arXiv Machine Learning

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

arXiv:2606. 29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes.

arXiv Machine Learning

Jul 14

Serving the Long Tail: Training-Free LLM Candidate Generation for Vacation Rental Marketplaces

arXiv:2607. 09877v1 Announce Type: new Abstract: Vacation rental marketplaces face a structural imbalance on the supply side: a small fraction of properties receive most user interactions, while the long tail of new, niche, and seasonal listings generates too little behavioral signal for collaborative filtering to serve effectively.

By Syed Mohammed Arshad Zaidi, Eric Rincon, Shayan Hassantabar

llms

arXiv AI

6d ago

CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation

arXiv:2605. 04495v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily estimate query--document relevance.

By Zhipeng Song, Yizhi Zhou, Xiangyu Kong, Jiulong Jiao, Xuezhou Ye, Chunqi Gao, Xueqing Shi, Yu Wang, Yuhang Zhou, Heng Qi

llms rag

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 Machine Learning

Jul 28

SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads

arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).

By Congfei Zhang, Jingxiao Ma, Xiaodong Liu, Hsiang-wei Chao, Siman Wang, Ge Liu, Shantanu Aggarwal, Vincent Zhang, Meghana Missula, Rachel Liao, Zichu Li, Xiao Bai, Yunzhi Zhou, Yajun Wang, Zhe Liu, Jinchao Li, Yu Zhang

llms rag

arXiv AI

1d ago

What the Reranker Sees: Multi-Aspect Page Annotation for Long-Document Multimodal Question Answering

arXiv:2608. 14841v1 Announce Type: new Abstract: Long-document visual question answering (VQA) over documents of tens to hundreds of pages mixing text, tables, charts, and figures typically follows retrieve-then-read pipelines.

By Guanchen Wu, Jiayuan Ding, Subhabrata Mukherjee, Carl Yang

llms nlp multimodal

arXiv Machine Learning

Jul 31

Recall Before You Rank: Similarity-Guided Top-$K$ Reuse for Efficient Long-Context Attention

arXiv:2607. 27692v1 Announce Type: cross Abstract: Top-$K$ sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries.

By Wenshuai Yao, Wenyong Zhou, Hanyong Shao, Yizhe Chen, Zhiyuan Ning, Yuannuo Feng, Ru Huang, Kechao Tang

efficiency

arXiv AI

Jun 9

Generative Reasoning Re-ranker

arXiv:2602. 07774v5 Announce Type: replace-cross Abstract: Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge.

By Mingfu Liang, Yufei Li, Jay Xu, Kavosh Asadi, Xi Liu, Shuo Gu, Kaushik Rangadurai, Frank Shyu, Shuaiwen Wang, Song Yang, Zhijing Li, Jiang Liu, Mengying Sun, Fei Tian, Xiaohan Wei, Chonglin Sun, Jacob Tao, Shike Mei, Wenlin Chen, Santanu Kolay, Sandeep Pandey, Hamed Firooz, Luke Simon

llms rag nlp reinforcement-learning fine-tuning 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

Jun 3

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.

By Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu, Peng Jiang, Kun Gai

llms reinforcement-learning fine-tuning safety

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 Machine Learning

Jun 4

Training-Free Lexical-Dense Fusion for Conversational-Memory Retrieval

arXiv:2606. 04194v1 Announce Type: new Abstract: Retrieving the few past turns that answer a new query across long multi-session histories is the retrieval bottleneck behind long-term conversational memory (LoCoMo, LongMemEval).

By Christian Lysenst{\o}en

rag benchmarks safety

arXiv AI

Aug 7

Comparative Approaches to Agent Retrieval over Large Skill Libraries

arXiv:2608. 06196v1 Announce Type: new Abstract: Agents backed by large skill libraries must decide which skills to load and in what order.

By Indivara Kolluru, Nathan Sportsman

llms rag agents

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