Large language models have achieved strong performance on medical reasoning benchmarks, yet their deployment in clinical settings demands rigorous verification to ensure factual accuracy. While reward models offer a scalable approach for reasoning trace verification, existing methods face two limitations: they produce only scalar reward values without explicit justification, and they rely on single-pass retrieval that precludes adaptive knowledge access as verification unfolds. We introduce Med-TIV, an agentic framework that addresses these limitations by training medical reward models to iteratively query external medical evidence during evaluation. Our approach combines tool-augmented verification with an iterative reinforcement learning paradigm that requires only trace-level supervision, alongside an adaptive curriculum mechanism that dynamically adjusts training data distribution. Across four medical reasoning benchmarks, Med-TIV achieves substantial gains over existing methods, improving MedQA accuracy by 23.5% and MedXpertQA by 32.0% relative to the base generator. Crucially, Med-TIV demonstrates an 8× reduction in sampling budget compared to prior reward model baselines. These findings establish that agentic medical reward modeling offers a principled path toward more reliable medical reasoning systems.

Biomedical retrieval-augmented generation (RAG) produces long-form answers that mix supported, under-evidenced, and contradicted claims, demanding fine-grained verification. We propose MedRAGChecker, a claim-level diagnostic framework that combines textual natural language inference (NLI) with a biomedical knowledge-graph (KG) compatibility signal. Its asymmetric selective-gating rule preserves the textual prediction for most claims and applies KG adjustment only to confident textual entailments assigned low plausibility by the KG back-end. We distill GPT-4.1 claim decompositions and NLI pseudo-labels into compact student models and evaluate three biomedical generators on PubMedQA, MedQuAD, LiveQA, and MedRedQA. The student-ensemble results show that selective BioPortal gating increases macro-averaged faithfulness from 60.8% to 64.4% and reduces the contradiction rate from 19.9% to 15.0%. On 172 annotations of 86 claims whose decisions change after fusion, agreement with human evidence judgments increases from 63.4% to 69.8%. A comparison with DRKG shows that fusion behavior varies substantially with KG coverage, entity linking, score construction, and calibration; the comparison therefore characterizes back-end effects rather than isolating a causal effect of coverage.