The Year in AI Papers
The year/Topics/Search and deep research

Research collection

Search and deep research

Information-seeking agents: deep research systems, open-web search agents, retrieval-augmented agents, and their training data and benchmarks.

Papers
59
Research labs
1
Official code
45

1–50 of 59 papers in this collection

01

Independent research

AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis

AskChem is a claim-centered infrastructure for cross-paper chemistry search, shifting the retrieval unit from documents to provenance-carrying claims. Each claim is an atomic, typed assertion extracted from a paper, grounded by a source DOI and a verbatim quote or evidence locator. The system indexes 2.4M claims from 147K papers (1925–2026) and exposes…

Bing Yan, Gregory Wolfe, Stefano Martiniani, Kyunghyun Cho
Published
Jul 2026
Citations
0
Code
Not linked
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02

Independent research

From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search

The paper introduces Multi-Agent Protocol Distillation (MAPD), a framework for distilling knowledge from proprietary LLMs to open-source student models in agentic search. It addresses two bottlenecks: inaccessible logits and tokenizer mismatches that prevent logit-matching, and style drift from imitating raw natural-language trajectories. MAPD uses a…

Junlin Liu, Jiangwang Chen, Zixin Song, Shuaiyu Zhou, et al.
Published
Jul 2026
Citations
3
Code
Not linked
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03

Independent research

A New Role for Relevance: Guiding Corpus Interaction in Agentic Search

The paper introduces the Relevance-Aware RipGrep Search Agent (RARG), which uses relevance as an execution prior to guide corpus interaction in agentic search. RARG ranks documents with an embedding retriever and makes ripgrep traverse them in that order, exposing matches from more relevant documents earlier. It has two extensions: RARG+ seeds the agent…

Jiangnan Li, Yuqing Li, Mo Yu, Jinchao Zhang, et al.
Published
Jul 2026
Citations
0
Code
51 stars
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04

Independent research

AREX: Towards a Recursively Self-Improving Agent for Deep Research

AREX is a family of recursively self-improving deep research agents developed by BAAI. It exploits the discovery-verification asymmetry in deep research by alternating between an inner research loop that gathers evidence and constructs provisional answers, and an outer self-improvement loop that audits answers constraint-wise, identifies unresolved claims,…

Shuqi Lu, Chaofan Li, Kun Luo, Zhang Zhang, et al.
Published
Jul 2026
Citations
0
Code
Not linked
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05

Independent research

SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration

SearchOS is a multi-agent framework for robust open-domain information seeking. It formulates information-seeking tasks as relational schema completion with grounded citations, where agents discover entities, populate attributes across linked tables, and anchor each value to source evidence. To manage long-horizon search, it introduces Search-Oriented…

Yuyao Zhang, Junjie Gao, Zhengxian Wu, Jiaming Fan, et al.
Published
Jul 2026
Citations
0
Code
503 stars
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06

Independent research

RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM

RAGU is an open-source, modular GraphRAG engine that addresses three obstacles in existing systems: single-pass extraction, dependence on expensive LLMs, and engineering immaturity. It separates extraction from consolidation via a six-stage pipeline: chunking, two-stage typed extraction (entities then relations) under the NEREL schema, DBSCAN-backed…

Mikhail Komarov, Ivan Bondarenko, Stanislav Shtuka, Oleg Sedukhin, et al.
Published
Jul 2026
Citations
0
Code
118 stars
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07

Independent research

DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment

The paper introduces DeepSearch-World, a deterministic and verifiable offline environment for deep search agents, built on a Wikipedia corpus with 420K multi-hop QA tasks. It also presents DeepSearch-Evolve, a self-distillation framework that iteratively generates, filters, and converts scaffolded trajectories into ReAct-format supervision for student…

Xinyu Geng, Xuanhua He, Sixiang Chen, Yanjing Xiao, et al.
Published
Jul 2026
Citations
1
Code
8 stars
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08

Independent research

Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation

Visual generators fail on requests requiring world knowledge beyond their training data, such as new characters or recent events. The authors introduce SEARCHGEN-20K, a dataset of 20,939 prompts with twelve failure categories, and SEARCHGEN-BENCH, where open generators score only 21–28 out of 100, a 40-point drop from standard benchmarks. Naive search…

Haozhe Wang, Weijia Feng, Jinpeng Yu, Che Liu, et al.
Published
Jul 2026
Citations
2
Code
115 stars
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09

Independent research

FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents

The paper introduces FORT-Searcher, a deep search agent trained using FORT, a framework for synthesizing shortcut-resistant training data. The authors argue that existing methods for creating deep search tasks often fail to induce real search difficulty because agents can exploit shortcuts, such as evidence co-coverage, single-clue selectivity, exposed…

Jia Deng, Yimeng Chen, Xiaoqing Xiang, Ziyang Zeng, et al.
Published
Jun 2026
Citations
1
Code
27 stars
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10

Independent research

SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research

SearchSwarm introduces a method for training delegation intelligence in LLM agents for long-horizon deep research tasks. The approach uses a main-distributes, sub-executes paradigm where a main agent decomposes tasks and dispatches subtasks to subagents via a call_sub_agent tool, receiving only condensed, citation-grounded reports. The harness design…

Pu Ning, Quan Chen, Kun Tao, Xinyu Tang, et al.
Published
Jun 2026
Citations
3
Code
85 stars
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11

Independent research

K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts

K-BROWSECOMP is a new benchmark for evaluating web-browsing agents in Korean contexts, consisting of 400 problems. The 300-problem K-BROWSECOMP-VERIFIED subset was manually constructed and validated by native Korean speakers. On this subset, frontier LLMs like GPT-5.5, DeepSeek-V4-Pro, and GLM-5.1 achieve only 30.00–45.67% accuracy, a significant drop from…

Nahyun Lee, Dongkeun Yoon, Guijin Son, Geewook Kim, et al.
Published
Jun 2026
Citations
1
Code
14 stars
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12

Independent research

Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses

Harness-1 is a 20B search agent trained with reinforcement learning inside a stateful search harness. The harness maintains environment-side working memory (candidate pool, importance-tagged curated set, evidence graph, verification records, compressed/deduplicated observations, budget-aware rendering), while the policy retains semantic decisions: what to…

Pengcheng Jiang, Zhiyi Shi, Kelly Hong, Xueqiang Xu, et al.
Published
Jun 2026
Citations
2
Code
941 stars
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13

Independent research

Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories

This paper introduces TELBENCH, a benchmark for span-level error localization in deep-research agent trajectories, and DRIFT, a claim-centric auditing framework. The authors collected 2,790 real trajectories from two agent frameworks (MiroFlow, OAgent), three backbone models (GPT-5, Gemini-2.5-Pro, Claude-Sonnet-4.5), and three benchmarks (GAIA, XBench,…

Jiaming Wang, Ziteng Feng, Jiangtao Wu, Ruihao Li, et al.
Published
Jun 2026
Citations
0
Code
22 stars
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14

Independent research

Masking Stale Observations Helps Search Agents -- Until It Doesn't: A Regime Map and Its Mechanism

This paper investigates when masking stale observations in long-horizon search agents helps or hurts performance. The authors systematically vary backbone models (4B to 284B parameters) and retrievers (BM25, Qwen3-Emb-8B, AgentIR-4B) on offline (BrowseComp-Plus) and live-web (GAIA, xBench-DeepSearch, BrowseComp-ZH) benchmarks. They find that the accuracy…

Haoxiang Zhang, Qixin Xu, Zhuofeng Li, Lei Zhang, et al.
Published
May 2026
Citations
3
Code
22 stars
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15

arXiv.org

GrepSeek: Training Search Agents for Direct Corpus Interaction

GrepSeek is a Direct Corpus Interaction (DCI) search agent that bypasses traditional retrieval indexes by having a compact LLM issue Unix shell commands (e.g., rg, grep) directly over a raw text corpus. It uses a two-stage training pipeline: first, a cold-start dataset is generated via an answer-aware Tutor (backward chaining with answer-leak masking) and…

Alireza Salemi, Chang Zeng, Atharva Nijasure, Jui-Hui Chung, et al.
Published
May 2026
Citations
5
Code
52 stars
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16

arXiv.org

OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources

OmniRetrieval is a framework for unified retrieval across heterogeneous knowledge sources (unstructured text, relational databases, RDF knowledge graphs, and labeled property graphs). Instead of collapsing sources into a shared representation, it takes a natural-language query, selects relevant sources via a long-context LLM reading all source descriptors,…

Jinheon Baek, Soyeong Jeong, Sangwoo Park, Woongyeong Yeo, et al.
Published
May 2026
Citations
0
Code
33 stars
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17

arXiv.org

SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research

SciAtlas is a large-scale, multi-disciplinary knowledge graph designed to support automated scientific research by organizing over 43 million papers from 26 disciplines into a structured topological network. It contains 157 million entities (including papers, authors, institutions, keywords, topics, and fields) and 3 billion triplets, with 9 entity types…

Shuofei Qiao, Yunxiang Wei, Jiazheng Fan, Bin Wu, et al.
Published
May 2026
Citations
0
Code
137 stars
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18

arXiv.org

HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents

HyperEyes is a parallel multimodal search agent that addresses inefficiencies in sequential search agents for multi-entity queries. It introduces a Unified Grounded Search (UGS) action space, fusing visual grounding and retrieval into a single atomic action to enable concurrent search. Training uses a two-stage approach: a Parallel-Amenable Data Synthesis…

Guankai Li, Jiabin Chen, Yi Xu, Xichen Zhang, et al.
Published
May 2026
Citations
4
Code
71 stars
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19

arXiv.org

MiA-Signature: Approximating Global Activation for Long-Context Understanding

The paper introduces Mindscape Activation Signature (MiA-Signature), a compact representation of the global activation pattern induced by a query over a semantic memory space, inspired by cognitive science theories of global ignition and partial access. It is constructed via submodular selection of high-level concepts (session summaries) that cover the…

Yuqing Li, Jiangnan Li, Mo Yu, Zheng Lin, et al.
Published
May 2026
Citations
1
Code
Not linked
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20

arXiv.org

OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents

OpenSearch-VL is a fully open-source recipe for training multimodal deep search agents using agentic reinforcement learning. It addresses the lack of open high-quality training data, transparent trajectory synthesis, and detailed training recipes. The recipe includes a data curation pipeline using Wikipedia path sampling, fuzzy entity rewriting, and…

Shuang Chen, Kaituo Feng, Hangting Chen, Wenxuan Huang, et al.
Published
May 2026
Citations
10
Code
262 stars
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21

arXiv.org

OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories

OpenSeeker-v2 is a search agent developed by an academic team at Shanghai Jiao Tong University, demonstrating that a simple supervised fine-tuning (SFT) approach can rival resource-intensive industrial pipelines when trained on high-quality, high-difficulty trajectories. The authors introduce three data synthesis modifications: scaling the knowledge graph…

Yuwen Du, Rui Ye, Shuo Tang, Keduan Huang, et al.
Published
May 2026
Citations
6
Code
766 stars
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22

arXiv.org

Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction

The paper introduces Direct Corpus Interaction (DCI), a retrieval paradigm where agents search raw corpora using terminal tools (grep, bash, file reads) instead of conventional retrievers. DCI bypasses embedding models and vector indexes, enabling fine-grained, compositional search. Evaluated on BrowseComp-Plus, multi-hop QA, and IR benchmarks,…

Zhuofeng Li, Haoxiang Zhang, Cong Wei, Pan Lu, et al.
Published
May 2026
Citations
21
Code
382 stars
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23

arXiv.org

Memory Intelligence Agent

The paper introduces the Memory Intelligence Agent (MIA), a framework for deep research agents that combines a Manager-Planner-Executor architecture with dual memory systems. The Memory Manager stores compressed historical trajectories as non-parametric memory, while the Planner, trained via alternating reinforcement learning, serves as parametric memory.…

Jingyang Qiao, Weicheng Meng, Yu Cheng, Zhihang Lin, et al.
Published
Apr 2026
Citations
4
Code
792 stars
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24

arXiv.org

Gen-Searcher: Reinforcing Agentic Search for Image Generation

Gen-Searcher is the first trained multimodal deep search agent for image generation, addressing the limitation of frozen internal knowledge in text-to-image models. It performs multi-hop web search and reasoning to collect textual knowledge and reference images for grounded generation. The authors built a data pipeline producing two training datasets…

Kaituo Feng, Manyuan Zhang, Shuang Chen, Yunlong Lin, et al.
Published
Mar 2026
Citations
16
Code
381 stars
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25

Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval

Learning to Retrieve from Agent Trajectories

This paper introduces LRAT (Learning to Retrieve from Agent Trajectories), a framework for training retrieval models directly from the interaction data of LLM-powered search agents. The authors argue that traditional human-centric retrieval training is misaligned with agentic search, where agents issue intermediate queries and consume results in multi-turn…

Yuqi Zhou, Sunhao Dai, Changle Qu, Liang Pang, et al.
Published
Mar 2026
Citations
2
Code
56 stars
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26

arXiv.org

MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome

MiroEval is a benchmark and evaluation framework for deep research systems, comprising 100 tasks (70 text-only, 30 multimodal) grounded in real user needs and built via a dual-path pipeline supporting periodic updates. The evaluation suite assesses systems along three complementary dimensions: adaptive synthesis quality with task-specific rubrics, agentic…

Fangda Ye, Yuxin Hu, Pengxiang Zhu, Yibo Li, et al.
Published
Mar 2026
Citations
4
Code
46 stars
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27

arXiv.org

OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis

OpenResearcher is a fully open pipeline for synthesizing long-horizon deep research trajectories. It decouples one-time corpus bootstrapping from multi-turn trajectory synthesis, running the search-and-browse loop entirely offline over a 15M-document corpus using three browser primitives: search, open, and find. Using GPT-OSS-120B as the teacher, it…

Zhuofeng Li, Dongfu Jiang, Xueguang Ma, Haoxiang Zhang, et al.
Published
Mar 2026
Citations
20
Code
1.1K stars
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28

arXiv.org

MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification

MiroMind Team introduces MiroThinker-1.7, a research agent for complex long-horizon reasoning, and MiroThinker-H1, which adds verification-centric reasoning. MiroThinker-1.7 improves step-level reliability through agentic mid-training that emphasizes planning, reasoning, and tool interaction. MiroThinker-H1 integrates local verification (evaluating…

MiroMind Team, S. Bai, L. Bing, L. Lei, et al.
Published
Mar 2026
Citations
20
Code
Not linked
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29

arXiv.org

OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data

OpenSeeker is the first fully open-source search agent (model and data) achieving frontier-level performance, developed by an academic team at Shanghai Jiao Tong University. It uses two core innovations: fact-grounded scalable controllable QA synthesis, which reverse-engineers the web graph via topological expansion and entity obfuscation to generate…

Yuwen Du, Rui Ye, Shuo Tang, Xinyu Zhu, et al.
Published
Mar 2026
Citations
21
Code
766 stars
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30

arXiv.org

DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval

The paper introduces DARE (Distribution-Aware Retrieval Embedding), a lightweight retrieval model that incorporates data distribution information into function representations for retrieving R packages, addressing the underuse of rigorous statistical methods in R by LLM agents. The authors construct RPKB, a curated knowledge base from 8,191 CRAN packages,…

Maojun Sun, Yue Wu, Yifei Xie, Ruijian Han, et al.
Published
Mar 2026
Citations
3
Code
15 stars
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31

arXiv.org

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

The paper introduces MOOSE-Star, a framework for tractable training of LLMs for scientific discovery by directly modeling P(hypothesis|background). The authors argue that end-to-end training is intractable due to combinatorial complexity O(N^k) from retrieving k inspirations from a large knowledge base. MOOSE-Star decomposes this into sequential subtasks:…

Zonglin Yang, Lidong Bing
Published
Mar 2026
Citations
3
Code
90 stars
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32

arXiv.org

DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories

DeepImageSearch introduces a new paradigm for image retrieval, reformulating it as an agentic exploration task over visual histories. The authors construct DISBench, a benchmark with 122 queries across 57 users and 109,467 photos, built via a human-model collaborative pipeline that mines latent spatiotemporal associations. Queries are categorized into…

Chenlong Deng, Mengjie Deng, Junjie Wu, Dun Zeng, et al.
Published
Feb 2026
Citations
3
Code
87 stars
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33

arXiv.org

WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning

WIDESEEK-R1 explores width scaling for broad information seeking using a lead-agent–subagent framework trained via multi-agent reinforcement learning (MARL). The system uses a shared LLM with isolated contexts and specialized tools, where the lead agent decomposes tasks and delegates subtasks to parallel subagents. Training on a curated 20k dataset of…

Zelai Xu, Zhexuan Xu, Ruize Zhang, Chunyang Zhu, et al.
Published
Feb 2026
Citations
6
Code
4.5K stars
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34

arXiv.org

Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

The paper introduces VDR-Bench, a benchmark of 2,000 VQA instances designed to evaluate Vision-DeepResearch systems under realistic conditions. It identifies two flaws in existing benchmarks: they are not visual-search-centric (answers can be inferred from text cues or model priors) and rely on idealized retrieval (near-exact whole-image matching).…

Yu Zeng, Wenxuan Huang, Zhen Fang, Shuang Chen, et al.
Published
Feb 2026
Citations
21
Code
Not linked
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35

arXiv.org

Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

Vision-DeepResearch introduces a new paradigm for multimodal deep research, addressing the hit-rate problem in image search and the limited reasoning depth and search breadth of existing methods. The approach enables multi-turn, multi-entity, and multi-scale visual and textual search, supporting dozens of reasoning steps and hundreds of engine…

Wenxuan Huang, Yu Zeng, Qiuchen Wang, Zhen Fang, et al.
Published
Jan 2026
Citations
25
Code
669 stars
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36

arXiv.org

DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation

DeepResearchEval is an automated framework for constructing deep research tasks and evaluating deep research systems. It addresses limitations in existing benchmarks: annotation-intensive task construction, static evaluation dimensions, and incomplete fact verification. The framework uses a persona-driven pipeline to generate realistic, complex tasks…

Yibo Wang, Lei Wang, Yue Deng, Keming Wu, et al.
Published
Jan 2026
Citations
12
Code
142 stars
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37

Qwen

Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

The report introduces Qwen3-VL-Embedding and Qwen3-VL-Reranker, a unified framework for multimodal retrieval built on the Qwen3-VL foundation model. The embedding model uses a multi-stage training pipeline (contrastive pre-training, multi-task contrastive learning, and reranker distillation) to produce high-dimensional vectors, supporting Matryoshka…

Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, et al.
Published
Jan 2026
Citations
167
Code
1.3K stars
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38

Independent research

HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling

HGMEM is a hypergraph-based working memory system for multi-step retrieval-augmented generation (RAG) that improves complex relational modeling in long contexts. Unlike existing memory mechanisms that passively store isolated facts, HGMEM represents memory as a hypergraph where hyperedges serve as memory points, enabling the progressive formation of…

Chulun Zhou, Chunkang Zhang, Guoxin Yu, Fandong Meng, et al.
Published
Dec 2025
Citations
4
Code
131 stars
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39

arXiv.org

Step-DeepResearch Technical Report

Step-DeepResearch is a 32B-parameter, end-to-end Deep Research agent model developed by StepFun. It uses a data synthesis strategy based on atomic capabilities (planning, information seeking, reflection, and report writing) and a progressive training pipeline (agentic mid-training, SFT, RL) with a Checklist-style Judger reward. The model achieves a score…

Chen Hu, Haikuo Du, Heng Wang, Lin Lin, et al.
Published
Dec 2025
Citations
11
Code
569 stars
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40

arXiv.org

Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

The paper introduces Mindscape-Aware RAG (MiA-RAG), a framework that equips LLM-based RAG systems with a global semantic representation, called a mindscape, to improve long-context understanding. The mindscape is built via hierarchical summarization of a document. MiA-RAG conditions both retrieval and generation on this mindscape: a retriever (MiA-Emb)…

Yuqing Li, Jiangnan Li, Zheng Lin, Ziyan Zhou, et al.
Published
Dec 2025
Citations
6
Code
Not linked
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41

arXiv.org

How Far Are We from Genuinely Useful Deep Research Agents?

This paper introduces FINDER, a benchmark for evaluating Deep Research Agents (DRAs) on report generation, and DEFT, a failure taxonomy for diagnosing DRA errors. FINDER consists of 100 human-curated research tasks with 419 structured checklist items, refining the DeepResearch Bench by adding detailed prompts and checklists. DEFT, built via grounded theory…

Dingling Zhang, He Zhu, Jincheng Ren, Kangqi Song, et al.
Published
Dec 2025
Citations
9
Code
66 stars
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42

arXiv.org

DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research

The paper introduces DR Tulu-8B, the first fully open model trained end-to-end for long-form deep research tasks, using a new method called Reinforcement Learning with Evolving Rubrics (RLER). RLER constructs and maintains rubrics that co-evolve with the policy model during training, incorporating newly explored information from search and contrasting…

Rulin Shao, Akari Asai, Shannon Zejiang Shen, Hamish Ivison, et al.
Published
Nov 2025
Citations
75
Code
692 stars
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43

arXiv.org

Deep Research: A Systematic Survey

This survey provides a comprehensive overview of Deep Research (DR) systems, which combine large language models with external tools to perform complex, open-ended research tasks. The authors formalize a three-stage roadmap for DR: Agentic Search, Integrated Research, and Full-stack AI Scientist. They identify four key components of DR systems: query…

Zhengliang Shi, Yiqun Chen, Haitao Li, Weiwei Sun, et al.
Published
Nov 2025
Citations
15
Code
322 stars
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44

arXiv.org

GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization

The paper introduces GeoVista, an agentic multimodal model for geolocalization that integrates image zoom-in and web-search tools within a dynamic reasoning loop. The authors curate GeoBench, a benchmark of 1,142 high-resolution images (photos, panoramas, satellite) from 66 countries, with multi-level labels (country, province, city) and haversine distance…

Yikun Wang, Zuyan Liu, Ziyi Wang, Han Hu, et al.
Published
Nov 2025
Citations
8
Code
277 stars
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45

arXiv.org

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

MiroThinker v1.0 is an open-source research agent that introduces interaction scaling as a third performance dimension, alongside model size and context length. It trains models via reinforcement learning to handle deeper and more frequent agent–environment interactions, using a 256K context window and up to 600 tool calls per task. The 72B variant…

MiroMind Team, Song Bai, Lidong Bing, Carson Chen, et al.
Published
Nov 2025
Citations
62
Code
8.4K stars
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46

Independent research

IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling

IterResearch is a novel iterative deep-research paradigm that addresses the limitations of mono-contextual approaches, which accumulate all information in a single expanding context window, leading to context suffocation and noise contamination. It uses an MDP-inspired architecture with strategic workspace reconstruction, maintaining an evolving report as…

Guoxin Chen, Zile Qiao, Xuanzhong Chen, Donglei Yu, et al.
Published
Nov 2025
Citations
14
Code
Not linked
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47

Annual Meeting of the Association for Computational Linguistics

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs

GroupRank is a novel groupwise reranking paradigm for LLM-based passage retrieval that balances the efficiency of pointwise methods with the accuracy of listwise approaches. It partitions candidate documents into small groups for parallel cross-document comparison, avoiding context-window limits and reducing latency. Training uses an answer-free data…

Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, et al.
Published
Nov 2025
Citations
3
Code
259 stars
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48

Independent research

Tongyi DeepResearch Technical Report

Tongyi DeepResearch is an open-source agentic large language model designed for long-horizon, deep information-seeking research tasks. It uses an end-to-end training framework combining agentic mid-training (two-stage continual pre-training) and agentic post-training (SFT and reinforcement learning), supported by a fully automated synthetic data pipeline…

Tongyi DeepResearch Team, Baixuan Li, Bo Zhang, Dingchu Zhang, et al.
Published
Oct 2025
Citations
18
Code
Not linked
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49

arXiv.org

AgentFold: Long-Horizon Web Agents with Proactive Context Management

AgentFold is a new web agent paradigm that addresses the trade-off between context saturation in ReAct-based agents and information loss from fixed summarization. It treats context as a dynamic workspace with Multi-Scale State Summaries and a Latest Interaction, using a 'folding' operation with two modes: Granular Condensation (preserving fine details) and…

Rui Ye, Zhongwang Zhang, Kuan Li, Huifeng Yin, et al.
Published
Oct 2025
Citations
65
Code
Not linked
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50

arXiv.org

RAG-Anything: All-in-One RAG Framework

RAG-Anything is a unified framework for multimodal Retrieval-Augmented Generation (RAG) that addresses the limitation of existing text-only RAG systems in handling real-world documents containing text, images, tables, and equations. The framework introduces dual-graph construction, which builds a cross-modal knowledge graph for non-textual content and a…

Zirui Guo, Xubin Ren, Lingrui Xu, Jiahao Zhang, et al.
Published
Oct 2025
Citations
18
Code
23K stars
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