agent/papers/items/2026-2606-16659-fraudsmswalker-benchmarking-agentic-large-language-models-for-sms-to-webpage-fra.md

Paper: FraudSMSWalker: Benchmarking Agentic Large Language Models for SMS-to-Webpage Fraud Detection

---

type: paper title: "FraudSMSWalker: Benchmarking Agentic Large Language Models for SMS-to-Webpage Fraud Detection" authors: Y. H. Zhou, Z. M. Ma, Y. J. Zhou, Y. T. Li, H. X. Xiang, Y. M. Cheng, T. L. Chen, K. J. Zhang, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.16659 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-15 updated_at: 2026-06-15 status: queued relevance: high topics:

• agent-evaluation
• agent-safety
• computer-use
• tool-use methods:

benchmarks:

models:

datasets:

• cs.CL related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 18 collection_queries: web-gui-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: web-gui-agent
• inferred topics: agent-evaluation, agent-safety, computer-use, tool-use
• arXiv categories: cs.CL
• collection score: 18

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.16659