The exponential growth of scientific literature has made personalized, factually grounded paper discovery increasingly difficult. Existing systems suffer from limited personalization, cold-start fragility, or hallucinated explanations. We introduce HybridScholar, an integrated architecture combining a CrossAttention-gated fusion of collaborative filtering and RAG learning candidate-specific gate weights per candidate with a Chain-of-Verification (CoVe) pipeline for factually grounded explanations. Evaluated on the Scholar Inbox dataset, HybridScholar substantially outperforms (NDCG@10=0.2991) strong baselines including LightGCN and Scholar-Inbox. Cold-start analysis shows the RAG fallback prevents hard failure for sparse users (NDCG@10=0.283), with cold NDCG rising monotonically as interactions grow. The CoVe pipeline reduces hallucination rate by 23.8%, confirmed by a human evaluation study showing CoVe-revised explanations are preferred by experts on factual accuracy (76.7% vs. 36.0%) and by end-users on helpfulness (78.8%, p<0.0001). Overall, HybridScholar improves ranking quality, cold-start robustness, and factual reliability for large-scale scholarly research discovery, demonstrated on 15,881 users across 239,618 papers.