Skip to content

Author

Wenyi Yu

We have 5 of 21 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#artificial intelligence Preprint Oct 2026

Full-Duplex Interaction with Speech, Expressions and Gestures

Human face-to-face conversation requires coordinating speech, gestures and expressions across both speaking and listening. We present ELLSA 2, an end-to-end full-duplex dialogue model that jointly optimizes speech interaction and streaming gesture-expression generation. An autoregressive, chunk-wise synthesizer combine...

Si-Yin Wang, Wen-Yi Yu, T. Chiba et al. · 0 citations
#natural language process... Preprint Sep 2026

SALMONN-duo: Adaptive Dual-System Coordination for Full-Duplex Voice Agents

SALMONN-duo is proposed, an adaptive dual-system voice agent inspired by dual-process theories of cognition that separates real-time interaction from deliberative computation by pairing an always-on, fast-thinking full-duplex speech LLM with a powerful asynchronous slow-thinking LLM agent.

Wen-Yi Yu, Si-Yin Wang, T. Chiba et al. · 1 citation
Review Aug 2026

Towards general auditory intelligence for machine listening and speaking

This Review provides a comprehensive review of recent progress in integrating audio into large language models, with a focus on four key areas: audio comprehension, audio generation, speech-based interaction and audio–visual understanding.

Si-Yin Wang, Zengrui Jin, Changli Tang et al. · 1 citation
#small language model Preprint Aug 2026

Spectral Allocation: Why Muon Outperforms Adam, and How to Improve Muon

Spectral-Aware Muon is introduced, which holds the head at the Muon scale and amplifies the bulk using a static spectral prior, and both variants outperform tuned AdamW and Muon (Scion implementation) baselines in all evaluated model-scale and batch-size configurations.

Xiaodong Wu, Wen-Yi Yu, Chao Zhang et al. · 1 citation
Preprint Jul 2026

SALMONN-2: Advancing General-Purpose Hearing Abilities with Self-Supervised Representations

A multi-layer feature fusion (MLF) adapter that aggregates information from all encoder layers before projecting them into the language model is proposed and shows that MICL does not emerge naturally in ALLMs, but can be effectively acquired through targeted contextual biasing training.

Xiaoyu Yang, Xuenan Xu, Wenyi Yu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.