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The Affective Flow Language Model is proposed, which models multi-turn emotional support as an affective utility flow evolving along dialogue trajectories, providing a process-based approach to dynamic affect modeling and continuous strategy optimization.
Leveraging reinforcement learning, MPAR$^2$ improves perception performance on CAFE from 31.74% to 63.51% and effectively mitigates perception decay, concurrently enhancing reasoning capabilities to achieve a significant 74.59% accuracy on the MMAU benchmark.
Multi-scale CIF (M-CIF), which performs multi-level alignment by integrating character and phoneme level supervision progressively distilled into subword representations, thereby enhancing robust acoustic-text alignment, is proposed.
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