:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Jin, Haoxiang, Li, Ronghan, Lu, Zixiang, Miao, Qiguang
Format:	Preprint
Published:	2025
Subjects:	Information Retrieval
Online Access:	https://arxiv.org/abs/2509.06472
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval
by: Zheng, Yuanhang, et al.
Published: (2024)

LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
by: Gao, Jingtong, et al.
Published: (2024)

EviRerank: Adaptive Evidence Construction for Long-Document LLM Reranking
by: Li, Minghan, et al.
Published: (2024)

CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrieval
by: Tian, Runchu, et al.
Published: (2025)

UniRank: Unified List-wise Reranking via Confidence-Ordered Denoising
by: Jia, Pengyue, et al.
Published: (2026)

UniRQR: A Unified Model for Retrieval Decision, Query, and Response Generation in Internet-Based Knowledge Dialogue Systems
by: Hu, Zhongtian, et al.
Published: (2024)

Illusions of Relevance: Arbitrary Content Injection Attacks Deceive Retrievers, Rerankers, and LLM Judges
by: Tamber, Manveer Singh, et al.
Published: (2025)

LLM-based Listwise Reranking under the Effect of Positional Bias
by: Qiao, Jingfen, et al.
Published: (2026)

LANCER: LLM Reranking for Nugget Coverage
by: Ju, Jia-Huei, et al.
Published: (2026)

RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback
by: Abdallah, Abdelrahman, et al.
Published: (2025)

Passage Query Methods for Retrieval and Reranking in Conversational Agents
by: De Lima, Victor, et al.
Published: (2025)

Multivector Reranking in the Era of Strong First-Stage Retrievers
by: Martinico, Silvio, et al.
Published: (2026)

RALLM-POI: Retrieval-Augmented LLM for Zero-shot Next POI Recommendation with Geographical Reranking
by: Li, Kunrong, et al.
Published: (2025)

A Hybrid Retrieval and Reranking Framework for Evidence-Grounded Retrieval-Augmented Generation
by: Irany, Fariba Afrin, et al.
Published: (2026)

Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge
by: Lee, Joohyun, et al.
Published: (2024)

$\text{R}^2\text{R}$: A Route-to-Rerank Post-Training Framework for Multi-Domain Decoder-Only Rerankers
by: Wang, Xinyu, et al.
Published: (2025)

LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval
by: Li, Yunhan, et al.
Published: (2026)

Dynamic Ranked List Truncation for Reranking Pipelines via LLM-generated Reference-Documents
by: Sinhababu, Nilanjan, et al.
Published: (2026)

RankLLM: A Python Package for Reranking with LLMs
by: Sharifymoghaddam, Sahel, et al.
Published: (2025)

Dynamic and Parametric Retrieval-Augmented Generation
by: Su, Weihang, et al.
Published: (2025)

Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion
by: George, Sherine
Published: (2025)

Understanding Parametric Knowledge Injection in Retrieval-Augmented Generation
by: Tang, Minghao, et al.
Published: (2025)

Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-based Reranking
by: Li, Chuang, et al.
Published: (2025)

GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation
by: Miao, Ruizhong, et al.
Published: (2026)

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models
by: Abdallah, Abdelrahman, et al.
Published: (2025)

Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking
by: Huang, Jerry, et al.
Published: (2025)

ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
by: Yoon, Soyoung, et al.
Published: (2024)

Beyond Sequential Reranking: Reranker-Guided Search Improves Reasoning Intensive Retrieval
by: Xu, Haike, et al.
Published: (2025)

RRRA: Resampling and Reranking through a Retriever Adapter
by: Kim, Bongsu
Published: (2025)

Next-Scale Generative Reranking: A Tree-based Generative Rerank Method at Meituan
by: Wang, Shuli, et al.
Published: (2026)

LLM-Enhanced Reranking for Complementary Product Recommendation
by: Xu, Zekun, et al.
Published: (2025)

Diagnosing LLM-based Rerankers in Cold-Start Recommender Systems: Coverage, Exposure and Practical Mitigations
by: Lemdiasova, Ekaterina, et al.
Published: (2026)

Prism-Reranker: Beyond Relevance Scoring -- Jointly Producing Contributions and Evidence for Agentic Retrieval
by: Zhang, Dun
Published: (2026)

Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction
by: Li, Zhuofeng, et al.
Published: (2026)

Rethinking Agentic RAG: Toward LLM-Driven Logical Retrieval Beyond Embeddings
by: Zeng, Yuqi, et al.
Published: (2026)

Test-Time Training for Zero-Resource Dense Retrieval Reranking
by: Liu, Shiyan, et al.
Published: (2026)

Denoising Neural Reranker for Recommender Systems
by: Mao, Wenyu, et al.
Published: (2025)

Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
by: Cheng, Kai, et al.
Published: (2026)

TWOLAR: a TWO-step LLM-Augmented distillation method for passage Reranking
by: Baldelli, Davide, et al.
Published: (2024)

RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval
by: Skow, Tyler, et al.
Published: (2026)