This tutorial provides an in-depth treatment of modern RAG based on AI-facilitated systematic analysis of ~2000 recent papers (2020--2026) and traces the RAG pipeline from its modular foundations through graph-enhanced reasoning to the latest RL-driven agentic architectures, covering each stage.
Xin Dong, Sanat Sharma, Kai Sun et al.· Proceedings of the 32nd ACM...· 0 citations
Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale recommendation models introduces two challenges: (1) long feedback loops, where model training can take days, making serial iteration prohibit...
Ming Li, Dai-Peng Li, Xu-Ying Ning et al.· 1 citation
This work synthesizes a task-conditioned temporal workflow graph that jointly specifies agent connectivity and edge-level communication semantics, and introduces ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.
Retrieval-Augmented Generation (RAG) has emerged as the dominant strategy to ground LLM outputs in retrieved evidence to reduce hallucinations and increase factuality, yet RAG itself introduces new challenges: noisy retrieval, knowledge conflicts, and wasted computation from unnecessary retrieval. This tutorial provide...
X. Dong, Sanat Sharma, Kai Sun et al.· Proceedings of the 32nd ACM...· 0 citations
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