# Telecom Fraud Detection

The work presents TeleAntiFraud 2.0, a refreshable Chinese audio benchmark for telecom fraud detection organized as monthly frozen snapshots, in which mixed-tree generation makes fraud and lawful near-domain calls share context and diverge only at label-bearing actions, and it reports that three text classifiers reach perfect Macro-F1 against unrelated or ordinary negatives but drop to 0.65-0.68 with near-domain sibling negatives, while full-set audio and ASR+LLM evaluations expose class-prior shortcuts, prediction collapse, and snapshot sensitivity.

The work presents TeleAntiFraud 2.0, a refreshable Chinese audio benchmark for telecom fraud detection organized as monthly frozen snapshots, in which mixed-tree generation makes fraud and lawful near-domain calls share context and diverge only at label-bearing actions, and it reports that three text classifiers reach perfect Macro-F1 against unrelated or ordinary negatives but drop to 0.65-0.68 with near-domain sibling negatives, while full-set audio and ASR+LLM evaluations expose class-prior shortcuts, prediction collapse, and snapshot sensitivity.

Without changing any parameters of the audio-language model, FRAUDSkill optimizes an external layer of skill programs, routing policies, and decision rules and combines structured output control with validation-guided multi-path inference, raising Macro-F1 on the TeleAntiFraud benchmark from 41.54% for the shared frozen-model baseline to 73.50% while cutting the invalid-output rate from 36.04% to 1.94%.

Without changing any parameters of the audio-language model, FRAUDSkill optimizes an external layer of skill programs, routing policies, and decision rules and combines structured output control with validation-guided multi-path inference, raising Macro-F1 on the TeleAntiFraud benchmark from 41.54% for the shared frozen-model baseline to 73.50% while cutting the invalid-output rate from 36.04% to 1.94%.