We have 4 of 8 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
A dataset of real scam-call conversations collected by an active voice-agent honeypot, describing the collection system, record structure, and technical validation of the corpus's realism and label quality, including that the agent is recognized as non-human in only about 5% of engaged calls.
Screening a phone call for fraud needs a trustworthy probability after every caller turn, in milliseconds. Jev-style typed decisions promise exactly that: declared options go in, one calibrated probability per option comes out of a single forward pass, with no generated text. We test an open implementation of this read...
This work presents CallScreenBench, which reports five automated call-and-note measure groups motivated by owner endorsement, and reports quality measures and guardedness channels separately so that a single pass/fail score does not hide their trade-offs.
This work analyzes a complete corpus of 10,211 inbound scam and spam calls collected over 54 days by an AI voice-agent honeypot that answered callers and kept them talking, and introduced in a companion data descriptor.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
