{"type":"ScholarlyArticle","title":"An LLM-based Chain-of-Response Counter-Scam System","authors":[{"name":"Heedou Kim","slug":"heedou-kim","given":"Heedou","family":"Kim","affiliation":""},{"name":"Mogan Gim","slug":"mogan-gim","given":"Mogan","family":"Gim","affiliation":""},{"name":"Donghee Choi","slug":"donghee-choi","given":"Donghee","family":"Choi","affiliation":""},{"name":"Soonil Bae","slug":"soonil-bae","given":"Soonil","family":"Bae","affiliation":""},{"name":"Hoonick Lee","slug":"hoonick-lee","given":"Hoonick","family":"Lee","affiliation":""},{"name":"Mi-Young Kim","slug":"mi-young-kim","given":"Mi-Young","family":"Kim","affiliation":""},{"name":"Jaewoo Kang","slug":"jaewoo-kang","given":"Jaewoo","family":"Kang","affiliation":""}],"abstract":"The rapid evolution of online scams, driven by transnational networks and mass-produced social engineering scenarios, has exposed the speed limitations of conventional detection, necessitating tighter inter-agency coordination. While LLMs show promise in scam identification, their role in accelerating integrated response frameworks remains underexplored. We propose Counter-Scam, a unified LLM-based multi-agent framework that orchestrates end-to-end response from initial detection to crime investigation. The framework first proposes safe data guidelines, emphasizing non-public scam data and secure dataset construction via scam-specific NER. Developed with insights from 37 stakeholders to reduce delays and improve analytical efficiency, the system integrates CSRA (multi-agent mitigation), CSRT (nine role-aligned NLP tasks), and CSRD (a corpus of 185,300 scam cases and 38,587 knowledge entries). Experiments show that fine-tuned sLLMs surpass commercial models with over 10% in all CSRT tasks and a 0.24 F1 improvement in scam-specific NER. This proves the framework's capability for enabling rapid, collaborative mitigation of online scam.","doi":"10.24963/ijcai.2026/808","url":"https://ijcai.org/proceedings/2026/808","pdf":"https://ijcai.org/proceedings/2026/0808.pdf","markdown":"https://ijcai.org/proceedings/2026/808.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/808","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":808,"pages":{"first":7266,"last":7274},"keywords":["Natural Language Processing: Natural Language Processing","AI Ethics, Trust, Fairness: AI Ethics, Trust, Fairness","Agent-based and Multi-agent Systems: Agent-based and Multi-agent Systems","Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}