# Full-Duplex-Bench-v2: A Multi-Turn Evaluation Framework for Duplex Dialogue Systems with an Automated Examiner

arXiv: https://arxiv.org/abs/2510.07838 · HTML: https://mlsanity.com/paper/2510.07838/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2025.

## Built on by (most cited first, 30 of 41)

- [Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluency](https://mlsanity.com/paper/2604.04847/) (2026)
- [τ-Voice: Benchmarking Full-Duplex Voice Agents on Real-World Domains](https://mlsanity.com/paper/2603.13686/) (2026)
- [MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models](https://mlsanity.com/paper/2511.10262/) (2025)
- [Multi-Faceted Interactivity Alignment in Full-Duplex Speech Models](https://mlsanity.com/paper/2606.11167/) (2026)
- [Raon-Speech Technical Report](https://mlsanity.com/paper/2605.23912/) (2026)
- [TurnBench: A Multi-Domain Benchmark for Turn-Taking Dynamics in Spoken Dialogue](https://mlsanity.com/paper/2608.25218/) (2026)
- [Instruct-FD: Can Your Full-Duplex Speech System Follow Turn-Taking Instructions?](https://mlsanity.com/paper/2607.20460/) (2026)
- [Optimizing Conversational Quality in Spoken Dialogue Systems with Reinforcement Learning from AI Feedback](https://mlsanity.com/paper/2601.19063/) (2026)
- [EchoChain: A Full-Duplex Benchmark for State-Update Reasoning Under Interruptions](https://mlsanity.com/paper/2604.16456/) (2026)
- [IHBench: Evaluating Post-Interruption Recovery in Voice Agents with Structured Workflows](https://mlsanity.com/paper/2606.19595/) (2026)
- [OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants](https://mlsanity.com/paper/2605.26485/) (2026)
- [Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering](https://mlsanity.com/paper/2606.11386/) (2026)
- [DuplexWorld: Can voice agents help you get through the day?](https://mlsanity.com/paper/2608.10716/) (2026)
- [MSI-Bench: Evaluating Multi-Speaker Voice Interaction for Collaborative AI Agents](https://mlsanity.com/paper/2609.24812/) (2026)
- [ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models](https://mlsanity.com/paper/2604.10065/) (2026)
- [M3-DuplexBench: A Multi-Turn, Multilingual, Multidomain Benchmark for Full-Duplex Spoken Dialogue Models](https://mlsanity.com/paper/2607.29125/) (2026)
- [MTVA-Bench: Evaluating the Language Model Inside Cascaded Voice Agents](https://mlsanity.com/paper/2609.20152/) (2026)
- [MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant](https://mlsanity.com/paper/2609.13076/) (2026)
- [Same Words, Different Actions: Paired Turn-Taking Evaluation under Rewritten Dialogue Contexts](https://mlsanity.com/paper/2609.17360/) (2026)
- [A frontend-backend architecture for tool calls in full-duplex speech models](https://mlsanity.com/paper/2609.19334/) (2026)
- [TiCo: Time-Controllable Spoken Dialogue Model](https://mlsanity.com/paper/2603.22267/) (2026)
- [VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents](https://mlsanity.com/paper/2605.30256/) (2026)
- [A Reliability Assessment of LALM Audio Judges for Full-Duplex Voice Agents](https://mlsanity.com/paper/2607.07985/) (2026)
- [DuplexSpeechBench-IFEval: Evaluating Implicit Instruction Following in Full-Duplex Voice Agents](https://mlsanity.com/paper/2609.03423/) (2026)
- [Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction](https://mlsanity.com/paper/2609.21392/) (2026)
- [MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation](https://mlsanity.com/paper/2609.36903/) (2026)
- [PACE: A Playback-Aligned Context Engine for LLM-Based Full-Duplex Voice Dialogue](https://mlsanity.com/paper/2608.07631/) (2026)
- [Enabling Proactive Spoken Turns via a Generalized Style-Aware Full-Duplex Framework](https://mlsanity.com/paper/2608.28630/) (2026)
- [When Patients Cut In: Extending Clinical Conversational AI Safety to Interruptions](https://mlsanity.com/paper/2608.29241/) (2026)
- [DuplexJail: Safety Alignment Breaks Under Spoken Interruption in Full-Duplex Models](https://mlsanity.com/paper/2609.09420/) (2026)

## Abstract (arXiv)

While full-duplex speech agents enable natural, low-latency interaction by speaking and listening simultaneously, their consistency and task performance in multi-turn settings remain underexplored. We introduce Full-Duplex-Bench-v2 (FDB-v2), a streaming framework that integrates with an automated examiner that enforces staged goals under two pacing setups (Fast vs. Slow). FDB-v2 covers four task families: daily, correction, entity tracking, and safety. We report turn-taking fluency, multi-turn instruction following, and task-specific competence. The framework is extensible, supporting both commercial APIs and open source models. When we test full-duplex systems with FDB-v2, they often get confused when people talk at the same time, struggle to handle corrections smoothly, and sometimes lose track of who or what is being talked about. Through an open-sourced, standardized streaming protocol and a task set, FDB-v2 makes it easy to extend to new task families, allowing the community to tailor and accelerate evaluation of multi-turn full-duplex systems.