The rapid growth of pre-trained models has made selecting suitable models for downstream tasks challenging. Exhaustively fine-tuning or evaluating every candidate model is computa- tionally expensive, while existing proxy-based metrics such as LEEP, NCE, and LogME pro- duce one-shot static rankings that do not use feedback from earlier evaluations. We propose Reinforcement Learning–based Model Rank- ing (RL-MR), a budget-aware framework that reformulates pretrained model selection as a sequential decision-making problem. RL-MR learns a ranking policy that uses proxy-based transferability signals as rewards to guide the order in which candidate models are evaluated. In controlled experiments on SST-2, Yelp Po- larity, Amazon Polarity, and DynaSent, RL- MR consistently improves ranking correlation over static proxy baselines, achieving up to Kendall’s τ = 0.8695 while maintaining Top-1 selection accuracy. These results suggest that sequential proxy feedback can provide a use- ful alternative to one-shot model ranking under limited evaluation budgets.

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.