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Token-Cap Diagnostics Separated from Headline Comparisons
Tight Budgets And Token Caps (Results) in Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls
Method-Related Scope Notes for Adaptive Depth Gating for Prerequisite Retrieval (Auditable Strict-Parity Graph-RAG Paper)
When Adaptive Depth Helps vs When Fixed-Depth Is Safer (Budget/Cap Guidance)
The paper's token-budget guidance is a dataset-aware conditional recommendation: adaptive retrieval can be useful when is small and token caps are loose (where its tight-budget pointwise gain on LectureBank-Full appears at ); under strict token caps, the simpler fixed-depth hierarchical baseline is the safer descriptive default on LectureBank-Full, because adaptive contexts are longer on average and lose more under truncation. The recommendation is dataset-aware: it is supported on LectureBank-Full and is not supported on MOOC-CS, where the curve stays near zero. It is framed as descriptive guidance rather than a multiplicity-corrected significance claim.
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Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls
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Token-Cap Comparison on LectureBank-Full: Adaptive Loses More as Cap Tightens
When Adaptive Depth Helps vs When Fixed-Depth Is Safer (Budget/Cap Guidance)
Token-Cap Comparison on LectureBank-Full: Adaptive Loses More as Cap Tightens
LectureBank-Full Tight-Budget Advantage of Adaptive Depth Gating (Mean ΔR@k = +2.13 over k∈{1,2,3,4})
LectureBank-Full ΔR@k Peaks at k=4 (+6.4 Points, CI [1.0, 11.7])
When Adaptive Depth Helps vs When Fixed-Depth Is Safer (Budget/Cap Guidance)
MOOC-CS ΔR@k Curve Stays Near Zero Across k (Adaptive vs Hierarchical)
LectureBank-Full Tight-Budget Advantage of Adaptive Depth Gating (Mean ΔR@k = +2.13 over k∈{1,2,3,4})
When Adaptive Depth Helps vs When Fixed-Depth Is Safer (Budget/Cap Guidance)
Three Durable Signals of Strict-Parity Prerequisite Retrieval
Adaptive Depth Gating Helps Only in Tight-k Settings on Headline Prerequisite Benchmarks
HotpotQA External-Validity Probe: Adaptive Depth Does Not Transfer to a Denser Non-Prerequisite Graph (FullWiki-1k: Flat 93.4 / Hier 92.9 / Adaptive 94.0 R@10)