reranking

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 describes a system for SemEval-2026 Task 8 that uses a three-stage pipeline involving query rewriting with a fine-tuned Qwen model, hybrid retrieval, and cross-encoder reranking to improve multi-turn retrieval performance.

MemReranker is a reasoning-aware reranking model family (0.6B/4B) designed for agent memory retrieval, addressing limitations in semantic similarity by incorporating LLM knowledge distillation for better temporal and causal reasoning.

This paper introduces CoREB, a contamination-limited multitask benchmark for code search that evaluates text-to-code, code-to-text, and code-to-code retrieval with fine-tuned reranking capabilities.

Researchers identify systematic English and query-language bias in multilingual RAG rerankers and introduce LAURA, a utility-driven alignment method that boosts performance by retrieving answer-critical documents across languages.

Sentence Transformers v5.4 introduces support for multimodal embedding and reranking, allowing users to encode and compare text, images, audio, and video using a unified API.