Beyond Global Confidence: Region-Aware Robust Learning for Graph–LLM Feedback

Feedback between graph neural networks (GNNs) and large language models (LLMs) can improve average accuracy while leaving graph regions unreliable. We address the gap between selecting confident pairs and controlling their influence on learning through regional feedback allocation: regional contribution, structural pair admission, and residual risk within admitted regions. Stabilized importance weights balance regional influence; frozen-predictor counterfactual trials admit structure-dependent disagreements; and calibrated harmful-feedback estimates set regional KL-DRO budgets. Under matched $kC+500$ train-plus-calibration access, our framework outperforms Co-teaching, the principal paired comparator, by 1.20–2.72 percentage points across five primary datasets. Published GNN-as-Judge results descriptively trail ours by 2.6–6.5 accuracy points and 13.8–20.4 support-filtered worst-region accuracy points. Co-teaching is rerun; GNN-Judge uses published results here. Component removals cost 3.3–8.6 points, while held-out admission quality and fixed-radius ablations link these gains to pair selection and regional risk allocation. Benefits extend to heterophilic graphs, another LLM family, and cross-dataset transfer. Few-shot denotes direct training supervision; shared validation labels remain selection-only, and a separate strict-$kC$ adaptation test removes additional calibration labels. The results show how regional feedback diagnostics can guide training to improve both average accuracy and local reliability.