ReDence is an open‑source hybrid framework that combines a multimodal agentic Investigator model with a specialized Decider model to detect modern social engineering scams. It automates investigative planning, evidence interpretation, and decision‑making across multiple scam categories, achieving 96% accuracy and operating at least four times faster than comparable agentic models. The system outputs structured, evidence‑grounded reports that enable analysts to audit decisions and support mitigation efforts.
By Jaishnoor Kaur, Josef Hor\'a\v{c}ek, Ian Waskom, Sayak Saha Roy
arXiv:2607. 12267v1 Announce Type: cross Abstract: Language agents that interleave reasoning and tool use degrade sharply as reasoning chains lengthen, even when each individual step is easy.
By Ning Liu
arXiv:2607. 17291v1 Announce Type: new Abstract: Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleading documents.
By Jun Nie, Zhiqin Yang, Zhenheng Tang, Yonggang Zhang, Xiaowen Chu, Xinmei Tian, Bo Han
The paper introduces SEEDS, a preview-guided evidence admission framework that allows search agents to selectively admit retrieved documents into their reasoning process. SEEDS exposes lightweight previews of candidates and lets the agent decide which and how many to fully inspect based on its current reasoning state, enabling arbitrary subset selection rather than fixed or rank-based policies. Experiments on nine knowledge-intensive QA benchmarks show that SEEDS improves search effectiveness and reduces full-evidence consumption compared to traditional retrieval prefixes.
By Jiajin Liu, Chenxi Xu, Dongzhe Fan, Yuanfu Sun, Qiaoyu Tan
arXiv:2609.06027v1 Announce Type: cross
Abstract: Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning. E...
By Zhongan Bi, Qiwen Wang, Jianrong Jiang, Jigang Ding, Wenwen Xiong, Changhua Meng, Xuanang Gao, Kepeng Lin, Changjiang Jiang, Yiang Chen, Huan Yao, Wei Wang, Zhenyu Ma, Wenhui Dong
arXiv:2608. 02011v2 Announce Type: replace Abstract: Agentic retrieval-augmented generation (RAG) systems can fail before evidence-conditioned reasoning is tested: an agent may retrieve candidate snippets but finalize without inspecting them.
By Daeyoung Roh, Donghee Han