arXiv AI

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

arXiv AI

Aug 7

Contextual Information Policy Optimization for Search Agents

arXiv:2608. 06128v1 Announce Type: new Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reasoning.

By Xingyu Guo, Wei Chen, Linlin Yang, Baochang Zhang

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jul 2

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning

arXiv:2607. 00341v1 Announce Type: cross Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT).

By Hengyu Fu, Tianyu Guo, Zixuan Wang, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Stuart Russell, Song Mei

llms rag benchmarks

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

llms

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

rag benchmarks

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

llms rag agents reinforcement-learning benchmarks safety

arXiv AI

Aug 11

SAKE: Structured Agentic Knowledge Extrapolation for Complex LLM Reasoning via Reinforcement Learning

arXiv:2505. 15062v5 Announce Type: replace-cross Abstract: Knowledge extrapolation is the process of inferring novel information by combining and extending existing knowledge that is explicitly available.

By Jiashu He, Jinxuan Fan, Bowen Jiang, Ignacio Houine, Dan Roth, Alejandro Ribeiro

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

Process Reward Agents for Steering Knowledge-Intensive Reasoning

arXiv:2604. 09482v2 Announce Type: replace Abstract: Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external knowledge sources.

By Jiwoong Sohn, Tomasz Sternal, Kenneth Styppa, Torsten Hoefler, Michael Moor

rag agents benchmarks

arXiv AI

Jul 14

CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps

arXiv:2607. 10562v1 Announce Type: new Abstract: Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge.

By JungMin Yun, JuneHyoung Kwon, YoungBin Kim

llms nlp

arXiv AI

Jul 3

ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

arXiv:2607. 02509v1 Announce Type: new Abstract: Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications.

By Yanjun Zhao, Ruizhong Qiu, Tianxin Wei, Yuanchen Bei, Zhining Liu, Lingjie Chen, Ismini Lourentzou, Hanghang Tong, Jingrui He

llms efficiency

arXiv Computation and Language

Aug 25

GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning

arXiv:2608.22479v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop quest...

By Jun Chen, Yongchao Liu, Pengyu Qiu, Jiajun Zheng, Juelu Zhang, Yujie Zeng, Qin Zhang, Ziyue Qiao, Xiao Luo

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jun 30

HippoSpark: An On-Demand Experience System for LLM Reasoning

arXiv:2606. 29929v1 Announce Type: new Abstract: Distilling historical trajectories into reusable experience to enhance future problem-solving has become a focal point of recent LLM research.

By Jingyao Liu, Danling Meng, Chen Huang, Yukun Yan, Zhenghao Liu, Wenqiang Lei, See-Kiong Ng, Maosong Sun

llms benchmarks

arXiv AI

Jun 9

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

arXiv:2605. 12213v2 Announce Type: replace Abstract: LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context.

By Jiazhou Liang, Armin Toroghi, Yifan Simon Liu, Faeze Moradi Kalarde, Liam Gallagher, Scott Sanner

llms rag agents

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