SubmissionNumber#=%=#3
FinalPaperTitle#=%=#When Learned Context Planning Fails to Beat Strong Retrieval: A Controlled Study of Planning, Routing, and Reranking for Long-Context QA
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#Han Chen
JobTitle#==#
Organization#==#
Abstract#==#Learned context planning selects evidence atoms before an answer model reasons over them. We test whether this learned selection improves long-context multiple-choice QA after strong retrieval, routing, budgeted-selector, and reranking controls. Our primary diagnostic uses all 503 LongBench-v2 MCQ questions with Qwen2.5-7B-Instruct. The planner is SFT-trained on outcome-selected traces from 140 training and 28 development questions; because the 503-question analysis includes those questions, it is partly transductive. At an 18k-character budget, anchored hybrid retrieval reaches 36.18% accuracy and BM25 reaches 35.98%, while the best direct planner-guided method reaches 34.19%. On the untouched 152-question test split, anchored hybrid remains higher (42.11% versus 36.84%). Leakage-safe routers cannot convert a large oracle gap. Under tight budgets, the best planner is ahead by only 0.40 points at 6k and loses at 9k; planner-guided reranking has a +1.79-point estimate at 6k with a paired interval crossing zero and ties the control at 9k. Packing-order and score-flatness analyses did not identify a stable mechanism. Under this setup, learned planning is a weak relevance signal rather than a replacement for strong retrieval.
Author{1}{Firstname}#=%=#Yingrui
Author{1}{Lastname}#=%=#Li
Author{1}{Orcid}#=%=#
Author{1}{Email}#=%=#emmalyr0404@gmail.com
Author{1}{Affiliation}#=%=#Independent Researcher
Author{2}{Firstname}#=%=#Han
Author{2}{Lastname}#=%=#Chen
Author{2}{Username}#=%=#aapple
Author{2}{Orcid}#=%=#
Author{2}{Email}#=%=#hc414t@gmail.com
Author{2}{Affiliation}#=%=#University of Texas at Austin

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