# Unit-Based Agent for Semi-Cascaded Full-Duplex Dialogue Systems

arXiv: https://arxiv.org/abs/2601.20230 · HTML: https://mlsanity.com/paper/2601.20230/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 13 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 13 of 13)

- [Full-Duplex-Bench v1.5: Evaluating Overlap Handling for Full-Duplex Speech Models](https://mlsanity.com/paper/2507.23159/) (2025)
- [A Full-duplex Speech Dialogue Scheme Based On Large Language Models](https://mlsanity.com/paper/2405.19487/) (2024)
- [OSUM-EChat: Enhancing End-to-End Empathetic Spoken Chatbot via Understanding-Driven Spoken Dialogue](https://mlsanity.com/paper/2508.09600/) (2025)
- [FireRedChat: A Pluggable, Full-Duplex Voice Interaction System with Cascaded and Semi-Cascaded Implementations](https://mlsanity.com/paper/2509.06502/) (2025)
- [Chronological Thinking in Full-Duplex Spoken Dialogue Language Models](https://mlsanity.com/paper/2510.05150/) (2025)
- [Easy Turn: Integrating Acoustic and Linguistic Modalities for Robust Turn-Taking in Full-Duplex Spoken Dialogue Systems](https://mlsanity.com/paper/2509.23938/) (2025)
- [Game-Time: Evaluating Temporal Dynamics in Spoken Language Models](https://mlsanity.com/paper/2509.26388/) (2025)
- [From Turn-Taking to Synchronous Dialogue: A Survey of Full-Duplex Spoken Language Models](https://mlsanity.com/paper/2509.14515/) (2025)
- [Chain-of-Thought Training for Open E2E Spoken Dialogue Systems](https://mlsanity.com/paper/2506.00722/) (2025)
- [Chain-of-Thought Reasoning in Streaming Full-Duplex End-to-End Spoken Dialogue Systems](https://mlsanity.com/paper/2510.02066/) (2025)
- [The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era](https://mlsanity.com/paper/2601.05564/) (2026)
- [Towards General Auditory Intelligence: Large Multimodal Models for Machine Listening and Speaking](https://mlsanity.com/paper/2511.01299/) (2025)
- [MIKU-PAL: An Automated and Standardized Multi-Modal Method for Speech Paralinguistic and Affect Labeling](https://mlsanity.com/paper/2505.15772/) (2025)

## Built on by (most cited first, 1 of 1)

- [PACE: A Playback-Aligned Context Engine for LLM-Based Full-Duplex Voice Dialogue](https://mlsanity.com/paper/2608.07631/) (2026)

## Abstract (arXiv)

Full-duplex voice interaction is crucial for natural human computer interaction. We present a framework that decomposes complex dialogue into minimal conversational units, enabling the system to process each unit independently and predict when to transit to the next. This framework is instantiated as a semi-cascaded full-duplex dialogue system built around a multimodal large language model, supported by auxiliary modules such as voice activity detection (VAD) and text-to-speech (TTS) synthesis. The resulting system operates in a train-free, plug-and-play manner. Experiments on the HumDial dataset demonstrate the effectiveness of our framework, which ranks second among all teams on the test set of the Human-like Spoken Dialogue Systems Challenge (Track 2: Full-Duplex Interaction). Code is available at the GitHub repository https://github.com/yu-haoyuan/fd-badcat.