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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.
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.
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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