1Cademy - MOOC-CS ΔR@k Curve Stays Near Zero Across k (Adaptive vs Hierarchical)

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Method Part 2: Adaptive Depth Gating for Prerequisite Retrieval (Auditable Strict-Parity Graph-RAG Paper)
Tight Budgets And Token Caps (Results) in Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls
Scope Notes for MOOC-CS Prerequisite Benchmark (Auditable Strict-Parity Graph-RAG Paper)

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MOOC-CS ΔR@k Curve Stays Near Zero Across k (Adaptive vs Hierarchical)

On MOOC-CS, the paired-bootstrap $\Delta\text{R@}k$ curve between Adaptive (heuristic) depth gating and the fixed-depth hierarchical baseline stays near zero throughout $k$ , in contrast to LectureBank-Full where adaptive shows a pointwise advantage at $k=4$ . Even where the LectureBank-Full pointwise effect appears, it is weaker than the diffusion-and-quota gain. This reading supports the paper's descriptive conclusion that adaptive depth gating's tight-budget benefit is dataset-specific and not regime-wide.

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Updated 2026-05-17

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Reference: Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls

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