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LongCat-Next shatters the language-centric paradigm by unifying text, vision, and audio into a single autoregressive model with minimal modality-specific design, finally reconciling understanding and generation in discrete vision modeling.
Current memory systems like RAG and long-context LLMs stumble in AMemGym's interactive long-horizon conversations, revealing critical performance gaps in maintaining consistent user state.
