Papers17

#Retrieval-Augmented Generation

SmartSearch: Process Reward-Guided Query Refinement for Search Agents

Tongyu Wen, Guanting Dong et al.Jan 8arXiv

SmartSearch teaches search agents to fix their own bad search queries while they are thinking, not just their final answers.

#Search agents#Process rewards#Query refinement

AT$^2$PO: Agentic Turn-based Policy Optimization via Tree Search

Intermediate

Zefang Zong, Dingwei Chen et al.Jan 8arXiv

AT2PO is a new way to train AI agents that work in several turns, like asking the web a question, reading the result, and trying again.

#Agentic Reinforcement Learning#Turn-level Optimization#Tree Search

Multi-hop Reasoning via Early Knowledge Alignment

Intermediate

Yuxin Wang, Shicheng Fang et al.Dec 23arXiv

This paper adds a tiny but powerful step called Early Knowledge Alignment (EKA) to multi-step retrieval systems so the model takes a quick, smart look at relevant information before it starts planning.

#Retrieval-Augmented Generation#Iterative RAG#Multi-hop Reasoning

QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation

Intermediate

Dehai Min, Kailin Zhang et al.Dec 22arXiv

QuCo-RAG is a new way to decide when an AI should look things up while it writes, using facts from its training data instead of its own shaky confidence.

#Dynamic RAG#Retrieval-Augmented Generation#Uncertainty Quantification

Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

Intermediate

Yuqing Li, Jiangnan Li et al.Dec 19arXiv

Humans keep a big-picture memory (a “mindscape”) when reading long things; this paper teaches AI to do the same.

#Retrieval-Augmented Generation#Mindscape#Hierarchical Summarization

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