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本文提出 DeFiRank，一个基于联邦学习的成对排序框架，使多家金融机构在不共享私有交易数据的情况下协作训练治理排序模型，在真实区块链数据上接近集中式基线的排序性能，同时保障数据隐私。
This paper proposes separating generation from selection in explainable-recommendation systems to reduce serving costs, using a frozen candidate pool of explanations and a small CPU-resident selector. It benchmarks offline-pool selectors and finds that pairwise learning-to-rank outperforms single-action RL formulations like PPO, GRPO, and DPO in terms of F1 scores.
Proposes reframing Pairwise Ranking Prompting (PRP) reranking as active learning from noisy pairwise comparisons, improving NDCG@10 per call under budget constraints, and introduces a randomized-direction oracle that reduces LLM calls per pair.
This paper reframes pairwise ranking prompting as active learning from noisy comparisons, introducing a noise-robust framework with a randomized-direction oracle to improve ranking quality under call constraints and address position bias.