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$\tau$-Voice: Benchmarking Full-Duplex Voice Agents on Real-World Domains
🤖AI Summary
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.
Key Takeaways
- →Voice AI agents perform significantly worse than text-based systems, retaining only 30-45% of text capability under realistic conditions.
- →GPT-5 reasoning achieves 85% task completion in text, while voice agents reach only 31-51% under clean conditions.
- →Performance drops further to 26-38% when realistic audio conditions with noise and diverse accents are introduced.
- →79-90% of voice agent failures stem from agent behavior rather than technical limitations of the evaluation setup.
- →The τ-voice benchmark provides the first comprehensive framework for testing voice agents on complex, grounded real-world tasks.
Mentioned in AI
Models
GPT-5OpenAI
#voice-ai#benchmarking#full-duplex#ai-evaluation#performance-gap#real-world-testing#conversational-ai#audio-processing
Read Original →via arXiv – CS AI
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