arXiv:2608.28630v1 Announce Type: cross
Abstract: Compared with half-duplex dialogue systems where the system waits for user turn completion before it responds, natural full-duplex dialogue systems r...
By Tianrui Pan, Qinglin Zhang, Chong Deng, Luyao Cheng, Qian Chen, Wen Wang, Jie Tang, Gangshan Wu, Jie Liu
The paper introduces SimIF-Bench, a benchmark that tests whether full‑duplex conversational models can stay within a prescribed scenario and complete multiple goals in order. It shows that current open‑source models struggle with these constraints. The authors then propose a GDPO‑based training recipe that allows a full‑duplex model, named SteerablePlex, to follow textual instructions during conversation while preserving turn‑taking, and demonstrate that it outperforms existing open‑source models and GPT‑Realtime when used as a controllable user simulator.
By Haolong Zheng, Maike Z\"ufle, Dominik Mach\'a\v{c}ek, Peter Pol\'ak, Xulin Fan, Xavier Sumba, Siyin Wang, Ond\v{r}ej Klejch, Mark Hasegawa-Johnson
arXiv:2606.11167v2 Announce Type: replace
Abstract: Full-duplex spoken dialogue models can listen and speak simultaneously, making them a promising architecture for natural conversation. However, cur...
By Atsumoto Ohashi, Neil Zeghidour, Alexandre D\'efossez, Eugene Kharitonov
TurnBench is a new multi‑domain benchmark for evaluating turn‑taking dynamics in spoken dialogue. It comprises a 30‑hour hand‑labeled corpus of dyadic human conversations, a standardized evaluation protocol for end‑of‑turn and interruption detection, and covers six distinct interaction styles with triple annotation. The benchmark also provides a 104‑hour training set, a public leaderboard, and an interactive dataset viewer at https://turnbench.sesame.com.
By Freeman Jiang, Ramon Sanabria, Soham Deshmukh, Bandhav Veluri, Simon Michael Vuch Williams, Elliott K. Suen, Garreth Lee, Kevin Yoonho Choi, Takuya Umeki, Riku Kubo, Sathvik Udupa, Chien-yu Huang, Shih-Yun Shan Kuan, Zhuoyan Tao, Satyapriya Krishna, Sefik Emre Eskimez, Yu Tsao, Hung-yi Lee, Shinji Watanabe
arXiv:2609.38867v1 Announce Type: new
Abstract: Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular in...
By Terumi Chiba, Guangzhi Sun, Zheqi Yuan, Chao Zhang
arXiv:2609.27372v1 Announce Type: cross
Abstract: Benchmarks for full-duplex spoken dialogue models score turn-taking with binary fixed-window rules that reward immediate response or silence by compl...
By Kian Shamsaie, Iman Modarressi