arXiv AI June 8, 2026 By Fuqiang Wang, Song Tan, Zheng Guo, Jiaohao Fu, Xinglong Xu, Bihui Yu, Jie Dong, Zheng Sun, Siyuan Li, Jingxuan Wei, Cheng Tan

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

Read the original on arXiv AI →

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.

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

arXiv AI

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

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arXiv Machine Learning

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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.

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arXiv AI

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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.

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arXiv AI

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PreScience: A Dataset and Benchmark for Scientific Forecasting

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arXiv AI

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

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arXiv AI

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Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

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

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

DRFLOW: A Deep Research Benchmark for Personalized Workflow Prediction

PreScience: A Dataset and Benchmark for Scientific Forecasting

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Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning