Abstract #
Duplex Cue evaluates how voice agents adapt to listener contributions during ongoing turns, revealing that current models adapt far less often than humans.
thinkingmachines/Inkling-Small Full-duplex evaluation often emphasizes whether an agent keeps speaking or stops. That binary cannot express a third response humans use routinely: continuing to speak while incorporating what the listener just contributed. The contribution may be a missing word, a correction or a clarification. We introduce Duplex Cue, an evaluation of this in-turn adaptation in full-duplex voice agents. Duplex Cue separates listener intent (backchannel, collaboration, or interruption) from speaker behavior: continuing unchanged, adapting within the turn, or yielding. Adaptation includes acknowledgment as well as content revision. In a single-model case study using 300 human-confirmed cues from unscripted English conversations, we compare recorded human responses with PersonaPlex continuations generated while replaying the listener's audio. We retain 208 pairs with the ongoing speaker active at cue onset and a scorable response in each condition. On the 66 collaborative pairs, recorded speakers adapt in 68.2% of cases, compared with 34.8% for PersonaPlex. The model otherwise continues unchanged (42.4%) or yields (22.7%). These findings show why evaluating natural voice interaction requires measuring how an agent responds to a listener's contribution as well as whether it keeps speaking.
Get this paper in your agent:
hf papers read 2609.13117 ## Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash ## Models citing this paper 0
No model linking this paper
Datasets citing this paper 0 #
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper