Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluency
Abstract
We introduce Full-Duplex-Bench-v3 (FDB-v3), a benchmark for evaluating spoken language models under naturalistic speech conditions and multi-step tool use. Unlike prior work, our dataset consists entirely of real human audio annotated for five disfluency categories, paired with scenarios requiring chained API calls across four task domains. We evaluate six model configurations -- GPT-Realtime, Gemini Live 2.5, Gemini Live 3.1, Grok, Ultravox v0.7, and a traditional Cascaded pipeline (Whisper$\rightarrow$GPT-4o$\rightarrow$TTS) -- across accuracy, latency, and turn-taking dimensions. GPT-Realtime leads on Pass@1 (0.600) and interruption avoidance (13.5\%); Gemini Live 3.1 achieves the fastest latency (4.25~s) but the lowest turn-take rate (78.0\%); and the Cascaded baseline, despite a perfect turn-take rate, incurs the highest latency (10.12~s). Across all systems, self-correction handling and multi-step reasoning under hard scenarios remain the most consistent failure modes.
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Research summary
The paper introduces Full-Duplex-Bench-v3, a benchmark for evaluating real-time voice agents on multi-step tool execution using natural human speech. This work significantly contributes to the field by addressing the challenges of disfluency handling and tool use in voice interactions, paving the way for more effective and responsive AI systems.
Methodology
The methodology is robust, introducing a novel benchmark (FDB-v3) that evaluates spoken language models under realistic conditions, utilizing real human audio annotated for disfluencies. The design incorporates multi-step tool use across various domains, which is a significant advancement over previous benchmarks that relied on synthetic data or single-step tasks. The systematic approach to scenario formulation and audio collection enhances the validity of the evaluation.
Results & evaluation
The experiments are comprehensive, evaluating six different model configurations across multiple dimensions such as accuracy, latency, and turn-taking dynamics. The results are well-presented, showing clear performance differences among models and highlighting specific strengths and weaknesses, particularly in handling disfluencies and multi-step reasoning. The use of deterministic mock APIs for evaluation is a strong point, ensuring that the results are not confounded by external factors.
Reproducibility
The paper provides sufficient detail regarding the experimental setup, including the models evaluated and the evaluation metrics used. However, the lack of specific implementation details or code availability limits reproducibility. The benchmark is open and reproducible, which is a positive aspect, but without access to the models, full replication of results may be challenging.
Limitations
The study acknowledges limitations, such as the fixed server region for cloud-based evaluations and the lack of robustness testing against real-world network anomalies. Additionally, the dataset is relatively small (100 recordings), which may affect generalizability. The focus on specific disfluency categories may also overlook other potential challenges in real-world interactions.
Broader impact
This work has significant implications for the development of real-time voice agents, particularly in enhancing their ability to handle natural speech disfluencies and multi-step tasks. The findings suggest directions for future research, emphasizing the need for models that can balance speed and accuracy in dynamic conversational contexts. The benchmark itself could facilitate further advancements in the field by providing a standardized evaluation framework. The paper introduces Full-Duplex-Bench-v3, a benchmark for evaluating real-time voice agents on multi-step tool execution using natural human speech. This work significantly contributes to the field by addressing the challenges of disfluency handling and tool use in voice interactions, paving the way for more effective and responsive AI systems.
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