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Full-duplex speech models can listen and speak simultaneously, enabling natural interaction, but become increasingly difficult to control as the conversation history grows. When used as user simulators, this lack of control can cause them to deviate from prescribed scenarios and produce unreliable evaluation outcomes....
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.
This work introduces Few-Shot Aware GRPO (FSA-GRPO), an RL-based post-training recipe that uses a specially designed reward to encourage the model to leverage few-shot demonstrations, thereby strengthening its few-shot adaptation ability.
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.
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