arXiv:2607.02119v1 Announce Type: cross Abstract: While Large Multimodal Models excel in comprehension, high-throughput inference engines lack native support for multimodal generation. This is severe in Speech Language Models, where generating multi-layered audio tokens via decoupled AR+NAR or synchronous Multi-Token Prediction (MTP) with delay-pattern interleaving conflicts with standard single-stream loops. We present a vLLM-based inference pipeline for unified speech understanding and generation. We extend autoregressive decoding to natively execute delay-pattern de-interleaving and coordin
The paper addresses a current limitation in high-throughput inference engines for Large Multimodal Models (LMMs), specifically in efficient multimodal generation, which is a nascent but rapidly developing field.
This development proposes a unified pipeline for speech understanding and generation in LMMs, overcoming previous architectural conflicts and hinting at a significant acceleration in the deployment of advanced voice AI.
Current LMM inference pipelines become more efficient at handling complex audio tasks by integrating generation and understanding seamlessly, leading to more responsive and capable speech language models.
- · AI compute providers
- · Speech AI developers
- · Voice assistant companies
- · Generative AI platforms
- · Companies reliant on decoupled or less efficient multimodal inference architectu
More sophisticated and seamless conversational AI experiences will become possible.
Reduced latency and increased throughput for multimodal AI could accelerate its integration into enterprise applications and smart devices.
The enhanced efficiency in speech generation may pave the way for more human-like AI interactions, potentially impacting customer service and media creation industries.
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Read at arXiv cs.AI