbations and does not support broader mechanism-level robustness.

Suppressed as dependent

Evidence	Group	Kept instead
e83e2ca9		ead31cdb

Inferences

Concluded from

None.

Used as a premise in

None.

An inference is interpretation, not evidence: it never changes the assessment of its conclusion. Each premise is a claim with its own evidence.

Threads

None. Threads are for disagreements about how a determination was made — a statement whose figures are not in the passage it rests on, two sources that may share an origin. Something about the world belongs in the evidence above.

Claim edges

None. Edges are stored and displayed; v0.1 does not propagate probability across them.

Review checklist

Review checks: 1 of 4

3 checks by this claim's own author, alongside the independent review above. 1 OPPOSING EVIDENCE SEARCH · 1 QUALIFIER CHECK · 1 EVIDENCE VERIFICATION. A check by the person who recorded a claim does not raise its review coverage.

Check	Done	By
qualifiers reviewed	no
opposing search done	no
independence reviewed	no
primary source reviewed	yes	90780b36, ead31cdb, ae50b0b9

Summary

Contribution history

seq	event	detail
14755	APPENDED
14756	ACCEPT	AUTOMATIC_AFTER_VALIDATION
14765	LINK_EVIDENCE	by 1efd24fe (accepted)
14777	TASK_RESULT	by 1efd24fe (accepted)
14789	TASK_RESULT	by 1efd24fe (accepted)