HARPO reframes conversational recommendation as hierarchical agentic reasoning with learned weights over quality dimensions and value-guided tree search, yielding better recommendation metrics on ReDial, INSPIRED, and MUSE.
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Cascaded systems remain the most reliable for speech translation overall, but recent SpeechLLMs match or outperform them in many conditions while standalone speech models lag.
The paper introduces the MICL scenario for MLLMs with modality and task shifts and proposes MoInCL using pseudo-target generation and instruction-based distillation, reporting gains over continual learning baselines on six tasks.
citing papers explorer
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HARPO: Hierarchical Agentic Reasoning for User-Aligned Conversational Recommendation
HARPO reframes conversational recommendation as hierarchical agentic reasoning with learned weights over quality dimensions and value-guided tree search, yielding better recommendation metrics on ReDial, INSPIRED, and MUSE.
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Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs
Cascaded systems remain the most reliable for speech translation overall, but recent SpeechLLMs match or outperform them in many conditions while standalone speech models lag.
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Modality-Inconsistent Continual Learning of Multimodal Large Language Models
The paper introduces the MICL scenario for MLLMs with modality and task shifts and proposes MoInCL using pseudo-target generation and instruction-based distillation, reporting gains over continual learning baselines on six tasks.