arXiv AI June 17, 2026 By HyunJin Kim, Jaejun Shim, Young Jin Kim, JinYeong Bak

Temporal Preference Optimization for Unsupervised Retrieval

arXiv:2606. 17664v1 Announce Type: cross Abstract: Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevance, retrieving semantically related but temporally misaligned documents-an important aspect when a document collection spans multiple time periods (e.

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

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

Jul 3

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.

By Yuhang Wu, Xiangqing Shen, Fanfan Wang, Cangqi Zhou, Zhen Wu, Xinyu Dai, Rui Xia

llms rag reinforcement-learning benchmarks

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 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 AI

Jul 7

Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

arXiv:2607. 04088v1 Announce Type: cross Abstract: LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time.

By Yingdong Yang, Haijian Wu

rag benchmarks

arXiv AI

Jun 10

STORM: Stepwise Token Optimization with Reward-Guided Beam Search

arXiv:2606. 10621v1 Announce Type: cross Abstract: Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever the model changes.

By Arthur Satouf, Giulio D'Erasmo, Yuxuan Zong, Habiboulaye Amadou Boubacar, Pablo Piantanida, Benjamin Piwowarski

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Related stories

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

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

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

Test-Time Training for Zero-Resource Dense Retrieval Reranking

Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

STORM: Stepwise Token Optimization with Reward-Guided Beam Search