**Claim-level traceability** is a reporting discipline in which every empirical claim in a graph-RAG evaluation is linked to the **specific artifact** that produced it — the exact split, candidate pool, encoder, cutoff, matching rule, and configuration — so that an external reader can reproduce the number behind the claim. The paper operationalizes this discipline through two concrete artifacts named in the Budgeted context construction and reproducibility paragraph: a **claim-to-artifact traceability matrix** that maps each empirical claim to the run that produced it, and **source-derived paper-of-record table fragments** that are generated directly from the underlying experiment artifacts rather than transcribed by hand. The discipline operationalizes the broader case for **reproducible IR benchmarking** (reference implementations, show-your-work reporting, split design, controlled benchmark assumptions) and for **auditable-artifact reporting standards** (data statements, datasheets, model cards, reproducibility checklists) at the level of individual paper claims.

Claim-Level Traceability in Graph-RAG Evaluation

**Curated prerequisite DAGs** over educational concepts are used in this paper as a graph-RAG **test bed** rather than a target application. Their value is that the directed acyclic graph (DAG) of prerequisite relations is **explicit, inspectable, and auditable**, which makes graph expansion policies easy to specify and to ablate. The aim is to test whether graph-aware retrieval helps **beyond a matched dense baseline**, not to argue that graph retrieval dominates general RAG settings. Consequently, conclusions drawn on these DAGs are scoped to curated, template-based prerequisite QA.

Prerequisite DAGs as a Graph-RAG Evaluation Test Bed

**Adaptive depth gating** is a per-query controller that sets the ancestor traversal depth $$d_\uparrow$$ and descendant traversal depth $$d_\downarrow$$ as a function of the **hierarchy level $$\ell(v^\star)$$** of the top dense seed and the **dense-score contrast** $$\Delta = s_1 - s_5$$ between the top and fifth dense scores. From base depths $$d_\uparrow^{(0)}$$ and $$d_\downarrow^{(0)}$$, the controller adds an indicator when $$\ell(v^\star)$$ is high (pushing $$d_\uparrow$$ up) or low (pushing $$d_\downarrow$$ up), and adds an indicator when $$\Delta$$ is large (more upward depth) or small (more downward depth), with floors of $$1$$ and $$0$$ respectively. Across the studied benchmarks (LectureBank-Full, QASC, HotpotQA boundary probe), adaptive gating remains **statistically tied** to a fixed-depth hierarchical baseline once interfaces and splits are matched, so it is treated as an optional extension rather than a core claim.

Adaptive Depth Gating for Prerequisite Retrieval

**Question-disjoint** and **target-concept-disjoint** controls are the two leakage-auditing splits used in this paper for canonical prerequisite-QA benchmarks. The audit is motivated by the observation that canonical splits reuse many **exact question strings** and **target concepts** across train and test, so a system can match memorized surface forms rather than retrieve true prerequisites. Under the **question-disjoint** control, no test question string appears in training. Under the **target-concept-disjoint** control, no test target concept appears as a target in training. Results obtained only under these controls are treated as the **trustworthy** evidence, and the paper restricts its remaining conclusions to curated, template-based prerequisite QA.

Question-Disjoint and Target-Concept-Disjoint Leakage Controls

The paper's **headline claims are intentionally retrieval-only**: they cover retrieval performance on two curated prerequisite DAGs (**LectureBank-Full** and **MOOC-CS**) plus a distinct-domain fact-graph validation on **QASC**. End-to-end **generation outcomes are reported only as diagnostics of context quality**, not as headline results, and an auxiliary denser **HotpotQA** slice is included as an external-validity boundary check rather than a primary benchmark. **Qualitative manual bundles** are released for future audits and are explicitly not used to validate automatic judges or to support inferential claims. The abstract makes this scope explicit by stating that adaptive depth gating is treated as an **optional extension rather than a core claim**, while diffusion with role-aware quotas serves only as a **controlled test case**.

Retrieval-Only Scope of Headline Claims

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Method-Related Scope Notes for Abstract (Auditable Strict-Parity Graph-RAG Paper)

Root URL: https://1iman.com/
Canonical paper URL: https://1iman.com/assets/working-papers/pdf/emnlp-auditable-strict-parity-evaluation-of-prerequisite-graph-retrieval-for-rag-under-leakage-controls.pdf
Portfolio PDF URL: https://1iman.com/assets/working-papers/pdf/emnlp-auditable-strict-parity-evaluation-of-prerequisite-graph-retrieval-for-rag-under-leakage-controls.pdf
Paper title: Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls
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Reference: Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls

Scope Part 1: Abstract (Auditable Strict-Parity Graph-RAG Paper)

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