Unified Audio Intelligence Model Audex: Audio-Text LLM Without Regressing on Text Intelligence
Score
77提出统一音频文本模型且不损失文本能力，数据规模大，方法新颖。
论文结构清晰，实验充分，开源模型，质量高。
统一模型可简化多模态部署，保留文本能力利于实际应用。
2025年7月论文，紧跟多模态LLM前沿。
arXiv预印本，来自Nemotron-Labs，业界认可度高。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper presents an innovative architecture and training method, achieving SOTA on multiple audio tasks while open-sourcing the model, offering significant impact.
AI commentary
「This paper is noteworthy for demonstrating how to preserve the powerful capabilities of a text-only model within a unified multimodal framework, which is critical for building general AI agents. For researchers and engineers working on speech interaction, multimodal LLMs, and AI infrastructure, Audex's architecture and training methods provide a reproducible reference. Particularly striking is its achievement of SOTA audio performance without degrading text intelligence, addressing a core challenge in multimodal learning.」
Quick overview
Nemotron-Labs introduces Audex, a unified audio-text LLM built on a 30B-parameter MoE text LLM, using a single Transformer decoder that fuses audio inputs into the text embedding space and treats text tokens and quantized audio output tokens uniformly. Trained on meticulously curated datasets of 157.4B audio tokens and 320.5B text tokens through multi-stage supervised training, Cascade RL, and multi-domain on-policy distillation, Audex achieves SOTA in audio understanding, speech recognition/translation, TTS, audio generation, and speech-to-speech generation, while preserving the backbone’s reasoning, alignment, knowledge, long-context, and agentic capabilities with minimal regression. Model checkpoints are open-sourced.
- Architecture: Single Transformer decoder with unified audio-text processing, compatible with standard LLM training/inference.
- Training: 157.4B audio tokens + 320.5B text tokens, multi-stage supervised training, text Cascade RL, multi-domain on-policy distillation.
- Performance: SOTA on audio understanding/generation tasks; text capabilities preserved with marginal or no regression.
Full text
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