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SkillIR is proposed, a skill-guided framework that represents restoration experience as degradation-centered action evidence rather than complete tool-use trajectories rather than complete tool-use trajectories and improves restoration quality and enables more reliable and effective tool use.
A novel recommendation agent framework, termed as ReMem, that combines OCR-based multimodal perception with time-evolving dynamic memory, which allows the agent to preserve evolving user preferences without relying on external memory modules or disrupting the standard autoregressive generation process.
A novel RAIE taxonomy along four scaling dimensions is proposed, which optimizes the entire thought process through search algorithms and self-verification, and introduces a task-oriented guideline for choosing the best TTS strategy.
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