arXiv AI By Nan Lu, Yurong Hu, Jiaquan Fang, Yan Liu, Rui Dong, Yiming Wang, Rui Lin, Shaoyi Xu

SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management

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arXiv:2510. 08948v4 Announce Type: replace-cross Abstract: Effective e-commerce risk management requires in-depth case investigations to identify emerging fraud patterns in highly adversarial environments.

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arXiv AI
Jul 28

Traceable LLM Reasoning for Fake-Order Fraud Detection

arXiv:2607. 23075v1 Announce Type: cross Abstract: Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability.

By Siqi You, Bingsong Xu, Zhixian Zheng, Xinjian Peng, Yang Xie, Ying Wang, Jiarong Xu
arXiv AI
Jun 29

DMind Benchmark: Toward a Holistic Assessment of LLM Capabilities across the Web3 Domain

arXiv:2504. 16116v4 Announce Type: replace-cross Abstract: The Web3 ecosystem, underpinned by cryptographic primitives and decentralized consensus, represents a high-stakes environment where software vulnerabilities and incentive misalignments translate directly into financial loss.

By Enhao Huang, Pengyu Sun, Shuxun Wang, Zixin Lin, Alex Chen, Kaichun Hu, Joey Ouyang, Frank Li, Zhiyu Zhang, Haobo Wang, Yiming Li, Zhan Qin, James Yi, Gang Zhao, Ziang Ling, Lowes Yang
arXiv Computation and Language
Sep 11

LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains

LLMAR is a tuning‑free recommendation framework designed for sparse, text‑rich industrial B2B domains. It transforms user behavioral history into structured semantic motives using LLM inference, employs a reflection loop to self‑correct hallucinations, and operates cost‑effectively with asynchronous batch processing. Experiments on MovieLens‑1M, Amazon Prime Pantry, and a construction risk dataset show LLMAR surpasses state‑of‑the‑art learning models, achieving up to a 54.6% nDCG@10 improvement while keeping inference costs around $1 per 1,000 users.

By Ryogo Hishikawa, Ichiro Kataoka, Shinya Yuda