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This work introduces VAttnPO (Visual Attention-Guided Policy Optimization), which uses answer-conditioned visual attention to refine token-level credit while preserving the verifier-derived trajectory-mean advantage to the GRPO sequence advantage.
SpeechAgent-R, an audio agent that coordinates its intrinsic multimodal understanding with external skills and tools, is developed and HIU-Bench is introduced to jointly evaluate task performance, interaction quality and generalization to diverse task settings.
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