arXiv AI

PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams

arXiv:2606. 07454v1 Announce Type: cross Abstract: Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates.

arXiv AI

Aug 5

SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG

arXiv:2608. 03860v1 Announce Type: cross Abstract: We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19.

By Kaysarul Anas Apurba, Md. Hasibul Hasan, Rofiqul Alam Shehab, Asab Azad

rag nlp benchmarks

arXiv Machine Learning

Jul 31

Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study

arXiv:2607. 27577v1 Announce Type: cross Abstract: Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.

By Di Bai, Jintao Liu, Zhenwei Tang, Peifan Wu, Nada Al-Thawr, Luoshu Wang

benchmarks safety

arXiv AI

Jun 17

DRFLOW: A Deep Research Benchmark for Personalized Workflow Prediction

arXiv:2606. 18191v1 Announce Type: new Abstract: Deep research (DR) systems are increasingly used for complex information-seeking tasks, but existing works mainly focus on generating reports and summaries.

By Md Tawkat Islam Khondaker, Raymond Li, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Issam H. Laradji

agents benchmarks

arXiv AI

Jul 3

PreScience: A Dataset and Benchmark for Scientific Forecasting

arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?

By Anirudh Ajith, Amanpreet Singh, Jay DeYoung, Nadav Kunievsky, Austin C. Kozlowski, Oyvind Tafjord, James Evans, Daniel S. Weld, Tom Hope, Doug Downey

llms rag agents benchmarks

arXiv AI

Jun 3

Whose Name Comes Up? II: Benchmarking and Intervention-Based Auditing of LLM-Based Scholar Recommendation

arXiv:2602. 08873v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now used for academic expert recommendation.

By Lisette Esp\'in-Noboa, Gonzalo Gabriel M\'endez

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 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

1d ago

Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay

arXiv:2608. 15780v1 Announce Type: cross Abstract: Stale recommendations are a pervasive challenge and a leading source of user complaints on large-scale content platforms.

By Di Bai, Feng Han, Zhenwei Tang, Jintao Liu, Luoshu Wang, Jialu Liu

arXiv Machine Learning

2d ago

Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation

arXiv:2604. 05379v2 Announce Type: replace-cross Abstract: The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences.

By Xing Tang, Ziqiang Cui, Jingyang Bin, Xiaokun Zhang, Fuyuan Lyu, Jingyan Jiang, Dugang Liu, Chen Ma, Xiuqiang He

rag fine-tuning benchmarks

arXiv AI

Jul 2

Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature

arXiv:2604. 12243v2 Announce Type: replace-cross Abstract: Identifying promising research directions in fast-moving subareas is one of the most cognitively expensive tasks in modern AI research.

By Jinkai Tao, Yubo Wang, Xiaoyu Liu, Menglin Yang

llms agents benchmarks

arXiv Machine Learning

Jun 9

Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses

arXiv:2606. 08921v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to predict missing facts from an observed knowledge graph (KG), playing a crucial role in a wide range of real-world applications such as drug discovery, recommender systems, and retrieval-augmented generation (RAG).

By Sooho Moon, Jian Kang, Yunyong Ko

llms rag benchmarks safety

arXiv AI

Jun 12

DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks

arXiv:2606. 12871v1 Announce Type: new Abstract: Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses.

By Jingxuan Han, Wei Liu, Mingyang Zhu, Youpeng Wang, Ziwen Wang, Lin Qiu, Xuezhi Cao, Xunliang Cai, Zheren Fu, Licheng Zhang, Zhendong Mao

llms agents benchmarks safety

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