Auto86%Harbor: pendingAgentic / Tool UseMultimodal

DuplexAct

DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models?

A benchmark with 4,691 instances evaluating interaction action success in full-duplex spoken language models across real-world and synthetic scenarios.

How to run

ltb · duplexact
$ npm i -g lettokenburn-cli
$ ltb bench info duplexact

External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

13 sourced entries
HarnessSourceConfidenceLink
GPT Realtime 2.195.7direct replayIndependentmediumOct 13, 2026arxiv.org
Gemini 3.1 Flash Live85.4direct replayIndependentmediumOct 13, 2026arxiv.org
MiniCPM-o 4.574.4direct replayIndependentmediumOct 13, 2026arxiv.org
Grok Voice74.3direct replayIndependentmediumOct 13, 2026arxiv.org
Raon-SpeechChat70.3direct replayIndependentmediumOct 13, 2026arxiv.org
VITA-1.564.4direct replayIndependentmediumOct 13, 2026arxiv.org
Lychee-FD53.1direct replayIndependentmediumOct 13, 2026arxiv.org
Voila-Autonomous45.8direct replayIndependentmediumOct 13, 2026arxiv.org
Moshi45.6direct replayIndependentmediumOct 13, 2026arxiv.org
Mini-Omni232.7direct replayIndependentmediumOct 13, 2026arxiv.org
BayLing-Duplex31.2direct replayIndependentmediumOct 13, 2026arxiv.org
Freeze-Omni23.2direct replayIndependentmediumOct 13, 2026arxiv.org
FLM-Audio18.3direct replayIndependentmediumOct 13, 2026arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

All runs
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About

DuplexAct contains 4,691 test instances spanning casual conversation, meetings, customer service, and interviews, with 54% derived from real-world recordings. It defines a three-level taxonomy of eight interaction actions and 24 conditions.

It evaluates success via immediate execution and subsequent follow-through using action-specific metrics. The benchmark reveals uneven model performance and weak subsequent reasoning capabilities across the action space.