Awesome Retrieval for Agentic Search

An extensive and commented list of resources on agentic search and agentic deep research — systems where an LLM acts as an autonomous agent that plans, searches, inspects, and refines its retrieval in an iterative reasoning loop, rather than retrieving once and generating.

Contents

• Foundations
  • Classic RAG
• Retrieval Interface
  • Retriever Training
  • Reranking
  • Structured & Hierarchical Retrieval
  • Direct Corpus Interaction
  • Retrieval Analysis & Evaluation
• The Agentic Search Loop
  • Planning & Reasoning
  • Query Formulation & Decomposition
  • Adaptive Retrieval Control
  • Context & Memory Management
• Training & Optimization
  • RL-based Training
  • Data Synthesis
  • Analysis & Ablations
• Systems & Benchmarks
  • Systems & Frameworks
  • Benchmarks
• Resources
  • Surveys & Position Papers

Foundations

Classic RAG

The retrieve-then-generate paradigm that agentic search builds upon: a single retrieval step followed by generation, without iterative planning or tool use.

• REALM: Retrieval-Augmented Language Model Pre-Training
  Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang
  ICML, 2020
  📄 paper

• Dense Passage Retrieval for Open-Domain Question Answering
  Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
  EMNLP, 2020
  📄 paper | 🛠️ code

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
  Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela
  NeurIPS, 2020
  📄 paper | 🛠️ code

• Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
  Gautier Izacard, Edouard Grave
  EACL, 2021
  📄 paper | 🛠️ code

Retrieval Interface

How the agent accesses knowledge sources — the substrate underlying the entire agentic search loop, from retriever training to structured indexing to raw corpus interaction.

Retriever Training

Training the retriever/embedder itself to be reasoning-aware and agentic-search-aware, rather than treating it as a fixed component.

• O1 Embedder: Let Retrievers Think Before Action
  Ruiran Yan, Zheng Liu, Defu Lian
  arXiv, 2025
  📄 paper

• DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning
  Pengcheng Jiang, Jiacheng Lin, Lang Cao, Runchu Tian, SeongKu Kang, Zifeng Wang, Jimeng Sun, Jiawei Han
  COLM, 2025
  📄 paper | 🛠️ code

• ReasonIR: Training Retrievers for Reasoning Tasks
  Rulin Shao, Rui Qiao, Varsha Kishore, Niklas Muennighoff, Xi Victoria Lin, Daniela Rus, Bryan Kian Hsiang Low, Sewon Min, Wen-tau Yih, Pang Wei Koh, Luke Zettlemoyer
  arXiv, 2025
  📄 paper | 🛠️ code

• ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning
  Changtai Zhu, Siyin Wang, Ruijun Feng, Kai Song, Xipeng Qiu
  EMNLP, 2025
  📄 paper | 🛠️ code

• RaDeR: Reasoning-aware Dense Retrieval Models
  Debrup Das, Seán Ó Nualláin, Razieh Rahimi
  ICML, 2025
  📄 paper

• TongSearch-QR: Reinforced Query Reasoning for Retrieval
  Xubo Qin, Jun Bai, Jiaqi Li, Zixia Jia, Zilong Zheng
  arXiv, 2025
  📄 paper | 🛠️ code

• DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval
  Meixiu Long, Duolin Sun, Dan Yang, Junjie Wang, Yecheng Luo, Yue Shen, Jian Wang, Hualei Zhou, Chunxiao Guo, Peng Wei, Jiahai Wang, Jinjie Gu
  arXiv, 2025
  📄 paper

• Search-R3: Unifying Reasoning and Embedding Generation in Large Language Models
  Yuntao Gui, James Cheng
  arXiv, 2025
  📄 paper

• ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval
  Jianlyu Chen, Junwei Lan, Chaofan Li, Defu Lian, Zheng Liu
  arXiv, 2025
  📄 paper | 🛠️ code

• Agentic-R: Learning to Retrieve for Agentic Search
  Wenhan Liu, Xinyu Ma, Yutao Zhu, Yuchen Li, Daiting Shi, Dawei Yin, Zhicheng Dou
  arXiv, 2026
  📄 paper

• LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval
  Jiajie Jin, Yanzhao Zhang, Mingxin Li, Dingkun Long, Pengjun Xie, Yutao Zhu, Zhicheng Dou
  SIGIR, 2026
  📄 paper | 🛠️ code

• AgentIR: Reasoning-Aware Retrieval for Deep Research Agents
  Zijian Chen, Xueguang Ma, Shengyao Zhuang, Jimmy Lin, Akari Asai, Victor Zhong
  arXiv, 2026
  📄 paper | 🛠️ code

• Learning to Retrieve from Agent Trajectories (LRAT)
  Yuqi Zhou, Sunhao Dai, Changle Qu, Liang Pang, Jun Xu, Ji-Rong Wen
  SIGIR, 2026
  📄 paper | 🛠️ code

• CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
  Hansi Zeng, Liam Collins, Bhuvesh Kumar, Neil Shah, Hamed Zamani
  arXiv, 2026
  📄 paper | 🛠️ code

• Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems (RTriever / BRIGHT-Pro)
  Yilun Zhao, Jinbiao Wei, Tingyu Song, Siyue Zhang, Chen Zhao, Arman Cohan
  arXiv, 2026
  📄 paper

• LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG
  Yijia Zheng, Marcel Worring
  arXiv, 2026
  📄 paper

• Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback
  Md Zarif Ul Alam, Alireza Salemi, Hamed Zamani
  arXiv, 2026
  📄 paper

• RL-Index: Reinforcement Learning for Retrieval Index Reasoning
  Yongjia Lei, Zhisheng Qi, Utkarsh Sahu, Yu Wang, Nedim Lipka, Koustava Goswami, Franck Dernoncourt, Ryan A. Rossi
  arXiv, 2026
  📄 paper

• Retrieval Grounding Latent Reasoning for Dense Retrieval (RGLT)
  Gang Zhou, Xiongxi Yu, Hu Tian, Yang Wei, Lu Pan, Ke Zeng, Shibiao Xu, Xiaolong Zheng
  arXiv, 2026
  📄 paper

• Navigation-Informed Embeddings: Dense-Retriever Adaptation from Agent Search Traces
  Shrey Shah, Levent Ozgur
  arXiv, 2026
  📄 paper

• ITER: Interaction-Aware Retrieval for Agentic Search
  Haodong Chen, Shuai Wang, Yu Yin, Shengyao Zhuang, Guido Zuccon, Teerapong Leelanupab
  arXiv, 2026
  📄 paper | 🛠️ code

Reranking

Reranking components made reasoning-aware or reorganized around agentic search, rather than a fixed sequential pass.

• Beyond Sequential Reranking: Reranker-Guided Search Improves Reasoning Intensive Retrieval
  Haike Xu, Miao Zhang, Yipeng Kang, Zeyu Zhang, Sian-Chen Huang, Piotr Indyk
  arXiv, 2025
  📄 paper

• LimRank: Less is More for Reasoning-Intensive Information Reranking
  Tingyu Song, Yilun Zhao, Siyue Zhang, Chen Zhao, Arman Cohan
  EMNLP, 2025
  📄 paper | 🛠️ code

• Adaptive Retrieval for Reasoning-Intensive Retrieval (REPAIR)
  Jongho Kim, Jaeyoung Kim, Seung-won Hwang, Jihyuk Kim, Yu Jin Kim, Moontae Lee
  arXiv, 2026
  📄 paper

• Reproducing Adaptive Reranking for Reasoning-Intensive IR
  Mandeep Rathee, Venktesh V, Sean MacAvaney, Avishek Anand
  SIGIR, 2026
  📄 paper

• Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
  Sangkwon Park, Donghun Kang, Jisoo Mok, Sungroh Yoon
  arXiv, 2026
  📄 paper | 🛠️ code

• MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval
  Chunyu Li, Mengyuan Zhang, Jingyi Kang, Ding Chen, Jiajun Shen, Bo Tang, Xuanhe Zhou, Feiyu Xiong, Zhiyu Li
  arXiv, 2026
  📄 paper

• Tool-Adaptive LLM Reranker (TALRanker)
  Zichuan Liu, Ruijin Hua
  arXiv, 2026
  📄 paper

• Training Documents Reranker with Search Rubrics for Deep Research Agent (RubricRanker)
  Wenhan Liu, Yu Lu, Qiaolin Xia, Hui Xu, Tong Zhao, Jian Xi, Yutao Zhu, Haijin Liang, Haibo Shi, Hao Wang, Zhicheng Dou
  arXiv, 2026
  📄 paper

Structured & Hierarchical Retrieval

Reorganizing the corpus itself into structures (graphs, hierarchies, wikis) that agents can traverse, rather than a flat similarity index.

• From Local to Global: A Graph RAG Approach to Query-Focused Summarization (GraphRAG)
  Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Jonathan Larson
  arXiv, 2024
  📄 paper | 🛠️ code

• HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
  Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, Yu Su
  NeurIPS, 2024
  📄 paper | 🛠️ code

• From RAG to Memory: Non-Parametric Continual Learning for Large Language Models (HippoRAG 2)
  Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, Yu Su
  arXiv, 2025
  📄 paper | 🛠️ code

• Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning
  Junnan Dong, Siyu An, Yifei Yu, Qianwen Zhang, Linhao Luo, Xiao Huang, Yunsheng Wu, Di Yin, Xing Sun
  ICLR, 2026
  📄 paper | 🛠️ code

• LLM-Guided Hierarchical Retrieval (LATTICE)
  Nilesh Gupta, Wei-Cheng Chang, Ngot Bui, Cho-Jui Hsieh, Inderjit S. Dhillon
  arXiv, 2025
  📄 paper | 🛠️ code

• Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration
  Yuejie Li, Ke Yang, Bolin Chen, Bowen Li, Chengjun Mao, Tao Wang
  arXiv, 2026
  📄 paper

• T-Retriever: Tree-based Hierarchical Retrieval Augmented Generation for Textual Graphs
  Chunyu Wei, Huaiyu Qin, Siyuan He, Yunhai Wang, Yueguo Chen
  AAAI, 2026
  📄 paper | 🛠️ code

• A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
  Mingxuan Du, Benfeng Xu, Chiwei Zhu, Shaohan Wang, Pengyu Wang, Xiaorui Wang, Zhendong Mao
  arXiv, 2026
  📄 paper | 🛠️ code

• DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search
  Zhanli Li, Huiwen Tian, Lvzhou Luo, Yixuan Cao, Ping Luo
  arXiv, 2026
  📄 paper | 🛠️ code

• Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG (Corpus2Skill)
  Yiqun Sun, Pengfei Wei, Lawrence B. Hsieh
  arXiv, 2026
  📄 paper | 🛠️ code

• Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
  Haoliang Ming, Feifei Li, Xiaoqing Wu, Wenhui Que
  arXiv, 2026
  📄 paper

• More Context, Same Budget: Dual-Bounded Relational Recall Beyond Top-K Retrieval (DBRR)
  Thomson D. Nguy
  arXiv, 2026
  📄 paper

Direct Corpus Interaction

The agent bypasses pre-computed indexes entirely, interacting with the raw corpus via terminal-style tools (grep, file reads, shell commands).

• Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval
  Yulong Hui, Chao Chen, Zhihang Fu, Yihao Liu, Jieping Ye, Huanchen Zhang
  ICLR, 2026
  📄 paper | 🔗 openreview

• Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction
  Zhuofeng Li, Haoxiang Zhang, Cong Wei, Pan Lu, Ping Nie, Yi Lu, Yuyang Bai, Shangbin Feng, Hangxiao Zhu, Ming Zhong, Yuyu Zhang, Jianwen Xie, Yejin Choi, James Zou, Jiawei Han, Wenhu Chen, Jimmy Lin, Dongfu Jiang, Yu Zhang. arXiv, 2026
  📄 paper | 🛠️ code

• Rethinking Agentic Search with PI-SERINI: Is Lexical Retrieval Sufficient?
  Tz-Huan Hsu, Jheng-Hong Yang, Jimmy Lin
  arXiv, 2026
  📄 paper | 🛠️ code

• Rethinking Agentic RAG: Toward LLM-Driven Logical Retrieval Beyond Embeddings (LogicalRAG)
  Yuqi Zeng, Qixiang Deng, Yulei Wan, Ruiquan Jiang, Xiaoqing Zheng, Xuanjing Huang
  arXiv, 2026
  📄 paper

• GrepSeek: Training Search Agents for Direct Corpus Interaction
  Alireza Salemi, Chang Zeng, Atharva Nijasure, Jui-Hui Chung, Razieh Rahimi, Fernando Diaz, Hamed Zamani
  arXiv, 2026
  📄 paper

• Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses
  Pengcheng Jiang, Zhiyi Shi, Kelly Hong, Xueqiang Xu, Jiashuo Sun, Jimeng Sun, Hammad Bashir, Jiawei Han
  arXiv, 2026
  📄 paper | 🛠️ code

• Towards Retrieving Interaction Spaces for Agentic Search (RISE)
  Shengyao Zhuang, Yuansheng Ni, Hengxin Fun, Jimmy Lin, Xueguang Ma
  arXiv, 2026
  📄 paper | 🛠️ code

• DR-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion
  Yi Lu, Zhuofeng Li, Ping Nie, Haoxiang Zhang, Yuyu Zhang, Kai Zou, Wenhu Chen, Jimmy Lin, Dongfu Jiang, Yu Zhang
  arXiv, 2026
  📄 paper

• Boolean Queries Are All You Need? (Vole)
  Charles L. A. Clarke, Mark D. Smucker
  arXiv, 2026
  📄 paper | 🛠️ code (Vole) | 🛠️ code (Cottontail)

• A New Role for Relevance: Guiding Corpus Interaction in Agentic Search (RARG)
  Jiangnan Li, Yuqing Li, Mo Yu, Jinchao Zhang, Jie Zhou
  arXiv, 2026
  📄 paper | 🛠️ code

• Deep Agentic Search for Repository-Level Code Question Answering: An Empirical Study
  Amirkia Rafiei Oskooei, Bora Ilci, Alperen Kayim, Mehmet Egemen Uzun, Berat Can, Kaan Emre Kara, Ozan Orhan, Mehmet S. Aktas
  arXiv, 2026
  📄 paper

• Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
  Sagar Tamang, Ayush Vyas, Tabarakul Hazarika
  arXiv, 2026
  📄 paper

• Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Research Agents (SIEVE)
  Shuai Wang, Haodong Chen, Yu Yin, Shengyao Zhuang, Bevan Koopman, Guido Zuccon
  arXiv, 2026
  📄 paper

• Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents
  Qi Liu, Yiqun Chen, Zidan Chen, Yan Gao, Yi Wu, Yao Hu, Jiaxin Mao, Fengbin Zhu, Tat-Seng Chua
  arXiv, 2026
  📄 paper | 🛠️ code

• When Your Agent Opens the Chat App: Agent-Controlled Search over Raw Chat Logs Rivals Structured Memory (ReFind)
  Ruizhe Li, Licheng Zhang, Benfeng Xu, Mingxuan Du, Zheren Fu, Weidong Chen
  arXiv, 2026
  📄 paper

• LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents
  Xingjun Wang, Gongsheng Li, Qi Fan, Yunlin Mao, Luyan Su, Yingda Chen
  arXiv, 2026
  📄 paper

• CTIFoundry: An Agent-Native Corpus Scaffold for Cyber Threat Intelligence
  Yutong Cheng, Changze Li, Qian Cui, Wei Ding, Lingzhi Wang, Yan Chen, Peng Gao
  arXiv, 2026
  📄 paper

Retrieval Analysis & Evaluation

Studies characterizing agentic query workloads, comparing retrieval interfaces, and evaluating retrieval quality in deep research settings.

• Rerank Before You Reason: Analyzing Reranking Tradeoffs through Effective Token Cost in Deep Search Agents
  Sahel Sharifymoghaddam, Jimmy Lin
  arXiv, 2026
  📄 paper | 🛠️ code

• SAGE: Benchmarking and Improving Retrieval for Deep Research Agents
  Tiansheng Hu, Yilun Zhao, Canyu Zhang, Arman Cohan, Chen Zhao
  arXiv, 2026
  📄 paper | 🛠️ code

• A Picture of Agentic Search (ASQ)
  Francesca Pezzuti, Ophir Frieder, Fabrizio Silvestri, Sean MacAvaney, Nicola Tonellotto
  arXiv, 2026
  📄 paper | 🛠️ code

• Revisiting Text Ranking in Deep Research
  Chuan Meng, Litu Ou, Sean MacAvaney, Jeff Dalton
  SIGIR, 2026
  📄 paper | 🛠️ code

• Keyword Search is All You Need: Achieving RAG-Level Performance Without Vector Databases Using Agentic Tool Use
  Shreyas Subramanian, Adewale Akinfaderin, Yanyan Zhang, Ishan Singh, Mani Khanuja, Sandeep Singh, Maira Ladeira Tanke
  AAAI, 2026
  📄 paper

• Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agents
  Mahta Rafiee, Heydar Soudani, Zahra Abbasiantaeb, Mohammad Aliannejadi, Faegheh Hasibi, Hamed Zamani
  SIGIR, 2026
  📄 paper | 🛠️ code

• Reproducing Complex Set-Compositional Information Retrieval
  Vincent Degenhart, Dewi Timman, Arjen P. de Vries, Faegheh Hasibi, Mohanna Hoveyda
  SIGIR, 2026
  📄 paper | 🛠️ code

• Superintelligent Retrieval Agent: The Next Frontier of Information Retrieval
  Zeyu Yang, Qi Ma, Jason Chen, Anshumali Shrivastava
  arXiv, 2026
  📄 paper | 🛠️ code

• Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
  Sahil Sen, Akhil Kasturi, Elias Lumer, Anmol Gulati, Vamse Kumar Subbiah
  arXiv, 2026
  📄 paper

• Equal Accuracy, Unequal Evidence: Search APIs as Decision Surfaces for Tool-Using Agents
  Sriram Selvam, Anneswa Ghosh
  arXiv, 2026
  📄 paper

• Bridge Evidence: Static Retrieval Utility Does Not Predict Causal Utility in Multi-Step Agentic Search
  Debayan Mukhopadhyay, Utshab Kumar Ghosh, Shubham Chatterjee
  arXiv, 2026
  📄 paper

• Which RAG Paradigm Wins at Scale? A Scaling Study of Retrieval-Augmented Generation Paradigms
  Pengyu Wang, Benfeng Xu, Shaohan Wang, Xin Zeng, Huarui Wu, Lei Zhang, Licheng Zhang
  arXiv, 2026
  📄 paper

• Diagnosing Search Behavior and Failure Modes in Long-Horizon Search Agents
  Qi Liu, Jiaxin Mao, Fengbin Zhu, Tat-Seng Chua
  arXiv, 2026
  📄 paper

• The Devil Is in the Interface: Evaluating How Tool Architecture Shapes Coding Agent Behavior
  Xiangzhe Xu, Hamidreza Saghir, Qianhui Wu, Marc-Alexandre Côté, Tong Wang, Kiran Lakkaraju, Kexin Pei, Xiangyu Zhang
  COLM, 2026
  📄 paper | 🛠️ code

• Retrieved but not ranked: surface-form bias in structural retrieval, from mathematics to agent trajectories
  Nabira Rashid, Manolis Kellis
  arXiv, 2026
  📄 paper | 🛠️ code

The Agentic Search Loop

Core papers on the agent's search behavior: how it plans, queries, retrieves, and adapts across the full information-seeking loop. This section is provided for context — this list's focus is on the retrieval side of agentic search. For a more in-depth selection of papers on the agentic search loop itself, see Awesome-Search-Agent-Papers.

Planning & Reasoning

Foundational architectures that define the agent's core reasoning loop: interleaving thought, action, and retrieval.

• WebGPT: Browser-Assisted Question-Answering with Human Feedback
  Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman.
  arXiv, 2021
  📄 paper

• ReAct: Synergizing Reasoning and Acting in Language Models
  Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao
  ICLR, 2023
  📄 paper | 🛠️ code

• Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions (IRCoT)
  Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal
  ACL, 2023
  📄 paper | 🛠️ code

• Search-o1: Agentic Search-Enhanced Large Reasoning Models
  Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, Zhicheng Dou
  EMNLP, 2025
  📄 paper | 🛠️ code

• Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools
  Junde Wu, Jiayuan Zhu, Yuyuan Liu, Min Xu, Yueming Jin
  ACL, 2025
  📄 paper | 🛠️ code

• LLM-based Search Assistant with Holistically Guided MCTS for Intricate Information Seeking
  Ruiyang Ren, Yuhao Wang, Junyi Li, Jinhao Jiang, Wayne Xin Zhao, Wenjie Wang, Tat-Seng Chua
  SIGIR, 2025
  📄 paper

• WebThinker: Empowering Large Reasoning Models with Deep Research Capability
  Xiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian, Yutao Zhu, Yongkang Wu, Ji-Rong Wen, Zhicheng Dou
  NeurIPS, 2025
  📄 paper | 🛠️ code

• ArcticSwarm: Deferring Early Consensus in Long-Horizon Multi-Agent Research
  Soyoung Yoon, Boyi Liu, Yite Wang, Ruofan Wu, Canwen Xu, Nikki Lijing Kuang, Seung-won Hwang, Yuxiong He, Zhewei Yao
  arXiv, 2026
  📄 paper

Query Formulation & Decomposition

How the agent structures, decomposes, and parallelizes its search queries.

• Decomposed Prompting: A Modular Approach for Solving Complex Tasks (DecomP)
  Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal
  ICLR, 2023
  📄 paper | 🛠️ code

• Measuring and Narrowing the Compositionality Gap with Language Models (Self-Ask)
  Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, Mike Lewis
  EMNLP Findings, 2023
  📄 paper | 🛠️ code

• BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering
  Taolin Zhang, Dongyang Li, Qizhou Chen, Chengyu Wang, Xiaofeng He
  ACL, 2025
  📄 paper

• ParallelSearch: Train your LLMs to Decompose Query and Search Sub-queries in Parallel with Reinforcement Learning
  Shu Zhao, Tan Yu, Anbang Xu, Japinder Singh, Aaditya Shukla, Rama Akkiraju
  arXiv, 2025
  📄 paper

• SmartSearch: Process Reward-Guided Query Refinement for Search Agents
  Tongyu Wen, Guanting Dong, Zhicheng Dou
  SIGIR, 2026
  📄 paper | 🛠️ code

• Plan Before Search: Search Agents Need Plan
  Zhipeng Qian, Zihan Liang, Yufei Ma, Ben Chen, Huangyu Dai, Jiayi Ji, Chenyi Lei, Wenwu Ou, Xiaoshuai Sun, Qibin Hou
  arXiv, 2026
  📄 paper

• ReCite: Agentic Reasoning for Faithful Citation
  Yuyang Huang, Bobo Li, Jiajia Song, Yuzhe Ding, Chong Teng, Fei Li, Donghong Ji
  EMNLP Findings, 2026
  📄 paper

• Question's Gambit: The First Move Matters in Agentic Deep Search
  Radin Hamidi Rad, Amin Bigdeli, Negar Arabzadeh, Sajad Ebrahimi, Charles L. A. Clarke, Benjamin C. M. Fung, Ebrahim Bagheri
  arXiv, 2026
  📄 paper | 🛠️ code

Adaptive Retrieval Control

When to retrieve, how many times, and when to stop — including the decision between internal parametric knowledge and external retrieval.

• Active Retrieval Augmented Generation (FLARE)
  Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig
  EMNLP, 2023
  📄 paper | 🛠️ code

• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
  Akari Asai, Zeqiu Wu, Yizhong Wang, Avi Sil, Hannaneh Hajishirzi
  ICLR, 2024
  📄 paper | 🛠️ code

• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
  Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park
  NAACL, 2024
  📄 paper | 🛠️ code

• Chain-of-Retrieval Augmented Generation (CoRAG)
  Liang Wang, Haonan Chen, Nan Yang, Xiaolong Huang, Zhicheng Dou, Furu Wei
  NeurIPS, 2025
  📄 paper | 🛠️ code

• DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
  Xinyan Guan, Jiali Zeng, Fandong Meng, Chunlei Xin, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Jie Zhou
  ICLR, 2026
  📄 paper

• Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation (Amber)
  Qitao Qin, Yucong Luo, Yihang Lu, Zhibo Chu, Xiaoman Liu, Xianwei Meng
  ACL Findings, 2025
  📄 paper

• Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging
  Hongjin Qian, Zheng Liu
  NeurIPS, 2025
  📄 paper

• R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning
  Huatong Song, Jinhao Jiang, Wenqing Tian, Zhipeng Chen, Yuhuan Wu, Jiahao Zhao, Yingqian Min, Wayne Xin Zhao, Lei Fang, Ji-Rong Wen.
  arXiv, 2025
  📄 paper | 🛠️ code

• Pangu DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learning
  Wenxuan Shi, Haochen Tan, Chuqiao Kuang, Xiaoguang Li, Xiaozhe Ren, Chen Zhang, Hanting Chen, Yasheng Wang, Lu Hou, Lifeng Shang. arXiv, 2025
  📄 paper

• Stop-RAG: Value-Based Retrieval Control for Iterative RAG
  Jaewan Park, Solbee Cho, Jay-Yoon Lee
  NeurIPS Workshop, 2025
  📄 paper

• Deep Search with Hierarchical Meta-Cognitive Monitoring Inspired by Cognitive Neuroscience (DS-MCM)
  Zhongxiang Sun, Qipeng Wang, Weijie Yu, Jingxuan Yang, Haolang Lu, Jun Xu
  arXiv, 2026
  📄 paper

• AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
  Jingbo Sun, Wenyue Chong, Songjun Tu, Qichao Zhang, Yaocheng Zhang, Jiajun Chai, Xiaohan Wang, Wei Lin, Guojun Yin, Dongbin Zhao
  ACL Findings, 2026
  📄 paper | 🛠️ code

• GRASP: GRanularity-Aware Search Policy for Agentic RAG
  Varun Gandhi, Jaewook Lee, Shantanu Todmal, Andrew Lan, Franck Dernoncourt, Ryan Rossi, Zichao Wang
  arXiv, 2026
  📄 paper

• R²-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search
  Sheng Zhang, Junyi Li, Wenlin Zhang, Xiaowei Qian, Yingyi Zhang, Maolin Wang, Yichao Wang, Yong Liu, Xiangyu Zhao
  arXiv, 2026
  📄 paper

• TASR: Training-Free Adaptive Stopping for Iterative Retrieval
  Adrian Kieback, Uyiosa Philip Amadasun, Aman Chadha, Aaron Elkins
  arXiv, 2026
  📄 paper | 🛠️ code

• When Deep Research Agents Stagnate: Enhancing Reasoning with Retrieval-Aware Agent Control (RAAC)
  Heydar Soudani, Elizabeth Lingg, Faegheh Hasibi, Navid Rekabsaz
  arXiv, 2026
  📄 paper

Context & Memory Management

Managing information across long-horizon search trajectories without losing critical context.

• MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
  Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Jinhua Zhao, Bryan Kian Hsiang Low, Paul Pu Liang
  ICLR, 2026
  📄 paper | 🛠️ code

• WebResearcher: Unleashing Unbounded Reasoning Capability in Long-Horizon Agents
  Zile Qiao, Guoxin Chen, Xuanzhong Chen, Donglei Yu, Wenbiao Yin, Xinyu Wang, Zhen Zhang, Baixuan Li, Huifeng Yin, Kuan Li, Rui Min, Minpeng Liao, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou. arXiv, 2025
  📄 paper | 🛠️ code

• ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
  Xixi Wu, Kuan Li, Yida Zhao, Liwen Zhang, Litu Ou, Huifeng Yin, Zhongwang Zhang, Xinmiao Yu, Dingchu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Minhao Cheng, Shuai Wang, Hong Cheng, Jingren Zhou. arXiv, 2025
  📄 paper | 🛠️ code

• AgentFold: Long-Horizon Web Agents with Proactive Context Management
  Rui Ye, Zhongwang Zhang, Kuan Li, Huifeng Yin, Zhengwei Tao, Yida Zhao, Liangcai Su, Liwen Zhang, Zile Qiao, Xinyu Wang, Pengjun Xie, Fei Huang, Siheng Chen, Jingren Zhou, Yong Jiang. arXiv, 2025
  📄 paper

• MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search
  Sheng Zhang, Junyi Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Xiaowei Qian, Wenlin Zhang, Maolin Wang, Yong Liu, Xiangyu Zhao
  ACL, 2026
  📄 paper

• Self-Correcting Long-Horizon Search Agents via Tree-Structured Memory (ReTree)
  Aijun Yang, Qianxue Guo, Ziyi Huang, Yuxuan Chen, Shiyou Qian, Jian Cao
  arXiv, 2026
  📄 paper

• Mitigating Context Interference for Reliable and Efficient Search Agents (CRRL)
  Boyang Xue, Bin Wu, Shuofei Qiao, Sheng Wang, Rui Wang, Yiming Du, Hongru Wang, Jeff Z. Pan, Emine Yilmaz, Kam-Fai Wong, Aldo Lipani
  arXiv, 2026
  📄 paper | 🛠️ code

• PARSER: Read in Parallel, Reason in Depth for Long-Context LLM Agents
  Kun Li, Zexuan Qiu, Tianhua Zhang, Irwin King, Helen Meng
  arXiv, 2026
  📄 paper | 🌐 project

Training & Optimization

Methods for training agents to search effectively, through reinforcement learning or synthetic data generation. This section is provided for context — this list's focus is on the retrieval side of agentic search. For a more in-depth selection of RL-based agentic search training papers, see Awesome-RL-based-Agentic-Search-Papers.

RL-based Training

• PaSa: An LLM Agent for Comprehensive Academic Paper Search
  Yichen He, Guanhua Huang, Peiyuan Feng, Yuan Lin, Yuchen Zhang, Hang Li, Weinan E
  ACL, 2025
  📄 paper | 🛠️ code

• RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision
  Guangzhi Xiong, Qiao Jin, Xiao Wang, Yin Fang, Haolin Liu, Yifan Yang, Fangyuan Chen, Zhixing Song, Dengyu Wang, Minjia Zhang, Zhiyong Lu, Aidong Zhang
  arXiv, 2025
  📄 paper | 🛠️ code

• R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
  Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, Ji-Rong Wen
  arXiv, 2025
  📄 paper | 🛠️ code

• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
  Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Ö. Arik, Dong Wang, Hamed Zamani, Jiawei Han
  COLM, 2025
  📄 paper | 🛠️ code

• ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
  Mingyang Chen, Linzhuang Sun, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang, Jeff Z. Pan, Wen Zhang, Huajun Chen, Fan Yang, Zenan Zhou, Weipeng Chen
  arXiv, 2025
  📄 paper | 🛠️ code

• DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-World Environments
  Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, Pengfei Liu
  EMNLP, 2025
  📄 paper | 🛠️ code

• ZeroSearch: Incentivize the Search Capability of LLMs without Searching
  Hao Sun, Zile Qiao, Jiayan Guo, Xuanbo Fan, Yingyan Hou, Yong Jiang, Pengjun Xie, Yan Zhang, Fei Huang, Jingren Zhou
  arXiv, 2025
  📄 paper | 🛠️ code

• Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented Reasoning (AutoRefine)
  Yaorui Shi, Shihan Li, Chang Wu, Zhiyuan Liu, Junfeng Fang, Hengxing Cai, An Zhang, Xiang Wang
  NeurIPS, 2025
  📄 paper | 🛠️ code

• s3: You Don't Need That Much Data to Train a Search Agent via RL
  Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, Jiawei Han
  EMNLP, 2025
  📄 paper | 🛠️ code

• StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization
  Ziliang Wang, Xuhui Zheng, Kang An, Cijun Ouyang, Jialu Cai, Yuhang Wang, Yichao Wu
  EMNLP, 2025
  📄 paper

• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers (ExSearch)
  Zhengliang Shi, Lingyong Yan, Dawei Yin, Suzan Verberne, Maarten de Rijke, Zhaochun Ren
  NeurIPS, 2025
  📄 paper

• MaskSearch: A Universal Pre-Training Framework to Enhance Agentic Search Capability
  Weiqi Wu, Xin Guan, Shen Huang, Yong Jiang, Pengjun Xie, Fei Huang, Jiuxin Cao, Hai Zhao, Jingren Zhou
  arXiv, 2025
  📄 paper | 🛠️ code

• WebDancer: Towards Autonomous Information Seeking Agency
  Jialong Wu, Baixuan Li, Runnan Fang, Wenbiao Yin, Liwen Zhang, Zhengwei Tao, Dingchu Zhang, Zekun Xi, Gang Fu, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou
  arXiv, 2025
  📄 paper | 🛠️ code

• R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning
  Qingfei Zhao, Ruobing Wang, Dingling Xu, Daren Zha, Limin Liu
  arXiv, 2025
  📄 paper

• WebSailor: Navigating Super-Human Reasoning for Web Agent
  Kuan Li, Zhongwang Zhang, Huifeng Yin, Liwen Zhang, Litu Ou, Jialong Wu, Wenbiao Yin, Baixuan Li, Zhengwei Tao, Xinyu Wang, Weizhou Shen, Junkai Zhang, Dingchu Zhang, Xixi Wu, Yong Jiang, Ming Yan, Pengjun Xie, Fei Huang, Jingren Zhou. arXiv, 2025
  📄 paper | 🛠️ code

• Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL (ASearcher)
  Jiaxuan Gao, Wei Fu, Minyang Xie, Shusheng Xu, Chuyi He, Zhiyu Mei, Banghua Zhu, Yi Wu
  arXiv, 2025
  📄 paper | 🛠️ code

• ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards
  Shiyu Li, Yang Tang, Yifan Wang, Peiming Li, Xi Chen
  arXiv, 2025
  📄 paper

• HiPRAG: Hierarchical Process Rewards for Efficient Agentic RAG
  Peilin Wu, Mian Zhang, Kun Wan, Wentian Zhao, Kaiyu He, Xinya Du, Zhiyu Chen
  arXiv, 2025
  📄 paper

• InfoFlow: Reinforcing Search Agent via Reward Density Optimization
  Kun Luo, Hongjin Qian, Zheng Liu, Ziyi Xia, Shitao Xiao, Siqi Bao, Jun Zhao, Kang Liu
  arXiv, 2025
  📄 paper

• Optimizing Agentic Reasoning with Retrieval via Synthetic Semantic Information Gain Reward (InfoReasoner)
  Senkang Hu, Yong Dai, Yuzhi Zhao, Yihang Tao, Yu Guo, Zhengru Fang, Sam Tak Wu Kwong, Yuguang Fang
  ICML, 2026
  📄 paper | 🛠️ code

• KARL: Knowledge Agents via Reinforcement Learning
  Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal, Owen Oertell, Alexander Trott, Jacob Portes, Abhay Gupta, Pallavi Koppol, Ashutosh Baheti, Sean Kulinski, Ivan Zhou, Irene Dea, Krista Opsahl-Ong, Simon Favreau-Lessard, Sean Owen, Jose Javier Gonzalez Ortiz, Arnav Singhvi, Xabi Andrade, Cindy Wang, Kartik Sreenivasan, Sam Havens, Jialu Liu, Peyton DeNiro, Wen Sun, Michael Bendersky, Jonathan Frankle
  arXiv, 2026
  📄 paper

• SubSearch: Intermediate Rewards for Unsupervised Guided Reasoning in Complex Retrieval
  Roxana Petcu, Evangelos Kanoulas, Maarten de Rijke
  arXiv, 2026
  📄 paper | 🛠️ code

• BOUND: Brief-Guided Corrective Preference Distillation at Search-Control Boundaries
  Qingying Niu, Ruiyang Ren, Wayne Xin Zhao, Yaliang Li
  arXiv, 2026
  📄 paper | 🛠️ code

• LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation
  Zhixin Zhang, Xinke Jiang, Zhibang Yang, Weixuan Xu, Guohong Qiu, Xu Chu, Junfeng Zhao, Yasha Wang
  arXiv, 2026
  📄 paper

Data Synthesis

• HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions
  Zhiyu Shen, Jiyuan Liu, Yunhe Pang, Yanghui Rao, Fu Lee Wang, Jianxing Yu
  ACL, 2026
  📄 paper | 🛠️ code

• SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
  Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Wayne Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen. EMNLP Findings, 2025
  📄 paper | 🛠️ code

• WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
  Zhengwei Tao, Jialong Wu, Wenbiao Yin, Junkai Zhang, Baixuan Li, Haiyang Shen, Kuan Li, Liwen Zhang, Xinyu Wang, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou. arXiv, 2025
  📄 paper | 🛠️ code

Analysis & Ablations

Empirical studies analyzing reinforcement learning dynamics and reward design for agentic search training.

• Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
  Wenlin Zhang, Xiangyang Li, Kuicai Dong, Yichao Wang, Pengyue Jia, Xiaopeng Li, Yingyi Zhang, Derong Xu, Zhaocheng Du, Huifeng Guo, Ruiming Tang, Xiangyu Zhao
  arXiv, 2025
  📄 paper

• An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents
  Bowen Jin, Jinsung Yoon, Priyanka Kargupta, Sercan Ö. Arık, Jiawei Han
  arXiv, 2025
  📄 paper | 🛠️ code

• Retrieval, Reward, and Training Protocols: What Matters in Training Search Agents?
  Yibo Zhao, Zichen Ding, Jiayi Wu, Zun Wang, Xiang Li
  arXiv, 2026
  📄 paper

• HERALD: Counterfactual Audits and Minimal Repairs for Proof-of-Retrieval Rewards
  Zhuowen Liu, Bohan Cui, YinShang Guo, Yuting Wang, Hao Li
  arXiv, 2026
  📄 paper

Systems & Benchmarks

Systems & Frameworks

End-to-end systems, serving-efficiency work, and open multi-agent frameworks.

• Demystifying and Enhancing the Efficiency of LLM-Based Search Agents (SearchAgent-X)
  Tiannuo Yang, Zebin Yao, Bowen Jin, Lixiao Cui, Yusen Li, Gang Wang, Xiaoguang Liu
  ICLR, 2026
  📄 paper | 🛠️ code

• ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework
  Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, Wayne Xin Zhao
  EMNLP, 2025
  📄 paper | 🛠️ code

• Tongyi DeepResearch Technical Report
  Tongyi DeepResearch Team
  arXiv, 2025
  📄 paper | 🛠️ code

Benchmarks

• HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
  Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, Christopher D. Manning
  EMNLP, 2018
  📄 paper | 🛠️ code

• MuSiQue: Multihop Questions via Single-hop Question Composition
  Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal
  TACL, 2022
  📄 paper | 🛠️ code

• GAIA: A Benchmark for General AI Assistants
  Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, Thomas Scialom
  ICLR, 2024
  📄 paper

• Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for Deep Research
  Corby Rosset, Ho-Lam Chung, Guanghui Qin, Ethan C. Chau, Zhuo Feng, Ahmed Awadallah, Jennifer Neville, Nikhil Rao
  SIGIR, 2025
  📄 paper

• BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
  Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S. Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan Ö. Arik, Danqi Chen, Tao Yu
  ICLR, 2025
  📄 paper | 🛠️ code

• AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?
  Ori Yoran, Samuel Joseph Amouyal, Chaitanya Malaviya, Ben Bogin, Ofir Press, Jonathan Berant
  EMNLP, 2024
  📄 paper | 🛠️ code

• FRAMES: Fact, Fetch, and Reason — A Unified Evaluation of Retrieval-Augmented Generation
  Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, Manaal Faruqui
  NAACL, 2025
  📄 paper

• WebWalker: Benchmarking LLMs in Web Traversal
  Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang
  ACL, 2025
  📄 paper

• Humanity's Last Exam (HLE)
  Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, et al.
  arXiv, 2025
  📄 paper

• BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents
  Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, Amelia Glaese
  arXiv, 2025
  📄 paper

• BrowseComp-ZH: Benchmarking Web Browsing Ability of LLMs in Chinese
  Peilin Zhou, Bruce Leon, Xiang Ying, Can Zhang, Yifan Shao, Qichen Ye, Dading Chong, Zhiling Jin, Chenxuan Xie, Meng Cao, Yuxin Gu, Sixin Hong, Jing Ren, Jian Chen, Chao Liu, Yining Hua. arXiv, 2025
  📄 paper

• InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
  Yunjia Xi, Jianghao Lin, Menghui Zhu, Yongzhao Xiao, Zhuoying Ou, Jiaqi Liu, Tong Wan, Bo Chen, Weiwen Liu, Yasheng Wang, Ruiming Tang, Weinan Zhang, Yong Yu
  arXiv, 2025
  📄 paper | 🛠️ code

• DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
  Mingxuan Du, Benfeng Xu, Chiwei Zhu, Xiaorui Wang, Zhendong Mao
  arXiv, 2025
  📄 paper | 🛠️ code

• Dr. Bench: A Multidimensional Evaluation for Deep Research Agents, from Answers to Reports Yang Yao, Yixu Wang, Yuxuan Zhang, Yi Lu, Tianle Gu, Lingyu Li, Dingyi Zhao, Keming Wu, Haozhe Wang, Ping Nie, Yan Teng, Yingchun Wang arXiv, 2025 📄 paper | 🛠️ code

• Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge
  Boyu Gou, Zanming Huang, Yuting Ning, Yu Gu, Michael Lin, Weijian Qi, Andrei Kopanev, Botao Yu, Bernal Jimenez Gutierrez, Yiheng Shu, Chan Hee Song, Jiaman Wu, Shijie Chen, Hanane Moussa, Tianshu Zhang, Jian Xie, Yifei Li, Tianci Xue, Zeyi Liao, Kai Zhang, Boyuan Zheng, Zhaowei Cai, Viktor Rozgic, Morteza Ziyadi, Huan Sun, Yu Su
  NeurIPS, 2025
  📄 paper | 🛠️ code

• BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agents
  Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Sahel Sharifymoghaddam, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin. arXiv, 2025
  📄 paper

• Beyond Monolingual Deep Research: Evaluating Agents and Retrievers with Cross-Lingual BrowseComp-Plus (XBCP)
  Yuheng Lu, Qingcheng Zeng, Heli Qi, Puxuan Yu, Fuheng Zhao, Rui Yang, Hitomi Yanaka, Naoto Yokoya, Weihao Xuan
  arXiv, 2026
  📄 paper

• Projecting BrowseComp-Plus onto ClimbMix: Toward More Realistic Corpora for Agentic Search
  Sahel Sharifymoghaddam, Lingwei Gu, Yijun Ge, Jimmy Lin
  arXiv, 2026
  📄 paper | 🛠️ code

• WideSearch: Benchmarking Agentic Broad Info-Seeking
  Ryan Wong, Jiawei Wang, Junjie Zhao, Li Chen, Yan Gao, Long Zhang, Xuan Zhou, Zuo Wang, Kai Xiang, Ge Zhang, Wenhao Huang, Yang Wang, Ke Wang
  ICLR, 2026
  📄 paper | 🛠️ code

• InteractComp: Evaluating Search Agents With Ambiguous Queries
  Mingyi Deng, Lijun Huang, Yani Fan, Jiayi Zhang, Fashen Ren, Jinyi Bai, Fuzhen Yang, Dayi Miao, Zhaoyang Yu, Yifan Wu, Yanfei Zhang, Fengwei Teng, Yingjia Wan, Song Hu, Yude Li, Xin Jin, Conghao Hu, Haoyu Li, Qirui Fu, Tai Zhong, Xinyu Wang, Xiangru Tang, Nan Tang, Chenglin Wu, Yuyu Luo. arXiv, 2025
  📄 paper | 🛠️ code

• One Interaction Is Worth a Thousand Guesses: Benchmarking the Interactive Capabilities of Deep Research Agents (IDRBench)
  Yingchaojie Feng, Qiang Huang, Xiaoya Xie, Zhaorui Yang, Jun Yu, Wei Chen, Anthony K. H. Tung
  arXiv, 2026
  📄 paper

• DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent
  Tongzhou Wu, Yuhao Wang, Xinyu Ma, Xiuqiang He, Shuaiqiang Wang, Dawei Yin, Xiangyu Zhao
  SIGIR, 2026
  📄 paper | 🛠️ code

• PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR
  James Burgess, Jan N. Hansen, Duo Peng, Yuhui Zhang, Alejandro Lozano, Min Woo Sun, Emma Lundberg, Serena Yeung-Levy
  arXiv, 2026
  📄 paper | 🛠️ code

• DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation
  Sixiong Xie, Zhuofan Shi, Haiyang Shen, Jiuzheng Wang, Siqi Zhong, Mugeng Liu, Chongyang Pan, Peilun Jia, Baoqing Sun, Xiang Jing, Yun Ma
  arXiv, 2026
  📄 paper

• ClawBench: Can AI Agents Complete Everyday Online Tasks?
  Yuxuan Zhang, Yubo Wang, Yipeng Zhu, Penghui Du, Junwen Miao, Xuan Lu, Zhuofeng Li, Xingwei Qu, Dongfu Jiang, Ping Nie, Jiaheng Liu, Wenhu Chen, Kelsey R. Allen, et al.
  arXiv, 2026
  📄 paper | 🛠️ code | 🌐 project

• VisDocAgentBench: Benchmarking Agents for Visually Rich Document Retrieval
  Lexiang Hu, Yanzhao Zhang, Mingxin Li, Dingkun Long, Yikang Li, Fuwei Zhang, Yisen Wang, Zhouchen Lin
  arXiv, 2026
  📄 paper | 🌐 project

• Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems
  Maximilian Schall, Sedigheh Eslami, Markus Krimmel, Antoine Chaffin, Louis Milliken, Bo Wang, Denis Bykov
  arXiv, 2026
  📄 paper | 🌐 leaderboard

• Benchmarking Hybrid Deep Research Across Database Querying and Web Search (HybridDeepResearch)
  Ruofan Wu, Peiran Xu, Xiaolong Li, Fan Shu, Soyoung Yoon, Yite Wang, Xiaodong Yu, Boyi Liu, Feng Yan, Debiao Li, Yuxiong He, Zhewei Yao
  arXiv, 2026
  📄 paper

Resources

Surveys & Position Papers

• Towards AI Search Paradigm
  Yuchen Li, Hengyi Cai, Rui Kong, Xinran Chen, Jiamin Chen, Jun Yang, Haojie Zhang, Jiayi Li, Jiayi Wu, Yiqun Chen, Changle Qu, Wenwen Ye, Lixin Su, Xinyu Ma, Lingyong Yan, Long Xia, Daiting Shi, Junfeng Wang, Xiangyu Zhao, Jiashu Zhao, Haoyi Xiong, Shuaiqiang Wang, Dawei Yin
  arXiv, 2025
  📄 paper

• Deep Research Agents: A Systematic Examination And Roadmap
  Yuxuan Huang, Yihang Chen, Haozheng Zhang, Kang Li, Huichi Zhou, Meng Fang, Linyi Yang, Xiaoguang Li, Lifeng Shang, Songcen Xu, Jianye Hao, Kun Shao, Jun Wang. arXiv, 2025
  📄 paper | 🛠️ repo

• From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
  Weizhi Zhang, Yangning Li, Yuanchen Bei, Junyu Luo, Guancheng Wan, Liangwei Yang, Chenxuan Xie, Yuyao Yang, Wei-Chieh Huang, Chunyu Miao, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Yankai Chen, Chunkit Chan, Peilin Zhou, Xinyang Zhang, Chenwei Zhang, Jingbo Shang, Ming Zhang, Yangqiu Song, Irwin King, Philip S. Yu. arXiv, 2025
  📄 paper | 🛠️ repo

• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
  Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu. EMNLP Findings, 2025
  📄 paper | 🛠️ repo

• A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
  Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang. arXiv, 2025
  📄 paper | 🛠️ repo

• The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
  Guibin Zhang, Hejia Geng, Xiaohang Yu, Zhenfei Yin, Zaibin Zhang, Zelin Tan, Heng Zhou, Zhongzhi Li, Xiangyuan Xue, Yijiang Li, Yifan Zhou, Yang Chen, Chen Zhang, Yutao Fan, Zihu Wang, Songtao Huang, Francisco Piedrahita-Velez, Yue Liao, Hongru Wang, Mengyue Yang, Heng Ji, Jun Wang, Shuicheng Yan, Philip Torr, Lei Bai. arXiv, 2025
  📄 paper | 🛠️ repo

• Reinforcement Learning Foundations for Deep Research Systems: A Survey
  Wenjun Li, Zhi Chen, Jingru Lin, Hannan Cao, Wei Han, Sheng Liang, Zhi Zhang, Kuicai Dong, Dexun Li, Chen Zhang, Yong Liu. arXiv, 2025
  📄 paper

• A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications Minhua Lin, Zongyu Wu, Zhichao Xu, Hui Liu, Xianfeng Tang, Qi He, Charu Aggarwal, Hui Liu, Xiang Zhang, Suhang Wang. arXiv, 2025
  📄 paper | 🛠️ repo

List Maintainers

Francesco Benocci (ISTI-CNR, Pisa, Italy)