reranking

The author shares techniques to prevent RAG-based agents from acting on bad retrieved context, including hybrid retrieval, reranking, corrective retrieval steps, and guardrails, with a build lab event on Aug 29 to demonstrate these methods.

This paper presents HNR-DAC, a two-stage framework for scientific claim verification over cited papers, combining hard-negative reranking and distribution-aligned classification. It achieves strong results on NLPCC 2026 Task 10 Track 2, ranking third on the leaderboard with the highest Macro-F1.

This paper introduces UniME-R1, an embedder-adviser framework for unified multimodal retrieval that generates Retrieval-Centric Chain-of-Thought (RC-CoT) conditioned on retrieval feedback, improving retrieval performance by learning from hard negatives.

Turbopuffer announces beta support for late interaction, enabling models like ColBERT to represent text as token-level vectors, combining a fast single-vector ANN first pass with exact late interaction reranking to improve recall.

TabRank introduces a framework for training reasoning rerankers for tabular retrieval by distilling chain-of-thought traces from a large reasoning model (DeepSeek-R1) into compact student models, achieving significant improvements across multiple table retrieval benchmarks.

This paper introduces a source-aware reranking method for RAG that incorporates domain-informed source reliability priors, improving Precision@5 from 0.48 to 0.72 on a 120-document health corpus and reducing adversarial document retrieval.

This paper presents a method to fine-tune LLaMA 3 8B as an efficient reranker for Retrieval-Augmented Generation using knowledge distillation and 4-bit quantization, achieving 14-21% gains in retrieval metrics over cross-encoder baselines with reduced inference cost.

Proposes SkillReranker, an inference-time reranking framework that decomposes tasks and skills to build a directed acyclic execution graph and uses a cross-encoder for scoring, improving task performance and efficiency in agent skill selection.

This paper presents an experimental study on using AI to find simulation models via natural language queries, evaluating data representations, embedding models, and retrieval strategies, finding that open-source embeddings and reranking methods significantly improve performance.

This paper challenges the assumption that reranking always improves few-shot selection for LLMs, proposing a training-free gated reranking approach that uses model uncertainty to decide when to rerank, reducing computational costs by 15-80% while slightly improving performance.

This paper presents 5ting, a system for multi-turn Retrieval-Augmented Generation (RAG) that combines BGE-M3 dense retrieval, FAISS indexing, LLM-based reranking, and evidence-constrained generation. The system achieves strong results on SemEval-2026 Task 8, with a retrieval nDCG@5 of 0.4719 and an end-to-end harmonic score of 0.5597.

KaLM-Reranker-V1 is a fast reranker that decouples query and passage computation using an encoder-decoder architecture with Matryoshka embedding pooling and cross-attention, achieving state-of-the-art reranking performance on BEIR and competitive results on multilingual benchmarks.

This paper introduces ADAS, a training-free reranking rule for parallel masked diffusion decoding that uses attention to discount tokens that strongly attend to uncertain positions, improving low-NFE performance on reasoning and code tasks with minimal runtime overhead.

The paper proposes SF-Re2G, a method that improves document-grounded dialogue systems by leveraging document structure to enhance retrieval, reranking, and generation. It validates on Chinese and English datasets.

A practical fix for RAG hallucination caused by noisy retrieval: use cross-encoder re-ranking to filter chunks with a score > 1.5, improving relevance from -0.28 to +3.80 on average.

Garry Tan released new gbrain-evals benchmarks showing ZeroEntropy.dev achieves state-of-the-art performance in reranking and embedding cost, speed, and retrieval success, beating MemPalace and Vector RAG.

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.

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.