3 articles tagged with #full-duplex. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce τ-voice, a new benchmark for evaluating full-duplex voice AI agents on complex real-world tasks. The study reveals significant performance gaps, with voice agents achieving only 30-45% of text-based AI capability under realistic conditions with noise and diverse accents.
Researchers introduce S-MARC, a streaming framework for modeling conversational behavior in full-duplex dialogue systems that predicts communicative functions and interaction behaviors while capturing their causal relationships. The system generates interpretable reasoning chains and establishes benchmarks for conversational AI reasoning, advancing natural human-computer interaction capabilities.
DuplexCascade introduces a VAD-free cascaded streaming pipeline that enables full-duplex speech-to-speech dialogue while maintaining LLM intelligence. The system converts traditional long utterance turns into micro-turn interactions using special control tokens to coordinate turn-taking and response timing.
