arXiv AI June 2, 2026 By Sajad Ebrahimi, Bhaskar Mitra, Negar Arabzadeh, Ye Yuan, Haolun Wu, Fattane Zarrinkalam, Ebrahim Bagheri

From Noise to Order: Learning to Rank via Denoising Diffusion

Read the original on arXiv AI →

arXiv:2602. 11453v2 Announce Type: replace-cross Abstract: In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the document being relevant to the query given some feature representation of the query-document pair.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

diffusion

arXiv Machine Learning

Jun 15

Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA

arXiv:2604. 23336v3 Announce Type: replace-cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial computational costs.

By Teng Chen, Sheng Xu, Feixiang Guo, Xiaoyu Wang, Qingqing Gu, Hongyan Li, Luo Ji

llms rag robotics efficiency benchmarks

arXiv AI

Jun 3

Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors

arXiv:2606. 02814v1 Announce Type: cross Abstract: Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs.

By Francisco Valentini, Edgar Altszyler, Martin Fajcik

llms rag benchmarks safety

arXiv AI

Jun 2

Test-Time Training for Zero-Resource Dense Retrieval Reranking

arXiv:2606. 01070v1 Announce Type: cross Abstract: Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings.

By Shiyan Liu, Yichen Li

rag benchmarks

arXiv Machine Learning

Jul 7

Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls

arXiv:2607. 03515v1 Announce Type: cross Abstract: In many machine learning applications, the most relevant items for a query should be efficiently retrieved.

By Kirill Shevkunov, Andrey Ploskonosov, Liudmila Prokhorenkova

rag

arXiv AI

Jul 14

Tool-Adaptive LLM Reranker

arXiv:2607. 10555v1 Announce Type: cross Abstract: Generative Large Language Models (LLMs) have revolutionized information retrieval, yet their strictly parametric nature frequently leads to severe factual hallucinations when confronted with complex queries beyond their epistemic boundaries.

By Zichuan Liu, Ruijin Hua

llms rag agents reinforcement-learning efficiency benchmarks safety

arXiv AI

Jun 17

Non-negative Elastic Net Decoding for Information Retrieval

arXiv:2606. 17910v1 Announce Type: cross Abstract: Dense retrieval has become the dominant paradigm in information retrieval, in which each document is scored against a query by the inner product of their vector embeddings, and the top-$k$ documents by score are retrieved for this query.

By Koki Okajima, Yasutoshi Ida, Tsukasa Yoshida, Yasuaki Nakamura

rag benchmarks

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Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA

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