arXiv AI By Rubing Chen, Jian Wang, Wenjie Li, Xiao-Yong Wei, Qing Li

To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Computation and Language
Oct 22

Reasoning-Search-Augmented LLMs: An Operational Taxonomy, Benchmark Analysis and Controlled Evidence Synthesis

The paper reviews and categorizes large language models that combine step‑by‑step reasoning with interactive search, treating retrieval as an action within an iterative agentic loop. It analyzes these systems along four architectural axes—retrieval coupling, controller design, knowledge source, and grounding strictness—while also considering the learning signal as a cross‑cutting factor. The authors conduct controlled ablations and benchmark analyses to clarify how design choices affect search, stopping, and grounding behavior, aiming to provide a unified perspective for future research.

By Biswas Poudel, Nilson Chapagain, Amit Kumar, Xianshun Jiang
arXiv AI
Aug 6

Chained Recursive Language Models for Multi-Iteration Reasoning

arXiv:2608. 05124v1 Announce Type: cross Abstract: Long context reasoning in large language models (LLMs) is usually constrained by the fact that a single inference trajectory has to simultaneously explore the context, store intermediate state, verify evidence, and produce the final answer.

By Purbesh Mitra, Sennur Ulukus
arXiv AI
Sep 7

A Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence Gathering

A new framework called APT‑RAG is introduced for evidence‑intensive question answering. It uses adaptive planning to expand a tree‑based reasoning structure according to question dependencies and evidence needs, and employs topology‑aware evidence gathering that reuses sibling evidence, retrieves directly, and aggregates evidence from child nodes. The approach also includes evidence‑guided batched answer generation to reduce generation overhead, and experimental results show it outperforms existing structured RAG methods on evidence‑intensive QA benchmarks.

By Songeun Lee, Kyungjin Min, Injae Na, Suyeong Lee, Chiyoung Kim, Woohwan Jung
arXiv AI
Sep 1

AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing

AgenticRag‑R1 is a reinforcement‑learning framework that integrates reasoning, retrieval, and memory through a stack and fine‑grained action space. It uses hierarchical action‑aware rewards and an information‑aware trajectory rejection strategy to support long‑horizon learning. Experiments on multi‑hop, open‑domain, and agentic reasoning benchmarks show that AgenticRag‑R1 outperforms strong baselines and produces robust, interpretable, memory‑aware reasoning behaviors.

By Xinke Jiang, Yue Fang, Zhibang Yang, Jiaran Gao, Zhixin Zhang, Tao Feng, Rihong Qiu, Wentao Zhang, Hongxin Ding, Ruizhe Zhang, Yongxin Xu, Yuheng Huang, Xu Chu, Junfeng Zhao, Yasha Wang