Qwen-Audio-Agent Technical Report
A foreground-background architecture separates voice interaction from asynchronous task execution, coordinating authorization, state and delivery.
Open models · Multimodality · Agents. Selected primary papers, reports, model cards and official technical articles. Checked 2026-10-25. Product announcements are not presented as papers.
A foreground-background architecture separates voice interaction from asynchronous task execution, coordinating authorization, state and delivery.
A diffusion-model post-training pipeline combines task-specific rewards, reinforcement learning and on-policy distillation.
A language-conditioned video world model predicts future visual trajectories across manipulation, navigation and driving settings.
The report explores an omni-modal model family and training over heterogeneous visual, textual and audio data.
An open-weight model for coding agents explores strong training recipes with a small active parameter footprint.
A speech-recognition family and a forced-alignment model address multilingual transcription and timestamp prediction.
The Qwen vision-language family integrates text, image and video understanding across several model sizes.
Dense and mixture-of-experts models integrate thinking and non-thinking behavior, with configurable inference-time reasoning.
This index currently holds 8 records attributed to Alibaba Qwen (2025–2026). Each entry links to the primary source; product announcements and news posts are not counted as papers.
These records come from other institutions but address the same topics, so the lab can be read in context.