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.