Explainable Agentic System Detects Conversational Scams with Summary-Based Memory

Jul 14, 2026

Researchers present an explainable agentic system for detecting conversational scams that unfold over weeks or months, leveraging summary-based memory. The system achieves 100% phishing recall on isolated messages and 97.8% accuracy on the new ConScamBench-278 benchmark. User studies indicate increased user trust, self-confidence, and perceived need for AI-based scam detection after interacting with the system. The work also introduces ConScamBench-278, a public benchmark for reproducible evaluation of conversational scam detection.

Why it matters: This work addresses the challenge of detecting long-term conversational scams, which are not effectively caught by existing single-message detectors, and provides a new public benchmark for the field.

Full story at: arXiv Multiagent Systems ↗