Evidence-Gated Correction: A Controlled Study of Recursive Retrieval in Scientific QA

Retrieval-augmented generation for scientific question answering can produce answers whose support is insufficiently grounded in retrieved evidence. We formulate recursive correction as a selective control problem: after an initial answer, a controller decides whether to preserve it, retrieve additional evidence, generate a candidate correction, or commit that candidate. SelfHeal-RAG implements this separation using hybrid dense-sparse retrieval, NLI-based evidence scoring, recursive PubMed retrieval, and a state-aware controller that adjudicates candidate corrections against an affirmative proposition. On a frozen, question-only BioASQ held-out set (n=150; 110 Yes / 40 No), evidence-gated recursion improves paired accuracy from 42.67% to 51.33% (+8.67 percentage points; exact McNemar p=0.000244), with all 13 changed decisions beneficial and none harmful. A retrieval-off condition exactly reproduces the baseline, while V5 significantly differs from retrieval-off, consistent with recursive retrieval contributing to the observed improvement. Two controls qualify this result: unconditional replacement reaches 54.0%, so this experiment does not establish an accuracy advantage for selective gating over unconditional replacement; and the benchmark has a 73.33% majority-class baseline. In a complementary diagnostic with answer-bearing context supplied directly, baseline accuracy is 74.5% and recursive correction adds 1.5 percentage points (p=0.25). Together, these results provide evidence that recursive evidence acquisition can improve scientific question answering relative to one-pass correction, while leaving the relative value of selective commitment versus simpler unconditional correction unresolved.