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Adaptation of Agentic AI

Intermediate

Pengcheng Jiang, Jiacheng Lin et al.Dec 18arXiv

This paper organizes how AI agents learn and improve into one simple map with four roads: A1, A2, T1, and T2.

#agentic AI#adaptation#A1 A2 T1 T2

RePo: Language Models with Context Re-Positioning

Intermediate

Huayang Li, Tianyu Zhao et al.Dec 16arXiv

Large language models usually line words up in fixed order slots, which can waste mental energy and make it harder to find the important parts of a long or noisy text.

#context re-positioning#positional encoding#self-attention

Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication

Intermediate

Xiaoquan Zhi, Hongke Zhao et al.Dec 1arXiv

Clinical conversations are special because they mix caring feelings with precise medical facts, and old AI systems struggled to do both at once.

#clinical dialogue#agentic AI#large language models

Papers27

Adaptation of Agentic AI

RePo: Language Models with Context Re-Positioning

Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication