WebThinker: Empowering Large Reasoning Models with Deep Research Capability
Authors: Xiaoxi Li
Jiajie Jin
Guanting Dong
Hongjin Qian
Yongkang Wu
Ji-Rong Wen
Yutao Zhu
Zhicheng Dou
Venue: NeurIPS 2025 poster ()
Keywords: Retrieval-augmented Generation
Large Reasoning Models
Deep Search
Deep Research
Primary area: applications
TL;DR: This paper propose WebThinker, a deep research agent that empowers LRMs to autonomously search the web, navigate web pages, and draft research reports, all within its reasoning process.

Abstract: Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate impressive long-horizon reasoning capabilities. However, their reliance on static internal knowledge limits their performance on complex, knowledge-intensive tasks and hinders their ability to produce comprehensive research reports requiring synthesis of diverse web information. To address this, we propose WebThinker, a deep research agent that empowers LRMs to autonomously search the web, navigate among web pages, and draft reports during the reasoning process. WebThinker integrates a Deep Web Explorer module, enabling LRMs to dynamically search, navigate, and extract information from the web when encountering knowledge gaps. It also employs an Autonomous Think-Search-and-Draft strategy, allowing the model to seamlessly interleave reasoning, information gathering, and report writing in real time. To further enhance research tool utilization, we introduce an RL-based training strategy via iterative online Direct Preference Optimization (DPO). Extensive experiments on complex reasoning benchmarks (GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks (Glaive) demonstrate that WebThinker significantly outperforms existing methods and strong proprietary systems. Our approach enhances LRM reliability and applicability in complex scenarios, paving the way for more capable and versatile deep research systems. The code is available at https://github.com/RUC-NLPIR/WebThinker.

WebThinker: Empowering Large Reasoning Models with Deep Research Capability
Xiaoxi Li Jiajie Jin Guanting Dong" Hongjin Qian', Yongkang Wu
Ji-Rong Wen', Yutao Zhu' Zhicheng Dou RRemmin University of China BAAI 'Huawei Poisson Lab (xiaoxi 1i, ytzhu, dou ) @ruc. edu. en
Abstract
Large reasoning models (LRMs), such as OpenAl-ol and DeepSeek-RI, demon- strate impressive long-horizon reasoning capabilities. However, their reliance on static internal knowledge limits their performance on complex, knowledge knowledggeintensive intensive tasks and hinders their ability to produce comprchensive research reports requiring synthesis of diverse web information. To address this, we propose WebThinker, a deep research agent that empowers LRMs autonomously search the web, navi- gate among web pages, and draft reports during the reasoning process. WebThinnke integrates Deep Web Explorer module, enabling LRMs to dynamically search, navigate, and extract information from the web when encountering knowledge gaps. It also employs an Autonomous Think-Search-and-DDri the model to seamlessly interleave reasoning, information gathering, and report writing in real time. To further enhance research tool utilization, introduce an RL-based training strategy via iterative online Direct Preference Optimization (DPO). Extensive experiments on complex reasoning benchmarks (GPQA, GAIA, Web Walkerke,QA, HLE) and scientific report generation tasks (Glaive) demonstrate that WebThinker significantly outperforms existing methods and strong proprietary systems. Our approach enhances LRM reliability and applicability in complex sce- narios, paving the way for more capable and versatile deep research systems. The code is available at https: : github. com F /RUC- NLP IR/ TR/WebTThiie WebThinker.
strategy, allowing
Complex Problem Solving
Scientific Report Generation
80
10
70,3
QwQ-328 328
RAG Swee25-722 Deperreaach
70-
RAG-QuQ-518
63.39%.4
Sonndh-u1-22
NSNN
Gaminid o Dasp Resonch
Overall Performance
WebThinkerSad5 (lure)
WrlThinkee-ddh (Ders)
7.777
77
70.0
7.8
3E.0
6.4
6-4
20
10
7.2
5-
GPQA
GAIA
WebWalker
Comprehensive Thorough.
Factuality
Coherence
Figure Overall performance comparison of WebThinker with other models across two tasks: complex reasoning problem-solving (left) and scientific research report generation (right).
"quual contribution.
Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Introduction
Recently, large reasoning models (LRMs) have demonstrated remarkable capabilities across various domains like math, code, and science [34, 48, 14]. However, when confronted with complex information research needs, models that rely solely on internal knowledge struggle to conduct in-depth web information retrieval and to generate comprehensive and accurate reports through multi-step reasoning. Therefore, the deep integration of LRMs' reasoning capabilities with web information exploration has become a practical demand, which has sparked a series of deep research initiatives by OpenAI [35], xAI Grok3 [12]. and Google Gemini [10].
The objective of deep research technology is quite revolutionary:
enabling users to conduct deep searches, mining, and integration of comprehensive and reliable research information across the internet's massive information landscape through simple queries. This approach significantly reduces the time and costs associated with information gathering for researchers in knowledge-intensse fields (e.g., finance, science, engineering).
Unfortunately, existing open-source deep search agents typically employ retrieval auggeettd genera- tion (RAG) techniques with predefined workflows [1, 63, 42], which limits LRMs' ability to explore deeper web information and hinders close interaction between LRMs and search engines (Figure (a) & (b)). Consequently, developing a universal, flexible, open-source deep research framework has emerged as critical challenge urgently awaiting resolution in both academic and industrial circles.
To address this, we propose WebThinker, an autonomous deep research agent entirely powered by large reasoning models, as illustrated in Figure (c). It enables LRMs to autonomously conduct web searches and web page navigation to acquire external knowledge during the reasoning process, facilitating complex real-world problem solving. Furthermore, WebThinker allows LRMs to draft reports concurrently with thinking and searching. Once sufficient information is gathered for a specific section, the model can draft that part, ultimately producing comprehensive and customized reports tailored to users' research questions.
To empower LRMs with the capability to deeply explore web information, we design Deep Web Explorer module that enables LRMs to search, navigate pages by clicking interactive elements (like links or buttons), and extract relevant information. Based the current query's 's search results, the LRM can initiate follow-up searches and traverse deeper links until it collects all relevant information.
To facilitate scientific report writing, we introduce an Autonomous Think-Searchaamd andehh--nn-DDea -Draft strategy that deeply integrates report writing with the reasoning and search processes of LRMs. Rather than generating the entire report all at once after searching, seek necessary knowledge in real-time. To achieve this, we equip LRMs with three specialized tools: (1) drafting content for specific chapters, (2) checking the current report, and (3) editing the report. These tools enable LRMs to autonomously enhance the report by maintaining comprehensiveness, coherence, and adaptability to newly discovered information during reasoning.
approach enables the model to draft and
To further unlock the deep research potential of LRM backbones, we develop RL-based training strategies to optimize end-to-end task performance. We leverage the LRM equipped with the research tools to sample large-scale reasoning trajectories from complex tasks [46, 56, 47, 65, 25, 11J. Based on the accuracy of reasoning, tool usage, and final outputs, we construct preference pairs for online DPO training [40, 57, 5]. Through iterative, on- on-polic RL training. the model progressively improves its ability perceive, reason, and interact with research tools effectively.
We conduct extensive experiments on (1) knowledge-intensive complex reasoning benchmarks. including GPQA [41], GAIA [32], WebWalkerQA [56]. and Humanity's 's Last Exam (HLE) [37] to assess complex cabblm--olving, capabilities, and (2) open-ended reasoning tasks from Glaive [11] to evaluate report quality. As Figure shows, WebThinker consistently outperforms all competing approaches. Specifically, WebThinker surpasses Search-ol by 21.9% and 36.2% on the GAIA and HLE datasets, respectively, and even outperforms Grok3 and Gemini2.0 report generation tasks.
2 Related Work
Large Reasoning Models. Large reasoning models (LRMs) enhance test-time performance through extended reasoning, setting them apart from traditional large pre-trained models that scale mainly with model size or data volume [15, 61, 62, 28, 3]. Models like OpenAl-ol [20], Qwen-QwQ [48], and

---

Answer
Qween
Libra
Seurch
Mere
Liama
Depih Coherence
Lack of Thinking
Research
O
Research Question
O
Question
Traditional LLMs
a Search
Traditional LLMS
No
Answer
Shalliee
Scarch
Sourch
Results
Planner
Scarch
Need Another
Sourch?
Ye
Beetioon...
Next
(a) Standard RAG
(b) Advanced RAG with Predefined Workflow
Research Tools: Deep web explore, Report drnftt
Ansseer
olee OwO
Research
Think
C
Deep Web
Explore
Returned
Info.
Think
Question
LLMs
Fiu-tto-Fiul Optimitation
Continuous Doop-Thinking with Tools
(c) WebThinker: Autonomous Tools Calling within Continuous Deep-Thinking Process (Ours)
Figure 2: Comparison of RAG paradigms: (a) Standard RAG workflow, (b) Iterative RAG workflow, and (c) WebThinker, reasoning agent that autonomously searches, deeply explores web pages, and drafts research reports, all within its continuous thinking process.
DeepSeek-RI [14] demonstrate explicit chain-of- (CoT) reasoning [55], resembling human system-2 thinking in tasks such math and programming [28]. Various strategies have been proposed to achieve ol-like reasoning capabilities [9, 4, 45, 59, 16]. Some approaches introduce intentional reasoning errors during training help models partially internalize reasoning patterns [39, 64). Others enhance reasoning ability through distilled training data [33]. More recently, reinforcement learning has been explored as means to develop long CoT abilities in LLMs [14, 59, 16]. However, these methods are constrained by their reliance on static, parameterized architectures that lack access to external world knowledge. This limitation becomes particularly problematic in complex reasoning tasks requiring extensive real-world information.
Retrieval-Augmeetee Generation. parrivval-aummetted generation (RAG) enhances generative models by integrating retrieval mechanisms, enabling access to external knowledge beyond static parameters [24, 66]. Recent advances cover multiple dimensions, including retrieval necessity [44], query reformulation [31, 52, 69], document compression [60, 23, 68), denoising [30, 21]. and mnstruction-folloving [6]. Moreover, complex workflows such as structured planning [51, 58, 17] and
decision-making frameworks [29, 13] have shown notable gains in multi-hop reasoning, planning, and domain-specific tasks [53]. Recent work has also explored integrating ol ol-style reasoning with retrieval. For example, Search-ol [26] incorporates an agentic RAG framework and a Reason-in- Documents module merge retrieval with reasoning via prompt engineering. Other studies employ reinforcement learning to train reasoning with search capabilities from scratch [22, 43, 2, 67, 50, 8, 27, 38, 54], showing strong results Wikipedia-basse QA. Nonetheless, these methods fall short in adapting to complex real-world reasoning scenarios and comprehensive report- tasks.
3 Methodology
3.1 Problem Formulation
We consider a complex reasoning task that requires both multi-step reasoning and the utilization of research tools. The objective is to generate a comprehensive answer solution for given task query q- guided by an instruction I. A solution comprises a logical reasoning chain R and a final output (which could be an answer or a signal indicating completion). WebThinker enables the reasoning model to autonomously invoke tools from an available set T during its reasoning process, which can be formalized as the mapping (T, q, T) - (R,y). y). The generation process can be expressed as:
P(R,y v T, =
T. P(R, Rice I, (07)re))
II
Ty
P(ye Vet. R, T.9),
(1)
Reasoning with Tools
Final Output Generation
where T, the number of tokens in the reasoning sequence R. The token at position is R,, and R.c: represents all tokens generated before position l. (Or)rer denotes the outputs of all tool calls made before position Similarly, T, is the length of the output sequence y. with y/ being the token at position and y<u indicating all generated output tokens before position
3

---

Main Reasoning Process
Let me with linst seurch:
Wat. might nool
Firal
rrononing stop
Indo
Research
Question
Solving Mode
(1) Problem
Roastning Agest witd Deep Research Capabiliey
WERTHINKER:
Atswor
Gallin
rosert checaing
waiting
colling
Wich Explorer
Generation Mode
Explerer
LLM
Research
Plan
Scart Indo Deep Wich Explance
w
Research
Ubick Fide Besnact Repeet will Extared
Think F'w indo wiil aNSSON AS
WHite
generato
Check & Edit
Repect
Repoct
W multiple daps
Deep Web Explorer
Report Drafiing Tools
Piral
Curent
WESTHINKRR
Reques
Writing
Remese
Docaments
Relevant
LIM
Draf of Current
Section
telick)
(seach))
Decument Memery
Checking Reques
Canest
Farmet all tilleieb-itllss
Current Report Outline
-000
Repurt
Eacht exploend wet is added.
Web
Lonk Hence
in report uning
I. provides factual informationn maiit
Reques
Editing
Remeve
Documents
Relevant
LLM
Revised Repurt
Figure 3: Overview of the WebThinker framework. It operates in two modes: (1) Problem-Solving Mode equips reasoning models with a search tool backed by a Deep Web Explorer, enabling thorough web exploration to retrieve relevant information for solving complex real-world problems. (2) Report Generation Mode extends the model's capabilities with writing, checking, and editing capabilities, allowing it to iteratively craft comprehensive research reports while thinking and searching.
3.2 Overview of the WebThinker Framework
WebThinker is designed to enhance large reasoning models with deep research capabilities by enabling autonomous web exploration and report generation during the reasoning process. As illustrated in Figure 3, WebThinker operates in two primary modes:
Problem-Solving Mode: Empowers the LRM with a Deep Web Explorer module. When encountering knowledge gaps, the LRM can autonomously initiate web searches, navigate through web pages by clicking links or buttons, and extract relevant information before continuing its reasoning. This facilitates in-depth information gathering beyond standard shallow search.
strategy. The LRM interleaves reasoning, information seeking (via the Deep Web Explorer), and report generation. It utilizes specialized tools for drafting, checking, and editing report sections, executed by an assistant LLM leveraging an explored document memory, to ensure the final report is comprehensive, coherent, and grounded in the gathered evidence.
Report Generation Mode: Implements an Autonomous
Saink-Search-and-Drre
3.3 Solving Complex Reasoning Tasks with the Deep Web Explorer
In the Problem- Solving Mode, WebThinker tackles complex tasks requiring knowledge beyond the LRM's internal parameters. The core component is the Deep Web Explorer tool, Texp E T, which the LRM can invoke during reasoning (Figure 3).
Given task-specific instruction I and query q, the LRM generates reasoning chain R. potentially interspersed with calls to the Deep Web Explorer. The final output in this mode is typically direct answer The generation process formalized as:
P(R, a Lg:9) = II P(R. Ren, Iy: (02)
II
1=1 P(a: aca, R, Las 4),
(2)
where a = (aj...., (T.)) ) is the answer sequence. {O Deep Web Explorer calls completed before reasoning step t.
(}) (<v(()) denotes the set of outputs from all
The Deep Web Explorer itself is driven by the LRM, operating under a specific instruction Ie. It utilizes two elementary tools: search engine T, to retrieve web pages based on a generated query g. and a navigation tool T, interact with elements (e.g., click links) on the currently viewed page(s) DD The explorer, triggered by an information need 4s. generates its own internal reasoning chain RR to decide whether search further or navigate deeper based on the evolving web content D, encounters. Its goal is to produce concise output Oxxp that addresses the knowledge gap in the main

---

reasoning chain R. The exploration process within the tool is modeled as:
P(Re, Oxsp 4. D,,)) = II P(Re. Re 9s. 1e) P(Oexp) | Re: 9s De),),
(3)
where T. is the length of the explorer's reasoning chain Re. Dr represents the web content available step t, which dynamically changes based search and navigation actions. This hierarchical structure allows the main reasoning process to delegate complex information gathering tasks to the Deep Web Explorer, which can recursively search and navigate the web.
3.4 Generating Comprehensive Reports via Autonomous Think Shink-Search-and--ras
In the Report Generation Mode, the LRM autonomously produces comprehensive reports by inter- leaving reasoning, searching, and writing. Besides the Deep Web Explorer (Texp) for knowledge acquisition, the LRM utilizes a set of report writing tools Twrite = {Taraftt Teheck: Tedin). These tools are implemented by assistant LLM, separating the complex task orchestration performed by the main LRM from the detailed text manipulation required for report writing.
All web pages explored via Teup are accumulated in document memory M. When the main LRM decides invoke writing tool (e.g., Tarat), it generates an editing instruction e. The assistant model then receives e, the current report state and relevant documents Diop. retrieved from M. It produces the updated report content nnww according to:
P(rnew e, Dupp, II T P(Fnew,t F'newsctyy e, D Pupk,r))
where T, T.... is the length of the newly generated/edited report content Ynew-
The main LRM's role is to orchestrate the overall process: performing reasoning steps, deciding when to explore for more information using Texp. and determining when and how to modify the report using Twite. The main reasoning process concludes when the LRM generates the EOS token, denoted ylend. This overall process, conditioned on the initial instruction I and query g. is formalized as:
P(R, Neend 1, = II t=1 P(R, Ren, 1 g, (O (0(p))<)))))
P(yend R,,),
(5)
where ( (<(()) represents outputs from prior Deep Web Explorer calls. The document memory M serves as the knowledge base for the assistant LLM (Eq. 4) executing the writing operations.
3.5 Improving LRMs with Research Tools via Reinforcement Learning
To enhance the LRM's ability to effectively utilize research tools that range from high-level web exploration and report manipulation elementary search and navigation actions, we employ on- policy RL training. This focuses on constructing preference data reflecting desired tool usage patterns and applying an iterative online DPO [40, 57, 5] strategy.
Preference Data Construction. We generate diverse reasoning trajectories using WebThinker on complex datasets (e.g., SuperGiQQ [46], WebWalkerQA [56], OpenTToough [47]. Reason- ing [65], NuminaMath [25], and Glaive [1]]). For each query the LRM self-samples n distinct trajectories (R(*) > capturing varied solution strategies and tool usage patterns across the main reasoning chain and the Deep Web Explorer's internal operations.
Our goal is to identify trajectories that demonstrate not only correctness but also efficient use of the research tools. To achieve this, we establish preference pairs (R... R2), where R,,, is the preferred trajectory and R2 is the dis- dis-preferred trajectory. We apply the following criteria iteratively in order of priority to pairs of sampled trajectories (R,, R,) for the same task q:
Overall Correctness/Report Quality: If R, yields correct final answer (for reasoning tasks) or higher quality final report (for report generation tasks), while R, does not, then R, is preferred (Ri R4, R, R2)) This rule takes precedence over all others.
2. Tool Efficiency: If both R, and R, reach the correct final answer, the trajectory with fewer total
tool calls is preferred. If total_tool__eee cool_calls(RR)
< total_tool tool_calls((R,)) then R., = R, R₂ Ry. If both R, and R, are correct and involve the same number of tool calls, the shorter trajectory is preferred when the length ratio exceeds the threshold 7 > If len(output,) //lee ( > y, then R,, R, Ri- R₃.
3. Thinking Conciseness:
5

---

Table 1: Main results challenging research tasks, including PhD--evel science QA, general AI assistants, and web exploring benchmarks. We report Pass@ metric for all tasks. For 32B models, the best results are in bold and the second underlined. Results from larger closed-soureed models are in gray color for reference. < denotes results from their official releases.
GPQA (Science QA)
GAIA (General Assist.)
WebWalkerQA
Method
Phy.
Chem.
Bio.
Avg.
Level
Level
Level
Avg.
Easy
Med.
Hard
Avg.
Direct Reasoning (w/o Retrieval)
Qwen2.5-32B
52.3
30.1
68.4
43.4
20.5
13.6
3.3
3.1
DeepSeck-R1-32B
82.5
41.9
73.7
62.6
23.1
17.5
4.2
3.8
QwQQ32B
68.4
64.1
Qwen2.5-72B
58.1
39.8
57.9
49.5
20.5
13.5
14.6
9.4
3.3
6.3
GPT-4o
62.8
46.2
68.4
55.6
23.1
15.4
17.5
4.2
5.5
DeepSeek-R1-671B
84.2
74.2
43.6
26.9
31.1
ol-preview'
59.9
65.9
73.3
11.9
10.4
7.9
9.9
Enhancing Reasoning with RAG Workflow
Qwen2.5-322
52.0
Query Planning
61.6
40.9
52.6
51.0
30.8
20.4
29.4
36.4
25.0
30.7
w/ Iterativv RAG
64.0
41.9
57.9
53.0
35.9
19.2
8.3
24.3
30.6
35.7
25.4
30.9
RAG-QwQ-32B
84.9
46.2
63.2
64.6
33.3
36.5
8.3
32.0
36.9
26. 1
33.5
31.2
w/ Query Plamning
87.2
46.2
68.4
66.2
48.7
25.0
8.3
32.0
28.8
35.7
30.8
32.5
w/ Iterative RAG
84,9
45.2
73.7
65.2
51.3
28.8
8.3
35.0
29.4
32.9
31.3
31.5
Autonomous Search within Reasoning
OpenAl Deep Research"
74.3
69.1
47.6
67.4
Search-o1-32H1
84.9
49.5
73.7
67.2
53.8
34.6
16.7
39.8
43.1
35.0)
27.1
34.1
WebThhiner-322B-et 32B-Basc
87.2
5166
68.4
68.7
53.8
44.2
16.7
44.7
47.5
41.1
39.2
41.9
WebThinker-32B-RLL
90.7
50.5
78.9
70.7
56.4
50.0
48.5
58.8
44.6
40.4
46.5
By applying these rules across all valid sampled trajectory pairs for all tasks, we construct collection D = [[I, Rw, Ri)x} } of preference pairs.
Iterative Online DPO Training. We utilize the constructed preference dataset DD to train the LRM using iterative online DPO. The standard DPO loss function aims to increase the likelihood of preferred trajectories Rn while decreasing the likelihood of dispreferred trajectories Rj, relative to
reference policy Tref
Te (Ro 1,4)
To To 1,4) )}
(TPO Teet) - (R., RD log (Blog
Blog
Tee (Rw 1.g) The (R; I.g)
where me is the policy being trained, 8 is a hyperparameter controlling the deviation from the reference policy, and o is the sigmoid function.
We employ an iterative online scheme: (1) Train my on the current preference set 'D using Eq. 6. (2) Use the updated Ty sample criteria (1-3) to the new trajectories to generate an updated preference set D' (4) Set D +- D' and
trajectories for the tasks (exploration). (3) Apply the preference
Tief me. then repeat from step (I). This iterative process enables the LRM to refine its tool usage strategy, continuously improving performance via on-policy interaction with the environment.
Experiments
4.1 Tasks and Datasets
We evaluate WebThinker on two primary task categories: Complex Reasoning Benchmarks: Tests multi-step reasoning with external knowledge using: GPQA [41] (PhD-level science QA in physics, chemistry, biology): GAIA [32] (general Al assistant evaluation complex information acquisition tasks); WakkerA [56] (deep web navigation and information extraction); and Humanity's Last Exam (HLE) [37] (extremely challenging problems across disciplines requiring advanced search and reasoning skills). Accuracy is judged by Qwenn.5-72BBInnte Instrrttt [62]. Scientific Report Generation: Evaluates synthesis of research reports for open-ended questions using glaiveai/reassoing.. saatssonnnn------mm 11-20m

---

Table 2: Main results Humanity's s Last Exam. We report @ metric for all tasks. For 32B models, the best results are bold and the second are underlined. Results from larger or closed- sourced models are in gray color for reference. denotes results from their official releases.
Humanity's 's Last Exam (Extremely Hard Reasoning Tasks)
Method
Math
Bio/Med
Physics
CS/AI
Human.
Chem.
Engineer.
Other
Avg.
Direct Reasoning (wlo Retrieval)
Qwen2.5-322
6.0
7.0
2.01
3.2
10.0
0.0
4.4
5.4
DeepSeck-R1es R1-328
6.9
8.3
3.5
4.5
7.3
7.4
7.0
5.7
6.4
QwQ-32B
12.6
14.0
4.0
7.9
6.0
13.3
5.3
4.4
9.6
GPT-4o7
2.4
2.2
2.9
1.9
3.5
Gemini-2.0 2.0-Fi--- Saa--Fla----naen !
8.5
5.3
6.5
4.0
7.1
DeepSeck-R1-6715
9.3
8.6
5.8
7.4
11.0
5.6
10.3
7.5
8.6
03-mini (Medium)"
14.0
9.8
11.5
8.2
6.7
10.2
7.7
6.5
11.1
03-mini (High)?
18.8
11.1
14.2
11.1
6.2
10.2
7.7
8.0
14.0
Enhancing Reaxoning with RAG Workflow
JAG-Qwee2.5-322
4.2
7.0
8.2
1.6
8.0
13,3
5.3
0.0
4.8
w/ Query Planning
6.0
4.7
4.0
6.3
4.0
6.7
5.3
6.7
5.6
w/ Iterative RAG
5.1
7.0
4.0
4.8
6.0
13.3
5.3
4.4
5.4
RAG-QuQ-32B
7.9
14.0
2.0
4.8
14.0
0.0
0.0
4.4
7.2
Query Planning
11.2
16.3
4.0
4.8
12.0
6.7
11.1
9.6
Iterative RAG
10.2
14.0
4.0
10.0
10.5
8.9
Autonomous Search within Reasoning
OpenAl Deep Research'
26.6
Seurch-o1-32B
12.1
11.6
2.0
14.0
6.7
10.5
15.6
10.8
WebThinker-2BB-Bus
14.9
16.3
6.0
6.0
20.0
21.1
15.6
WehThinker-BB 32B-BBBBB
25.6
15.6
(Glaive) [11], a large-scale dataset with general, open-ended reasoning questions covering wide range of subjects. Reports are evaluated using average scores judged by PrepSeek-R1-677 [14] and GPT-4o [19]. Details on datasets and evaluation are in Appendix and C.
4.2 Baselines
We compare against three types of methods: (1) Direct Reasoning: Models using only inter- nal knowledge without search, including open-source models 1et.m2..-32272---nnitiiee [62],
[18], QwQ-32B [48], L
[7]) and closed-source
models (DeepSeek-R) ---nne [14], GPT-4o [19], o1-preview [34], o3-mini [36), Demini-220--Flas
Thinking [10]). (2) Eetrieval-Augmenntee Reasoning: Methods using external knowledge from search engines: Standard RAG (retrieves for the original query): (ii) RAG w/ Query Planning (decomposes query, retrieves for sub-queries, then generates); and (iii) Iterative RAG (retrieves infor- mation iteratively). (3) Autonomous Search within Reasoning: Systems integrating search actions into reasoning, including the open-source Search-ol framework [26], and non- con-roprrietary systems like OpenAl Deep Research [35], Grok3 DeeperSearch [12], and Gemini2.0 Deep Research [10].
4.3 Implementation Details
We use QwQ-32B [48] WebThinker's backbone in our main results. Assistant models use 0wen2.5- [62] with the same parameters the backbone. Generation uses max 81920 tokens, temperature 0.7, top_p 0.8, top_k 20, and repetition penalty 1.05. Search uses Bing Web Search API (US-EN region, k=10) with content fetched via Craw14Al [49]. Training involves iterations of online DPO with a max sequence length of 32,768. For baselines not trained for ol-like reasoning, we use Chain-of-Thought (CoT) [55] prompting. Detailed instructions can be found in Appendix C.
4.4 Results on Complex Problem-Solving
Main Results. Tables 1 and 2 show main results complex reasoning tasks. Key findings include:

---

Analysis of Information Scopes
Glaive (General Research Tasks)
Method
Comp. Thorough, Fact. Coherence Avg.
Retrieval-Angmented Report Generation
40
RAG-Qwen2.5-722
5.7
5.3
6.4
6,3
5.9
20-
RAG-DeepScck-RI
6.6
6.4
7.1
7.1
6.8
0-
Non-Proprietary Systems
Grok3 DeeperSearch
7.0
6.5
-20
Gemini2.0 Deep Reseurch
8.0
7.7
7.9
BAG
-60
Groks
Autonomous TThin--Sercch-and-he
(Ours)
Gomant
WebThinker-32B-Buss WebThinker-32B-RL
8.2
7.7
8.0
8.4
7.7
8.1
-10
-20
20
Figure 4: Main results on scientific report generation for general research tasks. Left: Overall performance comparison, reporting average scores evaluated by DeepSeek-R1 and GPT-4o. Right: t-SNE visualization of content embeddings from three randomly sampled topics.
Direct Generation
NBN
Stun,dard RAG
8008 WebTtinker (Ours)
50
10
10
10
Overall Performance
16.7
35.0
58.1
30-
30-
27-45
30
20-
20
17.5
78
6351
5.4
0-
GAIA
Web.
HLE
Report
GAIA
Web.
HLE
Report
GA1A
Web.
HLE
Report
DeepSeek-RR R1-7B
(b) DeepSeek R1-14B
(c) DeepSeek R1-32B
Figure: 5: Analysis of the performance of WebThinker with DeepSeek R1-based models across 7B, 14B, and 32B sizes, compared with direct generation and standard RAG approaches.
1. Base LRM & RAG Workflow Limitations: Reasoning models (e.g., QwQ-32B) surpass standard LLMs (e.g.. Qwen2.5-72B GPQA avg.) but falter knowledge-intensive tasks (GAIA, WcbWalkerQA). RAG improves performance on these tasks but shows inconsistent gains on complex HLE tasks that require deep integration of reasoning and search.
2. Autonomous Search Advantage: Autonomous search (e.g.. Search-ol) yields notable gains over direct reasoning and basic RAG, especially GAIA, WebWalkerQA, and HLE (e.g., HIE avg: Search-ol 10.8 vs. RAG-QwQ-32BB 7.2).
3. WehThinker Framework Superiority: Our training-free WebThinker-323 32B Base, utilizing its
Deep Web Explorer for deeper web exploration, consistently surpasses prior methods like Search-ol across all benchmarks (e.g., +22.9% WebWalkerQA and +20.4% HLE, respectively).
4. RL Improvement: RI-trained WebThiiner-32BBB achieves SOTA among 32B models on all benchmarks, substantially improving over its Base version (c.g., +8.5% on GAIA and +21.5% on HLE, respectively). Notably, on HLE, it surpasses even stronger models like 03-mini (High).
4.5 Results on Scientific Report Generation
Main Results. Figure 4 (left) presents WebThinker's performance on the Glaive scientific report generation task against RAG and non non-propri baselines, evaluated on Completeness (Comp.), Thoroughness (Thorough.), Factuality (Fact.), and Coherence. WebThinker achieves the top overall score (8.0), surpassing RAG baselines and advanced systems like Gemini-Deep Research (7.9). excels in Completeness (8.4) and Thoroughness (8.2), matching top Factuality (7.7) and Coherence (7.8) scores. These results highlight the Autonomous tiveness in enabling LRMss iteratively refine content through dynamic information gathering, yielding more comprchensive and coherent reports than predefined RAG workflows.
Sean---earr--and-Deeee strategy's 's effec-

---

Table 3: Ablation studies of WebThinker. Left: Performance on complex reasoning benchmarks
(GAIA, WebWalkerQA, HLE). Right: Performance on the scientific report generation tasks (Glaive).
Complex groblem--olvingg
Scientific Report Generation
Method
Method
GPQA
GAIA
Web.
HI.F
Avg.
Comp.
Tho.
Fact.
Coh.
Avg.
WebThiinker332--I
70.7
48.5
46.5
15.8
45.4
Web Web WebThhiker-322--
8.3
8,4
7.7
7.9
8.1
w/ Offiiee DPO
69.2
45.6
44.0
14.2
43.2
w/o Training (Base)
8.4
8.2
7.7
7.8
8.0
w/o Training (Base)
68.7
44.7
41,9
13.0
42.1
w/o Deep Web Explorer
7.9
7.9
7.5
7.6
7.7
w/o Link Clicking
69.7
42.7
42.6
15.2
42.6
w/o Report Check & Edit
8.1
8.0
7.6
6.9
7.7
w/o Deep Web Explorer
63.6
38.8
38.5
12.0
38.3
w/o Auto. Report Draft
6.3
6.4
6.8
7.0
6.6
Qualitative t-SNE visualization of content embeddings for reports on three randomly sampled Glaive topics (Figure 4, right) shows distinct topic clusters, where each point is report's 's embedding. Notably, WebThinker's reports often form broader sub-clusters within each topic group. This suggests WebThinker, with its Deep Web Explorer and iterative drafting, explores and synthesizes information from more diverse perspectives and depths than other methods. Its autonomous nature allows dynamic adaptation of information gathering and writing evolving report needs, yielding richer and more unique information coverage.
Analysis of Information Scopes.
4.6 Adaptability of WebThinker Across Different LRM Backbones.
We assessed WebThinker's adaptability with DeepSeek-R1 models of varying sizes (7B, 14B, 32B; Figure 5). To ensure stable tool usage, we conducted cold-start SFT (using 7.8k trajectories from QwQ-32B WebThinker) followed by RL training (Section 3.5). On all tasks and model sizes, R1- based WebThinker consistently outperformed direct reasoning and standard RAG. For example, Web WehThhnnee-RI.. achieved relative gains of 174.4 % (GATA) and 422.6 % (WebWalkerQA) over direct generation, and 82.9% (GAIA) and 161.3% (Web WebkallerQAA over standard RAG. Similar substantial improvements were observed across other settings, demonstrating WebThinker's general applicability and effectiveness in enhancing diverse LRMs' deep research capabilities.
4.7 Ablation Studies
Ablation studies (Table 3) assessed key WebThinker components. (1) RL Training: Iterative online RL markedly improves problem-solving (Avg. 44.9 vs. 42.1 Base, 43.2 offline DPO), validating our training strategy. Its impact on report generation is minimal. as the base framework is already effective. (2) Deep Web Exploration: Removing the Deep Web Explorer severely degrades both problem-solving (Avg. 38.3) and report generation (Avg. 7.7), proving its criticality. Disabling link- clicking alone also impairs mooblem-solving (Avg. 42.6), highlighting the value of deeper exploration. (3) Report Generation Components: Removing autonomous drafting causes the largest quality drop in report generation (Avg. 6.6), stressing the importance of interleaved thinking, searching, and drafting. Disabling check-and-edit tools also reduces quality (Avg. 7.7), notably coherence (6.9 vs. 7.9), reinforcing the need for iterative refinement. These findings affirm WebThinker's design.
5 Conclusion
In this work, we present WebThinker, which equips large reasoning models with deep research capabilities by enabling autonomous web exploration and comprehensive report generation through agentic reasoning. We develop: (1) Deep Web Explorer for dynamic web page navigation, (2) an autonomous mhink-Search-and-De -raaft strategy that integrates reasoning, searching, and writing, and (3) RL-based training strategies to enhance research tool utilization. Experiments on complex reasoning benchmarks and scientific report generation tasks demonstrate that WebThinker outperforms existing methods and proprietary systems. Despite its strengths, WebThinker has several limitations that require further improvement. First, it cannot process multimodal information such as images and videos, making the development of multimodal deep research systems an important direction. Second, it currently supports only limited set of tools, underscoring the need for tool scalability and generalization. Finally, extending WebThinker to support GUl-based web exploration would enable it to handle more complex and real-world interactive tasks.

---

Acknowledgments
This work was supported by Beijing Municipal Science and Technology Project No. 2231100010323009 National Natural Science Foundation of China No. 62272467, Beijing Natural Science Foundation No. L233008. The work was partially done at the Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE.
References
[1] Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannanch Hajishirzi. Self-rag: Learn- ing to retrieve, generate, and critique through self-reflection. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. Open- Review.net, 2024.
[2] Mingyang Chen, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang. Jeff Z. Pan, Wen Zhang, Huajun Chen, Fan Yang. Zenan Zhou, and Weipeng Chen. Research: Learning to reason with search for lims via reinforcement learning. CoRR, abs/2503.19470, 2025.
[3] Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, and Wanxiang Che. Towards reasoning A survey of long chain-of-thouge for reasoning large language models. CoRR, abs/2503.09567, 2025.
[4] Zhipeng Chen, Yingqian Min, Beichen Zhang, Jie Chen, Jinhao Jiang, Daixuan Cheng, Wayne Xin Zhao, Zheng Liu, Xu Miao, Yang Lu, Lei Fang, Zhongyuan Wang, and Ji-Rong Wen. An empirical study on eliciting and improving ri-like reasoning models. CoRR, abs/2503.04548 2025.
[5] Guanting Dong, Keming Lu, Chengpeng Li, Tingyu Xia, Bowen Yu, Chang Zhou, and Jingren Zhou. Self-play with execution feedback: Improving instruction-folloiing capabilities of large language models. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025.
[6] Guanting Dong, Xiaoshuai Song, Yutao Zhu, Runqi Qiao, Zhicheng Dou, and Ji-Rong Wen. Toward verifiable insrruction-folloiing alignment for retrieval augmented generation. In Toby Walsh, Julie Shah, and Zico Kolter, editors, AAAI-25. Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 March 4, 2025, Philadelphia, PA, USA, pages 23796-23804 AAAI Press, 2025.
[7] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, al. The llama 3 herd of models. arXiv preprint arXiv:2407.2178. 2024.
[8] Jiazhan Feng, Shijue Huang, Xingwei Qu, Ge Zhang, Yujia Qin, Baoquan Zhong, Chengquan Jiang, Jinxin Chi, and Wanjun Zhong. Retool: Reinforcement learning for strategie tool use in llms. CoRR, abs/2504.11536 2025.
[9] Mohamed Amine Ferrag, Norbert Tihanyi, and Mérouane Debbah. Reasoning beyond limits: Advances and open problems for llms. CoRR, abs/2503.22732. 2025.
[10] Gemini.
Gemini deep research.
https:://emiiee wwt.i////..oooeeeeeee//////o
deep- reserrhh, 2025.
[11] Glaive.
https: httpe://haggiiiffccso.ooottttttt reasoning-ve 120m, 2025.
reasoning-vl-20m.
[12] Grok.
Grok 3 beta the age of reasoning agents. https::// /ttp///::////aa////////////////
2025.
[13] Xinyan Guan, Jiali Zeng, Fandong Meng, Chunlei Xin, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, and Jie Zhou. Deeprag: Thinking to retrieval step by step for large language models. CoRR, abs/2502.01 1422, 2025.
10

---

[14] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, al. Deepseek-rl incentivizes reasoning in llms through reinforcement learning. Nature, 645(8081 1):633-6366 2025.
[15] Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al. Scaling laws for autoregressive generative modeling. arXiv preprint arXiv:2010. 14701, 2020.
[16] Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung- Open-reasonerr-zero An open source approach to scaling up reinforcement learning on the base model. CoRR, abs/2503.24290, 2025.
Hleug-Yeungg Yeung Shum.
[17] Yunhai Hu, Yilun Zhao, Chen Zhao, and Arman Cohan. MCTS-RAG: enhancing retrieval- augmented generation with monte carlo tree search. CoRR, abs/2503.20757, 2025.
(18) Binyuan Hui, Jian Yang. Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Kai Dang, An Yang. Rui Men, Fei Huang, Xingzhang Ren, Xuancheng Ren, Jingren Zhou, and Junyang Lin. Qwen2.5-coder technical report. CoRR, abs/2409.121880 2024.
[19] Aaron Hurst. Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-40 system card. arXiv preprint arXiv:2410.21274 2024.
[20] Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed Bl-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai ol system card. arXiv preprint arXiv:2412. 2024.
[21] Jinhao Jiang. Jiayi Chen, Junyi Li, Ruiyang Ren, Shijie Wang, Xin Zhao, Yang Song, and Tao Zhang, Rag-star: Enhancing deliberative reasoning with retrieval augmented verification and refinement. Luis Chiruzzo, Alan Ritter, and Lu Wang, editors, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Lin- guisties: Human Language Technologies, NAACI 2025 Volume I: Long Papers, Albuquerque, New Mexico, USA, April 29 May 4, 2025, pages 7064-7074, Association for Computational Linguistics, 2025.
[22] Bowen Jin, Hansi Zeng, Zhenrui Yue, Dong Wang, Hamed Zamani, and Jiawei Han. Search- r1: Training llms to reason and leverage search engines with reinforcement learning. CoRR, abs/2503.09516, 2025.
[23] Jiajie Jin, Yutao Zhu, Yujia Zhou, and Zhicheng Dou. BIDER: bridging knowledge inconsistency for efficient retrieval-augeee augmented 1lms via key supporting evidence. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024, pages 750-761. Association for Computational Linguistics, 2024.
[24] Patrick Lewis, Fthan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retreeval-augmented generation for knowledgg-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459-9474, 2020.
[25] Jia LI, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Albert Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Yann Fleureau, Guillaume Lample, and Stanislas Polu. Numi- namath. / //github. [https: /ttp:::///gggg//// face www.........oo.............. /um wawtiiiaMtthh1.5 (https: na / aimo--proorrte pttpppogrrsss-ppeeeel/looaaaaaaa report/numina_catasst. pdf), 2024.
[26] Xiaoxi Li. Guanting Dong. Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. Search-ol: Agentic search-enhanced large reasoning models. CoRR, abs/2501. 10156666,6666, 2025.
[27] Xuefeng Li, Haoyang Zou, and Pengfei Liu. Torl: Scaling tool-integrated RI. CoRR, abs/2503.23383, 2025.

---

[28] Zhong- Zhi Li, Duzhen Zhang, Ming-Liang Zhang, Jiaxin Zhang, Zengyan Liu, Yuxuan Yao, Haotian Xu, Junhao Zheng, Pei-Jie Wang, Xiuyi Chen, Yingying Zhang, Fei Yin, Jiahua Dong, Zhijiang Guo, Le Song, and Cheng-Lin Liu. From system to system 2: A survey of reasoning large language models. CORR, abs/2502. 174199 2025.
[29] Zhuoqun Li, Haiyang Yu, Xuanang Chen, Hongyu Lin, Yaojie Lu, Fei Huang, Xianpei Han, Yongbin Li, and Le Sun. Deepsolution: Boosting complex engineering solution design via tree-based exploration and bi- bi-point thinking. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar, editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACI. 2025, Vienna, Austria, July 27- August 1, 2025, pages 4380-4396. Association for Computational Linguistics, 2025.
[30] Jingyu Liu, Jiaen Lin, and Yong Liu. How much can RAG help the reasoning of Ilm? CoRR, abs/2410.02338,, 2024.
[31] Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. Query rewriting for retrenell-uugmented large language models. CoRR, abs/2305.1428. 2023.
[32] Grégoire Mialon, Clémentine Fourrier, Thomas Wolf, Yann LeCun, and Thomas Scialom. GAIA: a benchmark for general Al assistants. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024.
[33] Yingqian Min, Zhipeng Chen, Jinhao Jiang. Jie Chen, Jia Deng. Yiwen Hu, Yiru Tang. Jiapeng Wang, Xiaoxue Cheng. Huatong Song, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang. and Ji-Rong Wen. Imitate, explore, and self-improve: A reproduction report on slow-thinking reasoning systems. CoRR, abs/2412.09413, 2024.
[34] OpenAL. Learning to reason with llms. 11ms, September 2024.
https:
index//
gearning-to-ressonn
[35] OpenAI. Introducing deep research. introducing Seeep-researche 2025.
https: https://openai.comm
[36] OpenAl. Openai 03-mini. https://opent watppenaa.commnnnnio 2025.
[37] Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Sean Shi, Michael Choi, Anish Agrawal, Arnay Chopra, Adam Khoja, Ryan Kim, Jason Hausenloy, Oliver Zhang, Mantas Mazeika, Daron Anderson, Tung Nguyen, Mobeen Mahmood, Fiona Feng, Steven Y. Feng, Haoran Zhao, Michael Yu, Varun Gangal, Chelsea Zou, Zihan Wang. Jessica P. Wang, Pawan Kumar, Oleksandr Pokutnyi, Robert Gerbicz, Serguei Popov, John-Clark Levin, Mstyslav Kazakov, Johannes Schmitt, Geoff Galgon, Alvaro Sanchez, Yongki Lee, Will Yeadon, Scott Sauers, Mare Roth, Chidozie Agu, Søren Riis, Fabian Giska, Saiteja Utpala, Zachary Giboney, Gashaw M. Goshu, Joan of Are Xavier, Sarah-Jane Crowson, Mohinder Maheshbhai Naiya, Noah Burns, Lennart Finke, Zerui Cheng, Hyunwoo Park, Francesco Fournier-Fache John Wydallis, Mark Nandor, Ankit Singh, Tim Gehrunger, Jiaqi Cai, Ben McCarty, Darling Duclosel, Jungbae Nam, Jennifer Zampese, Ryan G. Hoerr, Aras Bacho, Gautier Abou Loume, Abdallah Galal, Hangrui Cao, Alexis C. Garretson, Damien Sileo, Qiuyu Ren, Doru Cojoc, Pavel Arkhipov, Usman Qazi, Lianghui Li, Sumeet Motwani, Christian Schröder de Witt, Edwin Taylor, Johannes Veith, Eric Singer, Taylor D). Hartman, Paolo Rissone, Jaehyeok Jin, Jack Wei Lun Shi, Chris G. Willcocks, Joshua Robinson, Aleksandar Mikov, Ameya Prabhu, Longke Tang, Xavier Alapont, Justine Leon Uro. Kevin Zhou, Emily de Oliveira Santos, Andrey Pupasov Maksimov, Edward Vendrow, Kengo Zenitani, Julien Guillod, Yuqi Li, Joshua Vendrow, Vladyslav Kuchkin, and Ng Ze-An. Humanity's 's last exam. CoRR, (bs//2501.1449, 2025.
[38] Cheng Qian, Emre Can Acikgoz, Qi He, Hongru Wang, Xiusi Chen, Dilck Hakkani- Gokhan Tur, and Heng Ji. Toolrl: Reward is all tool learning needs. CoRR, abs/2504. 13958, 2025.
[39] Yiwei Qin, Xuefeng Li, Haoyang Zou, Yixiu Liu, Shijie Xia, Zhen Huang, Yixin Ye, Weizhe Yuan, Hector Liu, Yuanzhi Li, et al. O1 replication journey: A strategic progress report-part 1. arXiv preprint 24Xiv:2410.189888 2024.
12

---

[40]
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine, editors, Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurlPS 2023, New Orleans, LA, USA, December 10- 16, 2023, 2023.
[41] David Rein. Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. GPQA: A graduate-level google-proc q&a benchmark. CoRR, abs/231 1. 12022. 2023.
[42] Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. Enhancing retrieval-augmenteed large language models with iterative retrieval-generation synergy. In Houda Bouamor, Juan Pino. and Kalika Bali, editors, Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023, pages 9248-9274. Association for Computational Linguistics. 2023.
[43] Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. R1-searcher: Incentivizing the search capability in llms via reinforcement learning. CoRR, abs/2503.05592 2025.
[44] Jiejun Tan, Zhicheng Dou, Yutao Zhu, Peidong Guo, Kun Fang, and Ji- Ji-Rong Wen. Small models, big insights: Leveraging slim proxy models to decide when and what to retrieve for llms. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024, pages 4420-4436. Association for Computational Linguistics, 2024.
[45] Xinyu Tang. Xiaolei Wang, Zhihao Lv, Yingqian Min, Xin Zhao, Binbin Hu, Ziqi Liu, and Zhiqiang Zhang. Unlocking general long of-thuot- reasoning capabilities of large language models via representation engineering. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar, editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2025, Vienna, Austria, July 27 August 1, 2025, pages 6832-6849. Association for Computational Linguistics. 2025.
[46] M.-A-P. Team, Xinrun Du Yifan Yao, Kaijing Ma. Bingli Wang, Tianyu Zheng, Kang Zhu, Minghao Liu, Yiming Liang, Xiaolong Jin, Zhenlin Wei, Chujie Zheng, Kaixin Deng, Shian Jia, Sichao Jiang, Yiyan Liao, Rui Li, Qinrui Li, Sirun Li, Yizhi Li, Yunwen Li, Dehua Ma. Yuansheng Ni, Haoran Que, Qiyao Wang. Zhoufutu Wen, Siwei Wu, Tianshun Xing, Ming Xu, Zhenzhu Yang, Zekun Moore Wang, Jun Zhou, Yuelin Bai, Xingyuan Bu, Chenglin Cai, Liang Chen, Yifan Chen, Chengtuo Cheng. Tianhao Cheng, Keyi Ding, Siming Huang, Yun Huang, Yaoru Li, Yizhe Li, Zhaoqun Li, Tianhao Liang, Chengdong Lin, Hongquan Lin, Yinghao Ma, Tianyang Pang, Zhongyuan Peng. Zifan Peng, Qige Qi, Shi Qiu, Xingwei Qu, Shanghaoran Quan, Yizhou Tan, Zili Wang, Chenqing Wang, Hao Wang. Yiya Wang. Yubo Wang, Jiajun Xu, Kexin Yang. Ruibin Yuan. Yuanhao Yue, Tianyang Zhan, Chun Zhang, Jinyang Zhang, Xiyue Zhang, Xingjian Zhang. Yue Zhang, Yongchi Zhao. Xiangyu Zheng, Chenghua Zhong, Yang Gao, Zhoujun Li, Dayiheng Liu, Qian Liu, Tianyu Liu, Shiwen Ni, Junran Peng. Yujia Qin, Wenbo Su, Guoyin Wang, Shi Wang. Jian Yang, Min Yang, Meng Cao, Xiang Yue, Zhaoxiang Zhang. Wangchunshu Zhou, Jiaheng Liu, Qunshu Lin, Wenhao Huang, and Ge Zhang. Supergpqa: Scaling LLM evaluation across 285 graduate disciplines. CoRR, 1a./2502..14399 2025.
[47] OpenThoughts Team. Open Thoughts. https: : /ttts:://opeetthuuhtett twwughtstt...ooo ai, January 2025.
[48] Qwen Team. Qwq: Reflect deeply on the boundaries of the unknown. Hugging Face, 2024.
[49] UncleCode. Crawl4ai: Open-source Ilm friendly web crawler & scraper. com/ http///ccecoodeccaaallll.. 4a 2024.
[50] Hongru Wang, Cheng Qian, Wanjun Zhong, Xiusi Chen, Jiahao Qiu, Shijue Huang, Bowen Jin, Mengdi Wang, Kam-Fai Wong, and Heng Ji. OTC: optimal tool calls via reinforcement learning. CoRR, 0ett//504..48000 2025.
13

---

[51] Liang Wang, Haonan Chen, Nan Yang. Xiaolong Huang, Zhicheng Dou, and Furu Wei. Chain- of-retrieval augmented generation. CoRR, abs/2501.14342, 2025.
[52] Liang Wang. Nan Yang, and Furu Wei. Query2doc: Query expansion with large language models. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6010, 2023, pages 9414-9423. Association for Computational Linguistics, 2023.
[53] Zhengren Wang, Jiayang Yu, Dongsheng Ma, Zhe Chen, Yu Wang. Zhiyu Li, Feiyu Xiong, Yanfeng Wang, Weinan E, Linpeng Tang, and Wentao Zhang. RARE: retrieval-augmented reasoning modeling. CoRR, abs/2503.23513 2025.
[54] Zihan Wang, Kangrui Wang. Qineng Wang, Pingyue Zhang, Linjie Li, Zhengyuan Yang, Xing Jin, Kefan Yu, Minh Nhat Nguyen, Licheng Liu, Eli Gottlieb, Yiping Lu, Kyunghyun Cho, Jiajun Wu, Li Fei-Fei, Lijuan Wang, Yejin Choi, and Manling Li. RAGEN: understanding self-evolution in LLM agents via multi-turn reinforcement learning. CoRR, abs/2504.20073, 2025.
[55] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. Chain-of-thouglee prompting elicits reasoning in large language models. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems 35; Annual Conference Neural Information Processing Systems 2022, NeurlPS 2022, New Orleans, LA, USA, November 28 December 2022, 2022.
[56] Jialong WU, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xic, and Fei Huang. Webwalker: Benchmarking Ilms in web traversal. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar, editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics Volume I: Long Papers), ACL 2025, Vienna, Austria, July 27 August I, 2025, pages (0290-1030)) Association for Computational Linguistics, 2025.
[57] Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, and Quanquan Gu. Self-play preference optimization for language model alignment. In The Thirteenth International Confer- ence on Learning Representations, ICLR 2025, Singapore, April 24-28. 2025. OpenReview.net, 2025.
[58] Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang. Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. Omnithink: Expanding knowledge boundaries in machine writing through thinking. CoRR, abs/2501.0975. 2025.
[59] Tian Xie, Zitian Gao, Qingnan Ren, Haoming Luo, Yuqian Hong, Bryan Dai, Joey Zhou, Kai Qiu, Zhirong Wu, and Chong Luo. Logic-rl: Unleashing LLM reasoning with rule-based reinforcement learning. CoRR, abs/2502.14768. 2025.
[60] Fangyuan Xu, Weijia Shi, and Eunsol Choi. RECOMP: improving sarrivval-uugmentr lms with context compression and selective augmentation. The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024.
[61] An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei. Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jianxin Yang, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Kegin Chen, Kexin Yang, Mei Li, Mingfeng Xue, Na Ni, Pei Zhang, Peng Wang. Ru Peng, Rui Men, Ruize Gao, Runji Lin, Shijie Wang, Shuai Bai, Sinan Tan, Tianhang Zhu, Tianhao Li, Tianyu Liu, Wenbin Ge, Xiaodong Deng, Xiaohuan Zhou, Xingzhang Ren, Xinyu Zhang, Xipin Wei, Xuancheng Ren, Xuejing Liu, Yang Fan, Yang Yao, Yichang Zhang, Yu Wan, Yunfei Chu, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, Zhifang Guo, and Zhihao Fan. Qwen2 technical report. CORR, abs/2407.10671, 2024. 2024.
14

---

[62]
An Yang, Baosong Yang. Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang. Jianhong Tu, Jianwei Zhang. Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report. CoRR, abs/2412.15115. 2024.
[63] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. The Eleventh International Conference Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.
[64] Tian Ye, Zicheng Xu, Yuanzhi Li. and Zeyuan Allen-Zhu. Physics of language models: Part 2.2. how to learn from mistakes on grade-school math problems. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025.
[65] Weizhe Yuan, Jane Yu, Song Jiang, Karthik Padthe, Yang Li, Dong Wang, Ilia Kulikov, Kyunghyun Cho, Yuandong Tian, Jason E. Weston, and Xian Li. Naturalreasoning: Rea- soning in the wild with 2.8m challenging questions. CoRR, abs/2502. 13124, 2025.
[66] Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, and Bin Cui. Retrieval-augmmened generation for ui-generated content: A survey. CORR, abs/2402. 0204402.1977.. 19473, 2024.
[67] Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, and Pengfei Liu. Deeprescarcher: Scaling deep research via reinforcement learning real-world environments. CoRR, abs/2504.03160. 2025.
[68] Yujia Zhou, Zheng Liu, Jiajie Jin, Jian- Yun Nie, and Zhicheng Dou. Metacognitive retrieval- augmented large language models. Tat-Seng Chua, Chong-Waan Ngo, Ravi Kumar, Hady W. Lauw, and Roy Ka-Wei Lee, editors, Proceedings of the ACM Web Conference 2024, WWW 2024, Singupore, May 13-17 2024, pages 1453-1463. ACM, 2024.
[69] Yutao Zhu, Jiajie Jin, Hongjin Qian, Zheng Liu, Zhicheng Dou, and Ji- Ri-Rong Wen. Single Ilm, multiple roles: A unified retrieval- augmented generation framework using role-specific token optimization. CoRR, abs/2505. 15444, 2025.
15

---

Appendix
Inference Process
17
A.1 Research-Related Tools
17
A.2 Inference Process of WebThinker
17
A.2.1 Inference for Main Reasoning Process
17
A.2.2 Inference for Deep Web Explorer
18
Datasets
18
B.1 Complex Reasoning Tasks
18
B.2 Scientific Report Generation Tasks
19
Instruction Templates
19
C.1 Instructions for WebThinker
19
C.2 Task Instructions
22
................................
C.2.1 Task Instruction for Qw Q-ased Models
23
C.2.2
Task Instruction for DeepSeck-RI-Be -base Models
................................
23
C,3 Instructions for Evaluation
23
C.3.1
Evaluation Instruction for Problem-Sone Solving Tasks
23
C.3.2
Evaluation Instruction for Report Quality
23
C.4
Additional Notes
25
Case Study
25
D.1
Case Study for Complex Problem Solving
25
D.2 Case Study for Deep Web Explorer
25
D.3 Case Study for Scientific Report Generation
26
Summary of Contributions
26
F Broader Impact
26
16

---

A Inference Process
A.1 Research-Related Tools
WebThinker utilizes several tools during its inference process:
Search Tool:
This tool is employed by both the main reasoning process and the Deep Web Explorer. In the main process, it invokes the Deep Web Explorer, while within the Explorer, it calls standard search engine API. The model signals search query by generating <ebegin_ quercch
and
Send_seerde - | The processed search results are then returned within <ibegia_searee sttrsseeerrrrrlleeeelee resulti>
and
<lend search resultis
Link & Button Clicking Tool:
This tool is exclusively used by the Deep Web Explorer to interact with web pages, typically invoking web crawler. We use Crawl4AI [49] for fetching web page
content. The model triggers this tool by generating <clbegin_eelice click linkls and
<<end click linkl]
The con-
tent retrieved from the clicked link (potentially summarized) is returned within <lbegja_click_reenee resultes
and <lend click resulti>
Report Writing Tools: These tools are specific to the Report Generation Mode and are invoked by the main reasoning process. They facilitate drafting and refining the research report.
Section Writing: Triggered by <ibegin_ write sectionls] |
section_namelnnconee namelncontents. sotisss_tt________t write <lend_ write ] This command instructs assistant LLM to write the specified section based on the provided content guidelines and gathered information. It does not return content directly into the main reasoning flow maintain coherence.
Triggered by <ibegin check articleb This command requests the current outline of the report. The system extracts all section and subsection titles to form the outline, which is then inserted into the reasoning context followed by <tend_check check articlebb) This avoids injecting the
Report Checking:
full report text, which could disrupt the reasoning flow.
Report Editing: Triggered by <ibegla_edit articci]) edit instruction [elend_edit artieele) This com-
mand instructs an assistant LLM to modify the existing report based on the provided editing
instructions. Similar to section writing, it doesn't return content directly into the main flow.
A.2 Inference Process of WebThinker
The overall inference process in WebThinker involves two main components: the main reasoning process and the Deep Web Explorer for in-depth information gathering. As outlined in Section 3.2, the main reasoning process operates in two distinct modes: Problem-Solving Mode and Report Generation Mode. Detailed instructions for each component and mode can be found in Section C.
A.2.1 Inference for Main Reasoning Process
this mode, the LRM receives a user's question and aims to find a direct answer. It primarily utilizes the Search Tool. When the model generates the [<lend search queryb> token, the generation process pauses. The system extracts the most recent query enclosed within assistant LLM formulates detailed
Problem-Solving Mode:
<ibeegnssearch_co search ] and <lend_ seadd_searh_geee -ueryis. Concurrently, search intent based on the query and the preceding reasoning chain. The system then uses a web crawler to fetch the content of the top-k (k=10 in experiments) retrieved web documents. The original query, the generated search intent, and the fetched web content are passed to the Deep Web Explorer. The Explorer processes this information and returns a refined summary relevant to the query and intent, enclosed within <ibeeiin search resultbb and <dend_ search resulti>.. The main LRM then resumes its reasoning with this new information. This cycle continues until the model generates an End-of-Sequence (EOS) token or reaches the maximum token limit. The final answer is extracted based predefined answer pattern (e.g., \boxed ANSWER) ANSWER: (ANSWER))
Report Generation Mode: This mode handles tasks requiring the generation of comprehensive research report. It receives the user's question along with a research plan (potentially generated by an assistant LLM). The LRM utilizes the Search Tool (invoking the Deep Web Explorer as in Problem-Solving Mode) and the three Report Writing Tools (Section Writing, Report Checking, Report Editing). Similar to the problem-solving mode, generation pauses whenever the model outputs
17

---

a closing tool token ( [<lend_ search. queryl)
selud_wwit__seeee
<lend edit articleb or the opening token for
checking <ibbgin check [atticlet ).
For search, the process mirrors the Problem-Solving Mode: invoke the Deep Web Explorer and insert results between aabegin_scarce resuttl and "kend_searee resultis
For section writing and report editing, the corresponding assistant LLM performs the action in the background. Since no direct output is inserted, the main LRM continues its reasoning immediately after generating the end token.
For report checking, the system generates the report outline, appends the <lend_ Ctend_check,_o aatticlls token, inserts this into the context, and resumes generation.
The model continues this loop of reasoning, searching, and drafting/editing until it generates an EOS token reaches the maximum token limit. The final, complete report is the accumulated result of the writing and editing actions performed by the assistant LLM throughout the process.
A.2.2 Inference for Deep Web Explorer
Deep Web Explorer:
The Deep Web Explorer operates as a sub-process invoked by the main
reasoning process when search is needed. Its goal is to deeply investigate the initial search results
provided by the main process. has access to two tools: direct Search Engine call and the Link & Button Clicking Tool. When the Explorer model generates generation pauses.
<lend search, queryls or
<lend click linkls
A search action direetly calls the search engine API (e.g., Bing) and returns the top- 10 results, typically including titles, URLs, and snippets (not the full page content). These results are inserted into the Explorer's reasoning context.
A link click action uses the crawler (Craw14AD) to fetch the full content of the specified URL. To manage potentially long web pages, an assistant LLM summarizes the fetched content based on generated "click intent", ensuring the inserted information is concise and relevant. The summarized
result is placed within <lbegin click resulti>
and <lend click resultis
The Explorer continues its internal reasoning process-analyzinng analyzing results, deciding whether to perform another search, clicking promising link-until gathers sufficient information relevant the initial query and search intent provided by the main reasoning process. It concludes by generating its findings within designated "Final Information" pattern, which is then passed back to the main
reasoning process as the output within <lbegin <ibegin_searrh resultis] and <iend search resulti> This process stops when the Explorer generates EOS token or reaches its maximum token limit.
B Datasets
B.1 Complex Reasoning Tasks
For testing, we use the following four widely used datasets:
GPQA [41]: Written by experts (holding or pursuing PhDs in relevant fields) in biology. physics, and chemistry, these "Google-proof" questions test the model's expertise and reasoning in these specific domains. It consists of multiple-choice questions (4 options each); we use the diamond set, totaling 198 questions.
GAIA [32]: benchmark dataset designed evaluate the capabilities of general artificial intel- ligence (AGI). Compared to GPQA, GAIA focuses more on generality, testing AI performance across diverse tasks such question answering, reasoning, code generation, and multimodal processing. As our model is text-based and cannot handle other modalities, we use the text-only validation subset, comprising 103 questions.
Web WalkerQA [56]: benchmark specifically designed to evaluate the web traversal capabilities of LLMs, simulating human ability to explore websites includes 680 challenging queries covering multilingual and multi-dommin web content. We use the test set for evaluation, totaling 680 questions.
via clicking to find required information. It
18

---

Humanity's Last Exam (HLE) [37]: Aims to assess LLMs' capabilities at the frontiers of human knowledge. It contains 2500 challenging discippiilll questions covering fields like mathematics, humanities, and natural sciences. The benchmark includes multiple-choice and short-answer questions, all with clear and easily verifiable solutions. Current state-of-the-ar models achieve less than 10% accuracy on HLE, highlighting its difficulty and effectiveness in measuring advanced academic capabilities. Due to its large test set, we randomly sample 500 text-only questions for testing.
For training, we use the following datasets. We sample approximately 3k data points from these datasets. First, perform direct generation using QwQ-32B and retain only the questions that cannot be answered correctly through direct generation. Then, for each of these questions, we generate three responses using WebThhineer32B-BBB the criteria outlined in Section 3.5.
filter for high-quality preference training data, following
SuperGPQA [46]: Designed to evaluate LLM knowledge and reasoning across 285 graduate-leve subjects. It contains 26,529 multiple-choice questions covering 72 fields, grouped into 13 broader disciplines, with strong emphasis on STEM subjects (77.2%).
WalkerAe (Silver) [56]] We the Silver of WebWalkerQA, containing 13.6k data points. The description is the same as above.
OpenThouuhts [47]: Contains 114,000 carefully crafted reasoning examples designed to train AI models for complex logical and mathematical reasoning tasks. Given that already use NuminaMath as training data, we prioritize sampling data related to STEM and puzzles.
Natural [65]] Aims to enhance I.IM reasoning capabilities through 2.8 million chal- lenging reasoning problems across multiple domains, including STEM (e.g., physics and computer science), economics, and social sciences.
NutinaMath [25]: Features problems ranging in difficulty from high school exercises to In- ternational Mathematical Olympiad questions, sourced from various online platforms and PDFs. NuminaMath provides high-quality, structured data enabling Al models learn and replicate expert-level mathematical reasoning.
B.2 Scientific Report Generation Tasks
For the scientific report generation task, we glaivea//reasoning-1-222 (Glaive) [11] for both training and testing. This dataset large-scale synthetic collection containing over 22 million general reasoning questions and responses generated using DeepScck- -aieeckk--R--iiieee
[14].
aims to cover diverse non-code/math topics such as social and natural sciences, education, and creative writing. We sample 1.5k questions for each iteration's preference data construction and 30 questions for testing.
C Instruction Templates
C.1 Instructions for WebThinker
Problem Solving Instruction for WebThinker
You are reasoning assistant with the ability to perform web searches help you answer the user's question accurately. You have special tools:
To perform search: write abegin_search queryl> your query here <lend seodd_searh__ooeee queryl>.
Then, the system will search and analyze relevant web pages, then provide you with helpful information in the format <lhegin_search, search_erhhh___ee eearch_result>> resulti> ...search results... <lend search_reeseeeee resultis.
You can repeat the search process multiple times necessary.
Once you have all the information you need, continue your reasoning.
Example:
Question: "Aliee David the voice of Lara Croft video game developed by which company?"
Assistant thinking steps:
II need to find out who voices Lara Croft in the video game.
Then, need to determine which company developed that video game.
Assistant:
19

---

David Lara Croft voiceclend_searccee _earcch_qurryt>>>>>>>>>>pp>_>>>> (System returss processed information from relevant web pages)
Assistant thinks: The search results indicate that Alice David the voice of Lara Croft specifie video game. Now, need to find out which company developed that game. Assistant:
<lbegin__searee set_sseerrr_eeeeeeeeeeeeeeeeeeee queryyiveee game developed by Alice David Lara searcclend_eerrch_quee (System returns processed information from relevant web pages) new information...
Assistant continues reasoning
Remember:
Use <lbegin_seeaee _eeacch_guerrlb>>>ee_eeepeeep to request a weh search and end with diend_search seurr_hhhhhhhrrbbb.....
When done searching, continue your reasoning.
Instruction for Deep Web Explorer
You are web explorer analyzing search results to find relevant information on given search query and search intent.
*Guuidelines:**
1. ** A**Alye the Searched Web Pages:**
Carefully review the content of each searched web page.
Identify factual information that relevant to **Current Search Query and can aid in reasoning
process for the original question.
2. **More Information Seeking:**
If the information is relevant the query, you could:
Search again: Eibegin_search
Leapinseeeeeuuurrrrreeeeeee querybanother search queryclend searcch__seee___eeeeeeeee quearhh
2. Access webpage content using: </hegin_cclie click linklliin URL<<led click linkl>
3. **Extract Relevant Information:
Return the relevant information from the "*Searched Web Pages that is relevant to the **Current Search Query **
**utpuut #ormat:**
Present the information beginning with **Final Information** as shown below.
**Final Information
[Relevant information]
**Inputs:**
* **Current Search Query:
searrch c
**Detailed Search Intent: **
srearch_interr
**Searched Web Pages:
[search_resulte
Now please analyze the weh pages and extract relevant information for the search query "seercc_querry" and the search intent.
Report Generation Instruction for WebThinker
You research assistant with the ability perform web searches write scientific research article. You have special tools:
To perform a search: write < seargiinsearhhh.cm querccyggguuglll >> your query here eed__search__ueer | Then, the system will search and analyze relevant web pages, then provide you with helpful information in the format <| beg' n_see rch_ resu it >search results< je__ssch__reess I>.
write section of the rescarch article: write begi wr wwwtteeeeeeeeee on section nameincontents
write< end
Then, the system will completely write the section based on your request and current gathered information.
To check the current article: write < bbbegi__check check article >system returns outline of all current written contentss send_check_artielell
To edit the article: write <l innedineeeeee aeriiitrrtiiiio icle y>your detailed goal instruc- tion< send_eiite lee__dittarriileee Marticclll
Then, the system will edit the article based on your goal and instruction and current gathered information.
Your task is research and write scientific article about:
{ (question)
Here is research plan guide your investigation:
(plan)
20

---

Please follow the research plan step by step:
1. Use web searches gather detailed information for each point
2. After each search, analyze the results and determine what additional information needed
section, request write section 4. Continue this process until the full article is complete
3. When you have sufficient information
5. Check the current article and edit sections needed to improve clarity and completeness Example:
queryl> Sreegin_search_gueee >fistt search query< lend__earrccc cu
>
of information from
searched
weh
Based these results, understand X, but still need to investigate Y...
|begin_see htt____eeere___uure_r_aaee =follow-up search query focusing e<ided_ss see weesseeerrrrrrreeee
[lbegin_search___c. www.earcchrrrsltl....eeee |SSmmaayy
of information
from
searched
>
Now have enough information to write the first section...
[ Introductioonaahiis
section
should
introduce
_end__rite_sect setteenssttinns
have written the introduction. Now I need to explore more information to write the next section
After writing the ahove sections, I need to check the current article to ensure the content is complete and accurate. bt___hcck_articleeee
returns
outline
of current
written
arti-
cle< lind_chhck_arttclllll Wait, realize that need to edit
<lbegin_edit eeii__ariict_ieeeeeeeaaeddddiaee >your edit instruction= len__edit, arriiiee _idiiiciiaattieeeeeeeeeeeeeeeeee
Assistant continues gathering information and writing sections until getting comprehensive information and finishing the entire article.
Remember:
Use beginnseare | rue__uerryyyyy >query<< send_seerrc__eee >> to get information from web searches
Use < lbbegin_wwe wwite_seccttteeeeeeeeereeeeiee__ icciooo ion| >section namelncontents to writes end write__ectioo >> to call
the system to write a section in the urticle
Use beeggin_hhcck_eeee
>outline of current article< wwweddd_hhhceeeeeeeeeeeee_eeeeee artccca check the
current written article
Use < <bbegineedi weeeedittrriice bedit instructions weedddddi__rttiioeoe to call the system to edit
and improve the article
You should strictly follow the above format call the functions.
Do propose methods design experiments, your task comprehensively research with web searches.
Do not omit any key points the article.
When you think the article is complete, directly output "I have finished my work." and stop.
Now begin your research and write the article about:
(question)
Instruction for Writing Section
You research paper writing assistant. Please write complete and comprehensive " (ssectionnnaaa section based following information.
**Potential helpful documents:
(reievant deee_evaat_doccmeeeeeee ddcuments)
**Original question:**
(question
**Previous thoughts:**
(previ outottustooooeo mas_thoughtss
**Outline of current written article:**
(current_A rticle]
**Name the next section write:**
## (sect oo _on_name)
**Your task is to comprehensively write the next section based on the following goal:** (task)
**Note:*
Write focused content that aligns with the above goal for this section.
-No need to mention citations or references.
Each paragraph should be comprehensive and well-developed to thoroughly explore the topic. Avoid very hrief paragraphs that lack sufficient detail and depth.
If possible, add markdown tables to present more complete structured information users.
Please provide the comprehensive content of the section in markdown format. ## (section, name!
21

---

Instruction for Editing Article
You are professional article editor. Please help me mudify article on following edit instruction:
**Edit instruction:**
(edit tednt_inst ruction))
**Current article:**
(artiele]
Please output the complete modificd article incorporating all requested changes.
*NNotc:**
Keep all original content that doesn't 't need moditication. (Do not just output the modified content, but output the entire modified article.)
Make edits specified the instructions.
Output format:
"ma mmarkowwn
Please provide the complete modified article in markdown format.
Instruction for Search Plan Generation
Please help create detailed plan to search over the web for solving the following question:
(query) }
Your task comprehensively gather relevant information thoroughly solve the user's question. Note:
No need to mention citations references.
Do not propose methods or design experiments, your task is research user's question with web searches.
comprehensive and thorough, do miss any relevant information.
No more than steps.
Please output the plan in numbered steps like:
(1)
etc.
Directly output the plan, do not include any other words.
Instruction for Search Intent Generation
Based on previous thoughts below, provide detailed intent latest search query. Previous thoughts:
(previous thoughts) a
Please provide the current search intent.
Instruction for Click Intent Generation
Based the previous thoughts below, provide the detailed intent of the latest click action.
Original question:
cquestion]
Previous thoughts:
Please provide the current click intent.
C.2
Task Instructions
The task instruction specifies the description of specific task and the answer output format for particular model. These instructions are directly concatenated after the method instructions (such as those for WebThinker, Search-ol, or RAG approaches).
22

---

C.2.1
Task Instruction for QwQ-based Models
Task Instruction for QwQ-based Models
Please the following question.
You should provide your final answer in the format
!hoxed/ YOURLANSWER).
Question:
(quest on)
C.2.2 Task Instruction for SeepSeek-RI-baae Models
Task Instruction for DeepSeek -R1 R1-based Models
Please answer the following question.
Provide final answer in the format ANSSEER: (YOUR ANSWER) Question: (quest ion]
C.3
Instructions for Evaluation
In this work, we use LLM--ssJJudes to evaluate both complex problem-solving and scientific report generation tasks. The specific instructions follows:
C.3.1 Evaluation Instruction for Problem-Solving Tasks
We use Dwee2.55-2B-Innttee [62] to evaluate all complex problem-solving tasks. output labeled "Correct" is considered correct, while "Incorrect" is considered wrong. In cases where the predicted answer cannot be accurately extracted, we directly use the last five lines of the model's output as the predicted answer. This approach helps reduce evaluation inaccuracies caused by formatting issues, case sensitivity, and similar factors, aligning with the official evaluation methods of benchmarks like WebWalkerQAA [56] and HLE [37].
Eraluation Instruction for Problem-Solvine Tasks
You are an evaluation assistant. Please determine if the predicted answer is equivalent the labeled answer. Question:
[question]
Labeled Answer:
(labeled answer)
Predicted Answer:
[pred_answer)
Are these answers equivalent? Please respond with "Correct" if they equivalent, "Incorrect" if they equivalent. include any other text.
C.3.2 Evaluation Instruction for Report Quality
For scientific report generation tasks, we use DeepSeek-RR166718 [14] and GPT-4o [19] respec- tively with the following instruction for evaluation, obtain scores, and take their average. Here, system_a) to (system_e) are sequentially the reports generated by the systems being eval- uated. We perform listwise evaluation to better compare the quality differences between reports generated by different systems. The input order is randomized rather than fixed to reduce bias caused by the model's context window. We did not employ human evaluation because the reports generated by each system have distinct features, making casy for humans to identify their originating system, which introduces significant bias. Model-basee evaluation is therefore more impartial.
23

---

Evaluation Instruction for Report Quality
Research Question:
4 Iquestion)
Please objectively evaluate the quality of research articles generated by systems A, B, C, D and E for this question, and provide scores of 10 for the following criteria:
Overall Comprehensiveness: The report should cover content comprehensively as possible
(2) Thoroughness of Discussion: Each section should discussed thoroughly, not just superficially
(3) Factuality: There should be minimal factual errors
(4) Coherence: The discussion should stay focused and relevant the topic
Notes:
A satisfactory performance deserves around points, with higher scores excellence lower scores deficiencies
You should not easily assign scores higher than 8 or lower than unless provide substantial reasoning. You do not need consider citations the articles Research article generated by system A:
[system_a]
Research article generated by system B:
[system_b]
Research article generated by system C:
(system_e]
Research article generated by system D:
[system_d]
Research article generated by system E:
(system_e)
Research Question:
(question)
Please objectively evaluate the quality of research articles generated by systems A, B, C, D and E for this question, and provide scores out of 10 for the following criteria:
Overall Comprehenssveness: The report should cover content comprehensively possible
Thoroughness of Discussion: Each section should discussed thoroughly, not just superficially
(3) Factuality: There should be minimal factual errors
(4) Coherence: The discussion should stay focused and relevant to the topic
Notes:
satisfactory performance deserves around points, higher scores excellence and lower deficiencies
You should not easily assign scores higher than 8 or lower than unless you provide substantial reasoning.
You do not need to consider citations in the articles
Please analyze each article and provide the final scores in the following JSON format:
joon
"System A":
"Overall Comprehensi.venes:: "Thoroughness of Discussion":
"Factuality": "Coherence":
System 3";
"Overall Comprehens! veness";
"Thoroughness of Discussion";
"Factuality";
"Coherence":
24

---

*System C": "Overall Comprehensivennss":
"Thoroughness of Discussion":
"Faetuality";
"Coherence";
System D":
"Overall Comprehens' veness"; venesss"""
"Thoroughness of Discussi on":
"Facruality":
"Coherence":
System E";
"Overall Comprehensi venness:
"Thoroughness of Discussion":
"Factuality"; "Coherence":
}
C.4 Additional Notes
All instructions presented above are provided user prompts rather than system prompts. When applying the WebThinker approach (detailed in Section C.1) to models based on QwQ-32B [48] or DeepSeek-R1 series [14], the corresponding task instruction (from Section C.2.1 or C.2.2) appended after the main WebThinker instruction. For other models, such as 11125--32B-Instrue [62], Qwen2.5-76 10l2255-72BIIntteee
[62], and GPT-4o [19], we include the Chain-of- -fhouughht prompt "You should think step by step solve it." before the question to explicitly encourage step-by-step reasoning prior to providing the final answer, following [55].
For detailed instructions regarding the baseline methods like Search-ol, Standard RAG, etc., you can refer the appendix of Search-ol [26] or visit our WebThinker GitHub repository: https: //github. com/ RUC-UNII NLP IR/WebThinke...
D
Case Study
The examples from Tables 4, 5, 6, 8, 9, and 10 demonstrate WebThinker's effectiveness across different capabilities including complex problem solving, deep web exploration, and scientific report generation.
D.1 Case Study for Complex Problem Solving
WebThinker shows strong reasoning capabilities in complex problem-solving scenarios:
In the GAIA dataset example (Table 4), WebThinker correctly identifies that Nemo from "Finding Nemo" is clownfish, and methodically searches for nonnative sightings in the USGS database, finding the only documented case Fred Howard Park, Florida. then determines the correct zip code (34689).
For the WebWalkerQA example (Table 5), WebThinker resolves ambiguity about the "evening after" the ACL 2023 awards ceremony by searching for relevant dates and determining that the social event occurred on the same day (July 11) from 7:00 PM to 10:30 PM, not the following day.
In the mathematical problem from Humanity's 's Last Exam (Table 6). WebThinker demonstrates sophisticated reasoning by finding the formula for simplicial volume of surfaces and their products, correctly calculating the simplicial volume of Ea x Σ17 as 11520.
D.2
Case Study for Deep Web Explorer
The Deep Web Explorer demonstrates effective information retrieval and integration:
25

---

For the ASH Annual Meeting deadlines (Table 7), the explorer not only finds the late-breaking abstract submission dates but also discovers the ancillary meeting deadline by clicking on a PDF link, compiling comprehensive deadline information with specific dates and requirements.
In the CLTS and Aedes mosquito control example (Table 8), the explorer clicks on repository link to find a case study integrating Community- Total Sanitation with mosquito control in Indonesia, Vietnam, and the Philippines, providing specific outcomes (40% reduction in breeding sites).
D.3 Case Study for Scientific Report Generation
WebThinker's report generation capability is illustrated in Tables and 10:
The model systematically follows a research plan for optimizing 3D printed lattice structures, conducting searches on FDM limitations, lattice optimization techniques, and material properties.
It methodically writes, checks, and edits sections, addressing duplicate content and ensuring coherent structure.
The final report (Table 10) presents comprehensive analysis of lattice optimization for robotic nodes, covering technical aspects from printing limitations to material selection, with practical applications and case studies.
These examples validate WebThinker's effectiveness across complex problem-solving. deep web exploration, and scientific report generation tasks.
E Summary of Contributions
In summary, the core contributions of this paper is follows:
1. We introduce WebThinker, deep research agent that autonomously search, deeply explore web pages, and draft research reports, all within its thinking process. Unlike traditional predefined workflow, WebThinker enables the LRM itself to perform actions its while thinking, achieving end-to-end task execution in a single generation.
2. We propose a Deep Web Explorer that empowers LRMs with web search and navigation capabili- ties to deeply gather, traverse, and extract high-quality information from the web.
3. We introduce an Autonomous Shink-Serrchaad-e -nd--af-nd strategy that enables real-time timetimee report writing during the thinking and searching process.
4. We develop RL-based training strategies that iteratively synthesize tool-usage preference data and apply online DPO training to enhance the LRM's 's tool utilization capabilities.
5. Extensive experiments demonstrate the effectiveness of WebThinker on complex reasoning tasks and scientific report generation tasks with both QwQ-based [48] and DeepSeek-R Rbbseedd [14] LRM backbones.
Broader Impact
This work introduces WebThinker, framework designed to significantly enhance the deep research capabilities of Large Reasoning Models (LRMs). By empowering L.Rss to autonomously explore the web, synthesize information from diverse sources, and generate comprehensive reports, WebThinker addresses critical limitations in tackling knowledge-intensiee real-world tasks. The potential broader impact of our research is considerable, offering a new paradigm for how complex information is accessed, processed, and utilized across various domains. This could accelerate scientific discovery, improve the quality of investigative journalism, and support more informed decision- makiing in sectors like finance and policy-making by providing powerful tools for in-depth research. Moreover, WebThinker has the potential to democratize access to advanced research capabilities, fostering innovation and learning.
The societal consequences of deploying such advanced research agents demand careful and proactive consideration. The ability autonomously gather, interpret, and synthesize vast quantities of online
26

---

information carries risks, including the potential for generating and disseminating sophisticated misin- formation, or inadvertently amplifying biases present in the training data and web sources. Concerns regarding data privacy, the verifiability of Al-generated research, and the potential displacement of human expertise in research-intensive roles must also be addressed. Therefore, that the advancement of technologies like WebThinker is coupled with the development of ethical guidelines, robust validation mechanisms, and a commitment to transparency to mitigate potential harms and ensure these tools serve the broader public good.
imperative
27

---

Table 4: A problem-solving example from main reasoning process of WebThinker on GAIA dataset, with special symbols used in the search queries and search results highlighted in blue box [..]] indicates omitted content.
Example # from GATA Dataset
Question:
I'm researching species that became invasive after people who kept them as pets released them. There's a certain species fish that was popularized as a pet by being the main character the movie Finding Nemo According to the USGS, where was this fish found nonnative species, before the year 2020? I need the answer formatted as the five-digit zip codes of the places the species was found, separated by commas there is more than one place.
Labeled Answer:
34689
Model Output by WebThinker
Okay, let's tackle this question step by step. The user wants to know the five-digit zip codes of places the US where the clownfish (the main character from Finding Nemo) has been found as nonnative species according to the USGS, before 2020.
First, I need to confirm which species is the main character in Finding Nemo. That's Nemo the clownfish, specifically Amphiprion o It's important to look for the correct species.
Next, the user mentioned the USGS database on nonindigenous aquatic species. need to check this database for records before 2020.
The task requires the answer formatted as five-digit zip codes. need find if the USGS data provides this level of detail or if I'll need to find the zip codes based on reported locations (like cities parks). Let me start by searching the USGS for nonnative sightings
"Amphiprion ocellaris* before 2020.
<lbegin search queryb USGS Amphiprion ocellaris nonnative sightings pre 2020 clend search querybs
<ibegin search resulti> The only documented nonnative sighting of "Amphiprion ocellaris* (clown anemonefish) the United States prior 2020, reported by the USGS Nonindigenous Aquatic Species (NAS) database, occurred in **Pinellas County, # specifically at #*Fred Howard Park** in the Gulf of Mexico region during ***018**, [...] The provided web pages do not explicitly mention the * *five-diggit zip code** associated with this location. [...] For precise zip code details, accessing the full NAS database record [...] may he
necessary. <dend__serch_ search_resultt>>>
Okay, the search result indicates only one sighting: Fred Howard Park in Pinellas County, Florida, in 2018. It explicitly states the zip code isn't provided in search results.
So, I need to find the zip code for Fred Howard Park. The result mentions Pinellas County, but the park might be in specific city within county. Let me search for the park's ' zip code.
<ibegin secein__earr___eee soochhggggr__a_ee Fred Howard Park St. Petersburg Florida zip sode/ searcherrrrree _uarcb_ <lbegin search resulti> The zip code for Fred Howard Park in Tarpon Springs, Florida (part of Pinellas County) #*34689** While the search query references St. Petersburg, the park is officially located in Tarpon Springs [..]] The address consistently listed [..]] is ** 1700 Sunset Drive, Tarpon Springs, FL 34689**
[..]]] [..][_ieed__seeee dclend_searhhheeeeeeeeeee_ee searrch_rearettt>>>eeeeee_ese
Ah, the park is in Tarpon Springs, FL, within Pinellas County, and the zip code is 34689.
The first search result mentioned this was the *only* documented sighting before 2020. To be absolutely sure, I'll run the first search query again to confirm no other locations were missed.
<lbegin_search sewrccearrrreeeeeeeeeeeeeeeeee search_guerrbb Amphiprion ocellaris USGS nonnative sightings before 2020 clend search_eeehhhh queryi>
<lhegin_scane search resultis The USGS Nonindigenous Aquatic Species (NAS) database indicates that * Amphiprion ocellaris* (clown anemonefish) has only one documented nonnative sighting in the United States prior to 2020. This sighting occurred in **Fred Howard *ark** **Pinellas County**, **Florida**, **2018** [..]] No other locations besides Fred Howard Park are listed [...] as having nonnative sightings of *A. ocellaris* before 2020. The five-digit zip code for Fred Howard Park, Tarpon Springs, Florida, is **34689**
[...]] [..]]<leend search resultis
Confirmed again. The only documented nonnative sighting before 2020 was at Fred Howard Park, zip code
34689. Since the user asked for zip codes separated by commas if there's 's more than onc, and there's only onc, the answer just this single zip code.
* Final *
34689
28

---

Table 5: A problem-solving example from main reasoning process of WebThinker on WebWalk- erQA dataset, with special symbols used in the search queries and search results highlighted in blue box [...] indicates omitted content.
Example from WebWalkerQA Dataset
Question:
What the specific schedule for the social event held the evening after the 2023 best paper awards ceremony?
Labeled Answer:
The social event was scheduled from 7:00 PM to 10:30 PM on July 11th.
Model Output by WebThinker
Okay, so the user is asking about the specific schedule of social event that took place the evening *after* the ACL 2023 best paper awards ceremony. First, need to figure out when the awards ceremony actually happened. Let search for that specifically.
<lbegin searrsserrhhhhc Lerrch__uerybb/// ACT 2023 hest paper awards ceremony date <lend search queryio
<lbegin search resulti> The ACI. 2023 best paper awards ceremony took place on **July 11, 2023** according
multiple sources: [...] Web Page [...] explicitly states that the awards were announced July 11, 2023.
[..] Web Page 5 [...] mentions the award presentation occurred on July 11, 2023 [...]. Web Page [...] confirms
the awards were announced on July 11, 2023. [...] Web Page [...] also indicates the awards were announced on July 11, 2023. [..]] the awards ceremony itself was held on the 11th* [...][ searc search result>>
Okay, the awards ceremony was definitively on July 11, 2023. The question asks for the social event on the evening *after* the ceremony, which would he the evening of July 12. Let me search for the social events schedule what was planned.
<ibegin searrlsserchhhgggee queryy ACL 2023 social events schedule searhh >
<ibegin search resulti> The ACL 2023 social events schedule includes the following relevant details: 1. SSoiil Event on July 11, 2023**;- **Time"** 7:00 PM 10:30 PM (EDT) * **Location**: Steam Whistle Brewing [..] - ***eescription**: This event occurred on the evening of July 11, immediately following the Best Paper Awards ceremony (which took place earlier that day at 17:00- [...] 2. **No Explicit Social Events on July 12, 2023**;---+ The evening of July 12, 2023, is not listed as having any official social events in the provided sources. [...] **Note**; The primary social event refereneed in the sources is on July not July 12. The Best Paper Awards ceremony was held on July 11, so the subsequent evening event likely refers to the Social Event on that same day. For July 12, only daytime sessions are documented. [...] searssseerssuulllleeeleeee Hmm, interesting. The awards were on July 11, and the search results show a social event on the evening of July 11, immediately following awards ceremony (7:00 PM 10:30 PM). However, the results explicitly state there were official social events scheduled for the evening of July 12, which is technically the evening *after* the July 11 ceremony.
Given the user's phrasing "evening after", but the lack of any scheduled event July 12 evening, it's highly probable they meant the social event that occurred on the *same evening* as the awards ceremony. which did happen *after the ceremony concluded earlier that day. The only documented social event connected to the awards ceremony fits this description. Therefore, the specific schedule requested corresponds the event on July 11.
The social event on the evening after the ACL 2023 best paper awards ceremony (held on July 11, 2023) was scheduled from 7:00 PM to 10:30 PM EDT Steam Whistle Brewing / Canada's 's Premium Beer.
29

---

Table 6: A problem-solving example from main reasoning process of WebThinker on Humanity's ss Last Exam dataset, with special symbols used in the search queries and search results highlighted in blue [...] indicates omitted content.
Example from Humanity's 's Last Exam Dataset
Question:
Let Eg denote oriented closed surface genus g. Compute the simplicial volume Ea x EIT.
Labcled Answer:
11520
Model Output by WebThinker
Okay. so I need compute the simplicial volume of Eg1 x E17, where Mg is oriented closed surface of genus g.
First, I should recall the simplicial volume of a single surface Eg. For g 2 2, the simplicial volume is | - 4(g 1). Let me quickly verify this.
cibeein_search_gge querryy simplicial volume of closed surface of genus g clend search search__qerrrb>>>>>>>
<ibegin_seane
searcherrchh.. resultlts The simplicial volume of closed surface of genus 2 is indeed 4(g 1). This result
derived from the relationship between simplicial volume and the Euler characteristic (x) of the surface. Specifically, for surface Dg of genus the Euler characteristic is x(_g) = 2 2y. The simplicial volume given by || 2 21x(E,)) leading to:
|E - 2/2
2gl 4(g 1).
[..]] For the product of two closed surfaces of genus h 2 2, the simplicial volume [ Ex has known bounds related to the product of individual volumes, hut the exact value requires specifie theorems. [...] <lend search. resultis
Sources confirm ||>;, | 4(g 1). [...][[iend
Okay, the formula | = 4(g - 1) for g 2 2 confirmed.
Now, the problem is the simplicial volume of the product Ex1 E17. The first search mentioned bounds but not an exact formula. need the specific rule for products of surfaces. Let me search for that.
<lbegin_search_ search_ queryybggerhhbaee simplicial volume of product surfaces ceensddedddddedeeeedeedd seeend_eearhhcc search__uerrbb <lbegin searcc_rrsssiieeeeeeeeee The simplicial volume of the product of two oriented closed surfaces of genus and l (each given by the formula:
E. Fxm = ||
||
Substituting the known simplicial volumes of individual surfaces (|اا 4(g 1) and ||>n 4(h 1)). this becomes:
3 m x Exll 4(g - 1) 4(h 1) = 24(g 1)(h - 1).
This result derived from Michelle Bucher's work, leveraging the proportionality principle for manifolds covered by HH2 x H². [...] <lend search resulti>
Ah, perfect. The formula is |> x En. =
|2 ||>>||
24(g 1)(h 1).
Now can substitute 31 and h 17:
|[ Σ17l|
(31 - 1) (17 -1)
|[ Σι7l| 24 30- 16
Calculating this:
24 x 30 720
720 x 16 = 11520
So the simplicial volume is 11520.
The simplicial volume of an oriented closed surface Eg of genus 2 2 is 4(g 1). For the product of two such surfaces E., Σ,, the simplicial volume is given by times the product of their individual simplicial volumes, which simplifies to 24(g - () - 1). Applying the formula for 31 and h = 17:
Eei x Σirl| = 24(31 (17 - 1) 24 30 16 720 16 11520.
Thus, the simplicial volume of Es1 x Σ17 is 11520
30

---

Table 7:
An example from the Deep Web Explorer of WebThinker on finding deadlines for the ASH 66th annual meeting, with special symbols used in the search queries, search results highlighted in blue hox and click actions/results highlighted in red hox [...] indicates omitted content. Due space limitations, the input "Searched Web Pages" in the table are summarized.
Example from Deep Web Explorer
Inputs Deep Web Explorer:
Current Search Query:
ASH 66th annual meeting deadlines ancillary meetings late-breaking abstracts
Detailed Search Intent:
The current search intent is to find the specific deadlines for requesting ancillary meetings and submitting late-brekking abstracts for the 66th ASH Annual Meeting. The search query aims gather this information from the official ASH wehsite or other reliable sources to ensure accuracy and relevance.
Searched Web Pages:
Provided 10 web page snippets from sources like Hematology.org and Confex.com. Pages 1, 5, and 7 (e.g.. "Call for Late Breaking Abstracts Hematology.org "Call for Abstracts confex.com consistently stated the late-breaking abstract submission period opens October 16, 2024, and closes October 28, 2024, 11:59 p.m. Pacific Time. Page ("ASH Annual Meeting Abstracts Hematology.org contained conflicting dates mentioning 2025, which were disregarded as likely incorrect for the 66th meeting. None of the provided snippets explicitly mentioned the deadline for ancillary meeting requests. [...]
Deep Web Explorer Processing Output
Okay, need to find deadlines for ancillary meetings and late-breaking abstracts for the 66th ASH Annual Meeting.
From the initial web pages, the late-breaking abstract dcadline is confirmed as October 16-28, 2024 (11:59 PM PT) from pages 1, 3, 5, and 7.
However, none of the initial pages provide the deadline for ancillary meetings. I need to search specifically for that.
<lbegin_search queryy ASH 66th annual meeting ancillary meetings proposal deadline <lead search, suarcl_ <ibegin Beegin_search_nee secarch_resultii Search results included a PDF link (Web Page 1) indicating the ancillary meeting dead- line is #oNoembeer 22, 2024, 11:59 p.m. PST** Another page (Weh Page 4) stated the request form becomes available **July 24, 2024** Other pages mentioned meeting dates or referred to the 2025 meeting.
[..]]] <lend_ seareeearrhhhee pearchh_ssult>>
The new search results provide the ancillary meeting deadline (Nov 22, 2024) and the date the request form opens (July 24, 2024). Web Page looks like the official guidelines PDF: Let me click that to confirm details.
meeting/2022_ ____ash__aaa_ ancillary_meetinggnae http://////mmmiieeeeemoooooo//i/
<lbegin click linkls httt:://wwww.hmmatogg.....
fiinaall.ddaa <lefd <lend click linkl>
cibegin_cllet click resulti> The clicked PDF (Ancillary Meeting Guidelines for the 66th ASH Annual Mecting)
confirmed: Request Form Availability: * "Wddnesday, July 24, 2024, 11:00 a.m. EDT** Submission Deadline: =*Friday, November 22, 2024, 11:59 p.m. PST** A non-refundable processing fee applies, tiered by event type (e.g., $200 for Member/Non-Ptoot
$250 for Advisory Board, $500 for Investor Meeting). Space is limited, first-come, first-served, using designated hotels (no convention center space). Eligibility: Organizer must he affiliated with ASH (cxhibitor, sponsor, member, etc.). Contact for questions: asheventre- quests @ spargoinc.com [...][<lend <lend click resulti>
Okay. now have confirmed information for both late-breaking abstracts (from initial search) and ancillary meetings (from the additional search and click). I can now consolidate this information. ## Final Information
After reviewing the official ASH resources and related search results, the critical deadlines for the #*66th
ASH Annual * are summarized below:
### **Lacc-Braakie Breaking Abstracts Deadlines** **Open Date**; October 16, 2024 **Close Date**; October
28, 2024, at #*11:59 p.m. Pacific Time*s e **Dttiis***; Requires justification for late submission.
Submission fee: **$125 USD** (non-refundable). Max **six abstracts*** accepted for presentation
**Dec 10, 2024**
#н# ** Ancillary Meetings * ++Form *vailable* July 24, 2024, #* #*11:00 a.m. EDT** **Submission Due**; November 22, 2024, at #1:55 p.m. PST*s **Rquurremmnts Organizer must be afH-affiliated Tiered fees apply ($200/$250/5500 based on type). Use approved hotels (no convention center space), first-come, first-served. Must not conflict with official ASH programming.
31

---

Table 8: An example from the Deep Web Explorer of WebThinker
studies of CLTS and Aedes mosquito control, with special symbols used in the search queries, search results highlighted in blue box and click actions/results highlighted in red box [...] indicates omitted content. Due to space limitations, the input "Searched Web Pages" in the table are summarized.
Example from Deep Web Explorer
Inputs to Deep Web Explorer:
Current Search Query:
case studies of CLTS and Aedes mosquito control in rural Southeast Asia
Detailed Search Intent:
Find case studies integrating Community-- Total Sanitation (CLTS) with Aedes mosquito control in rural Southeast Asia. The goal to identify real-world implementations combining these programs address waste management and water storage challenges [...] related to dengue fever.
Searched Web Pages:
Provided 10 weh page snippets from sources like PuhMed, NCBI, Parasites & Vectors, ScienceDirect and UNAIR Repository. Several pages (1-4) focused Aedes mosquito control methods including insecticide resistance and sterile insect techniques. Web Page 6 mentioned "government commumiiy partnerships for vector control Southeast Asia. Web Page 10 discussed Aedes breeding in water containers in rural areas, though in Cameroon rather than Southeast Asia. None of the snippet information explicitly connected CLTS Aedes control. [...]
Deep Web Explorer Processing Output
Okay, I need to find case studies connecting CLTS and Aedes mosquito control in Southeast Asia. From the initial web pages, I'm not seeing explicit mentions of Community- Total Sanitation (CLTS) being integrated with Aedes mosquito control measures. Web Page 6 mentions government-commnnig partnerships for vector control, which is related to community engagement but not specifically CLTS. Let me check the UNAIR repository article for more details.
<lbegin click linkls http://epoositor.uuuaii id/ Arepe///Ariikk-fffcccieeesss pdff clend click. linkl>
<lbegin_cliae clice_llike_eeeeeeeeeeeeee resulti> The clicked PDF (from UNAIR Repository) detailed a study integrating Community- Total Sanitation (CLTS) with Aedes mosquito control in rural Southeast Asia (Indonesia, Vietnam, Philippines). The study combined CLTS implementation (community mobilization, latrine construction) with vector control (larvicide distribution, education). Results showed a 40% lower prevalence of Aedes breeding sites in villages with the integrated approach compared to controls, demonstrating improved sanitation and reduced mosquito breeding through community engagement. [...] <lendd clend_ resultis
The UNAIR article provides the exact case study information we're looking for, explicitly documenting CLTS integration with Acdes mosquito control in rural Southeast Asia.
I should also search for additional case studies to provide a more comprehensive answer.
<lbegin_search_ queryb CLTS and dengue prevention in rural Indonesia <lend search_ qearcd__ueryl>>
<ibegin_ssent sewrccarrrrrrrr resuulllessts>>> No additional clear case studies of CLTS explicitly integrated with Aedes control were found in the search results. Most sources discuss either CLTS for sanitation improvements or Aedes con- trol measures separately, but not their intentional integration. The previously found UNAIR Repository article remains the most relevant source documenting this specific integrated approach in Southcast Asia. [...]] <lend_ search search_results
The additional scarch did not yield further explicit case studies beyond the UNAIR Repository article.
## Final Information
Based on the analysis of web pages and further exploration, one key case study of CLTS integration with Aedes mosquito control in rural Southeast Asia was identified:
**Case Study from UNAIR Repository:** Locationn: Rural villages in Indonesia, Vietnam, and the Philippines- ** **Intervention ** CLTS was combined with Aedes control measures including: Community mobilization and latrine construction Larvicide distribution for water container treatment Education on identifying and eliminating breeding sites Proper waste disposal and water storage practices **Outcomes:t** 40% reduction in Aedes breeding sites in CLTS-integrated villages Significant improvements in sanitation facilities Enhanced community engagement and sustained behavior change
While Web Page 6 mentioned government-communig partnerships in vector control, and Web Page 10 highlighted breeding site management (though Cameroon), neither explicitly discussed CLTS integration with Aedes control.
The findings suggest that CLTS can be effectively combined with mosquito control by addressing the environ- mental conditions that enable mosquito breeding, particularly through improved waste management and water storage practices that reduce standing water sources.
32

---

Table 9: A report generation example from the main reasoning process of WebThinker the Glaive dataset, concerning the optimization of 3D printed lattice structures for robotics. Special symbols highlight search queries/results box and writing/hhecking/ldiinng actions green [...] indicates omitted content.
Research Question
How can I optimize the lattice structure a 3D printed robotic node for maximum trrength-to-weigee ratio while minimizing the amount of support material required, considering the limitations of FDM printing and the need for integrated connectors for inter-robot communication and power transfer in a swarm robotics application?
Research Plan
(1) Search for "FDM 3D printing limitations" to understand the constraints of the printing process.
(2) Look up "lattice structure optimization for 3D printing" to find general strategies and software tools.
[..]
(7) Look into "material properties for 3D printing lattice structures" for best materials.
(8) Find case studies on "3D printed robotic components" for real-world examples.
Model Output by WebThinker
Okay, so need to help write a scientific article about optimizing a 3D printed robotic node's lattice structure
[..] Let me start by following the research plan step by step.
<lbegin search_ queryb FDM 3D printing limitations <lend search_ search__uurrybb
<ibegin_searce resultii The limitations of FDM (Fused Deposition Modeling) 3D printing technology relevant
to optimizing the lattice structure of robotic node include:
1. **Print Orientation FDM prints layer-by-layer, so orientation affects structural integrity and support
requirements [...]
2. ** *Overhang Angles"";
Overhangs exceeding shallow angles (c.g.. 45%) risk sagging
[...]] <lend_ search search_resultis
[... multiple additional searches performed for each research plan point....
Now I need to start writing the sections based on the gathered info. Let me start with the Introduction section
first.
<lbegin write sectioni> Introduction
The optimization of lattice structures in 3D printed robotic nodes crucial for achieving high strength- to to-weight ratios and minimizing support material, particularly in swarm robotics applications. Fused De- position Modeling (FDM) printing presents unique challenges such as piientation-dppnnde limited overhang angles, and material constraints, which must be addressed to ensure structural integrity
anisotropy,
[..]] <lend write sectioul> [... continues writing each section according to research plan ...]
<lbegiinsearchh querryb material properties for 3D printing lattice structures cleud search search__ueryl>>
Becriisssrrr___ree PPAA offers moderate rigidity but struggles with interlayer adhesion. TPEE provides superior energy absorption (3260 kJ/m") compared to PLA (200.90 kJ/m"). TPU excels in producing flexible lattice structures, with honeycomb architectures demonstrating 30% higher rigidity than gyroid structures
[...]] search___ssshhh_ee resultis
<ibegin_wwed write sectioni> Material Selection for Enhanced Performance
Selecting the right material is pivotal for optimizing lattice structures. PLA is cost-effective but briutle, requiring careful design to prevent delamination. TPU offers flexibility and superior energy absorption,
making ideal for connectors and flexible nodes [...] <lend write secttesscctions]
<ibegin check arrtieeet # Optimizing Lattice Structures in 3D Printed Robotic Nodes for Swarm Robotics: Balancing Strength-to-Wert
Ratio and Minimizing Support Material ## Introduction ## FDM 3D Printing
Limitations Impacting Lattice Design ### Print Orientation Effects [..]] Ceend_checc_rreeeeeeeeeeeeeeeeee artticle
[... notices duplicate sections ...]
<lbegin edit articlels Remove duplicate "Real-Worke Applications and Case Studies" section and ensure each numbered section corresponds correctly to the research plan's 's eight points. Also, reorder sections to follow
the research plan sequence precisely. <lend edit articlels
[... notices missing conclusion after editing... ...]
<lbegin_wwit writer siriteeseetion>>e>eee (Conclusion
In conclusion, optimizing the lattice structure of 3D printed robotic nodes [...] <lend. wwrtewrriteeeee sectionl
<ibegiinedi edit eetiieeetdiedtitltaaaaaaa ] Ensure all sections maintain consistent formatting and cross-reference related concepts
throughout the article. Add transition sentences between major sections to improve flow. dow.<lend <lend_ edit articles
I have finished my work.
33

---

Table 10: An example of report generation by WebThinker: summary outlining the structure and key sections of the final report on optimizing 3D printed lattice structures for swarm robotics.
Final Report Summary
Optimizing Lattice Structures 3D Printed Robotic Nodes for Swarm Robotics: Balancing Strength- to-Weight Ratio and Minimizing Support Material
Introduction
Swarm robotics draws inspiration from social insects. The optimization of lattice structures in 3D printed robotic nodes crucial for achicving high strength-to to-weight ratios and minimizing support material, particu- larly in swarm robotics applications. Fused Deposition Modeling (FDM) printing presents unique challenges [..]
FDM 3D Printing Limitations Impacting Lattice Design
Fused Deposition Modeling (FDM) 3D printing is widely adopted but has inherent limitations affecting lattice design, including print orientation effects, overhang limitations, material property restrictions, and layer adhesion challenges [...]
Lattice Structure Optimization Techniques
Optimizing lattice structures involves balancing strength, weight, and support material reduction. Key approaches include oulti-objective frameworks (e.g.. combining Design Experiments (DOE) and Genetic Algorithms (GA)), selecting prominent lattice patterns (e.g., Hexagon, Diamond/Octet, BCC/SC, Fluorite), using appropriate software tools, optimizing printing parameters, minimizing support material, and integrating connectors [...]
Key Factors Influencing Strength-to Weight Ratio
Achieving high strength-to-weie ratio is essential. This involves careful material selection, lattice architec- ture design, applying optimization techniques, performing testing and validation, and balancing competing parameters [...]
Reducing Support Material Through Design and Process Adjustments
Minimizing support material is essential for cost, efficiency, and quality. Strategies include strategic design modifications, optimizing printing orientation, using advanced support types, manual support placement, stress-guided topology optimization, and integrating connectors [...]
Integrating Connectors for Inter-Robot Communication and Power Transfer
In swarm robotics, communication and power transfer between nodes essential. This requires integrating suitable mechanical and electrical connectors (c.g., Box Joint, Dovetail, Snap Fits), considering material selection, integration techniques, and specific design considerations [...]
Design Considerations Specific to Swarm Robotics
Swarm robotics requires coordination through decentralized interactions. Design must account for communi- cation systems, energy efficiency, mobility, structural intogrity, and connector integration [...]
Material Selection for Enhanced Performance
Selecting appropriate materials (e.g., PLA, TPU, TPEE) is crucial. Considerations include mechanical properties, suitability for lattice structures, potential closed-cell topologies hybrid strategies, and specific material parameters and printing techniques [...]
Real- World Applications and Case Studies
Practical applications demonstrate the effectiveness of these principles, including custom protective covers, modular structures using spatial lattice printing, rapid tooling replacement, and lattice-base nodes for swarm systems [...]
Conclusion
Optimizing lattice structures for 3D printed swarm robots requires holistic approach addressing FDM limitations, materials, and design. Selecting appropriate materials and lattice patterns enables high strength-to- weight ratios while minimizing support. Connector integration enhances functionality. Careful consideration of parameters, orientation, and materials leads to robust, lightweight nodes for coordinated tasks, as shown in case studies [...] Future work should focus further optimization and exploring new materials and technologies.
34

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The abstract and introduction accurately describe the paper's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
The limitations and directions for future improvements are discussed in the conclusion section.
Justification:
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model sell-specfiiatiinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-tex system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
35

---

Answer:
[NA]
Justification:
This paper does not involve theoretical results or proofs.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proof's can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Question:
Answer: [Yes]
Justification: Detailed information for all experimental results is provided in the main text and appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, descríbing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e-g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing verifying the results.
5. Open access to data and code
36

---

Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: We provide complete code and model checkpoints allow readers to reproduce our experimental results.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please the NeurIPS code and data submission guidelines (https: //ntts::/nippi http//iiss...../p/////////.ee cc/
public/ guides Code Submi s Moooolicy))) for more details.
While encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See NeurlPS code and data submission guidelines (httpat /nniss. cc/ cccppblicccccccccccccccccccccccl ptwplc/gpppiippeepppc.pe cttic///iiieeee ides/ CooeeSumm ssi onPol (ioolicy) for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended the paper) is recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: The training and testing details are described in detail in the dataset description section of the main text and in the appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification: Due to the high computational cost of our experiments, we did not report error bars.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emor bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
37

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not show in tables figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification:
The computational resources for the experiments are described in detail in the implementation details section.
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (c.g., preliminary or failed experiments that didn't 't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics https: //eurirrpppp wttpccppppcccccccccc.ccccccccccc httellii//tttitttttieuae caGui del iine?
Answer: [Yes]
Justification:
We have followed the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (c.g.. if there special consid- eration due to laws or regulations in their jurisdiction..
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: Potential positive and negative socictal impacts are discussed in section F.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
38

---

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
This work does not pose such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g.. code, data, models), used in the paper, properly credited and the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All code, models, and datasets used are properly cited in the paper.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URI.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
39

---

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, papersw www..ww. thoode com/ datasets s has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. If this information is not available online, the authors encouraged to reach out the asset's s creators.
13. New assets
Question: Are assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: We provide anonymous GitHub repository: https://github..om RUC NLPIR/ WWLPIR/WebTiiniee/ WWw/hhihkher
Guidelines:
The answer NA means that the paper does not release assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
This paper does not involve crowdsoureing or research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
This paper does not involve crowdsourcing or research with human subjects.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
40

---

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology. scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification:
We did use LLMs for research.
Guidelines:
The answer NA means that the method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy (https: //n/urrippp cc/ Conferences/ / 2025/ LLM) for what should should not described.
41


Reviews:
[{"review_id": "BI3dNijxDn", "reviewer": "Reviewer_6BBj", "summary": "The work proposes WebThinker, which augments large reasoning models with autonomous web exploration and report generation functiom. The models can dynamically search the web, navigate pages, and draft or revise content during the reasoning process. The difference with RAG is that, WebThinker interleaves reasoning, searching, and writing in an integrated loop. It introduces a Deep Web Explorer and specialized drafting tools, and further leverages RL to optimize tool usage based on preference feedback. The system achieves better performance on challenging benchmarks, including GPQA and Humanity’s Last Exam, outperforming both open-source and proprietary baselines.", "questions": "See Weakness", "limitations": "The authors point out several limitations in the conclusion section:\n- WebThinker cannot process multimodal information such as images and videos\n- It currently supports only a limited set of tools\n- Extending WebThinker to support GUI-based web exploration would enable it to handle more complex and real-world interactive tasks.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n- The presentaion and flow is clear\n- The experiments are pretty comprehensive\n\n**Weaknesses**:\n- **Fair Comparison**: I am not sure if the comparison is entirely fair between WebThinker and baselines. In Figure 2, there're multiple traditional LLMs decide whether more search is needed. Moreover, with multiple retrieved content blocks accumulated in the context, the cost or input size can be huge, which is also identified by authors: `Justification: Due to the high computational cost of our experiments, we did not report error bars.`. Simultaneously, the method needs to maintain a written report. Can you provide more justification into the cost in the inference time, and how does this compared to other models?\n- **Clarity**: The authors say \"The objective is to generate a comprehensive answer solution for a given task query q\" (Line 96). What does \"comprehensive\" mean here specifically? Why is this important in all cases?\n- **Transparency:** Following Weaknesses point 1, how redundant is the search process? Would it be possible that the agent decides to keep searching while this is actually not necesary? While I see the author penalize such redundancy with the DPO where shorter trajectories are preferred, I imagine it might still happen.\n- **Training details**: It is not clear how long and how many trajectories does the model need to be effective? In Section 3.5, what are the percentages of different types of DPO pairs?\n- **(Minor) Novelty**: One has to admit that many components in WebThinker are not new and explored in the previous works, but in general this looks like a useful approach. Therefore the issue is a minor concern to me.", "was_revised": true, "final_justification": "I reduced my confidence by 1, as I am not sure about the novelty claim. The authors said \"However, prior to early this year, most existing RAG methods did indeed rely on predefined workflows.\", I don't think this is a good reason for one to say they are novel and the novelty exists before a specific time frame. Many industry solutions and agentic framework has already incorporated similar techniques.", "rebuttal": "Thank you very much for your detailed review. We have addressed your concerns point by point and further supported our responses with comprehensive quantitative analyses:\n\n> ### Q1: Fair Comparison: Concerns about computational costs and inference time comparisons between WebThinker and baselines\n\nThank you for raising this important point. Computational overhead and efficiency are indeed common challenges faced by all current Deep Research methods.\n\nWebThinker is designed to strike a balance between performance gains and efficiency, which is particularly challenging when incorporating large volumes of web information. To address this, we introduced a deep web explorer module that intelligently understands and integrates external web content, returning distilled and relevant information rather than indiscriminately inserting entire web pages into the reasoning chain. Directly including all retrieved content would disrupt reasoning coherence and result in excessively long contexts, ultimately degrading both performance and efficiency. This design philosophy also extends to report generation, where we employ additional LLMs for writing, checking, and editing report content, instead of embedding the entire report into the main reasoning chain.\n\nTo empirically validate this approach, we conducted experiments comparing the inference time of different methods. All methods utilized 32B models as the backbone and were evaluated on the GAIA benchmark using a single node equipped with 8 NVIDIA H800 80G GPUs. The results are summarized below:\n\n| Method         | Avg. Inference Time (s) | Avg. Retrievals | Avg. Seq. Length (tokens) | Accuracy (%) |\n|----------------|------------------------|-----------------|---------------------------|--------------|\n| Direct Gen     | 11.1                   | 0               | 5531                      | 22.3         |\n| Standard RAG   | 23.2                   | 1.0             | 9246                      | 32.0         |\n| Iterative RAG  | 34.4                   | 2.3             | 16734                     | 35.0         |\n| WebThinker     | 40.8                   | 4.9             | 8732 (main)+17374 (aux)   | 48.5         |\n\nAs shown, WebThinker achieves significantly higher answer accuracy (48.5%) while keeping inference time and sequence length within the same order of magnitude as traditional RAG methods. Notably, the main reasoning chain length in WebThinker is even shorter than that of Standard RAG and Iterative RAG, which directly stack retrieved content. In practical deployment, we can further reduce inference time and computational costs by employing smaller auxiliary LLMs, since these only handle relatively simple sub-tasks.\n\n\n> ### Q2: Clarity: The authors say \"The objective is to generate a comprehensive answer solution for a given task query q\" (Line 96). What does \"comprehensive\" mean here specifically? Why is this important in all cases?\n\nIn this context, \"comprehensive\" encompasses both the entire reasoning process and the final answer or report. We believe that, whether in problem-solving or report generation scenarios, both the reasoning process and the final output are essential, which is why we use this term.\n\nHowever, we acknowledge that this expression could be clearer. A more precise formulation would be: ```The objective is to generate a comprehensive reasoning process and answer for a given task query q, guided by an instruction I```, which better conveys our focus on both the completeness of the reasoning process and the thoroughness of the final answer.\n\n> ### Q3: Transparency: Concerns about redundancy in the search process and potential for unnecessary searches\n\nGood question. Indeed, redundant search calls by the Search Agent can lead to unnecessary computational costs, making efficiency a critical concern in this field. To address this, WebThinker mitigates redundancy at the training data level by constructing preference pairs: reasoning trajectories with fewer and more accurate search calls are treated as positive examples, while those with incorrect or excessive search calls are treated as negative examples.\n\nTo more concretely illustrate the impact of this design on search efficiency, we conducted the following ablation experiments:\n\n| Model Variant | Avg. Search Count | Effective Search Ratio (%) | WebWalkerQA Accuracy (%) |\n|---------------|------------------|---------------------------|-------------------------|\n| WebThinker (no DPO) | 7.3 | 42.5 | 41.7 |\n| WebThinker (correctness DPO only) | 6.8 | 45.3 | 45.2 |\n| WebThinker (full DPO) | 4.9 | 68.7 | 46.5 |\n\nAs shown, the full DPO training strategy significantly reduces the average number of search calls (from 7.3 to 4.9, a 32.9% reduction) and substantially increases the effective search ratio (from 42.5% to 68.7%). Here, the \"effective search ratio\" refers to the proportion of searches along the labeled path relative to the total number of searches, serving as a measure of the model’s search efficiency. These results indicate that our training strategy effectively guides the model to search more selectively, thereby reducing redundant operations.\n\nImportantly, this improvement in search efficiency does not come at the expense of performance. On the contrary, accuracy on the WebWalkerQA benchmark increases by 3.8 percentage points. This demonstrates that our approach not only enhances efficiency but also improves the model’s overall reasoning ability, enabling it to better determine when additional information is needed and what content should be retrieved.\n\n> ### Q4: Training details: It is not clear how long and how many trajectories does the model need to be effective? In Section 3.5, what are the percentages of different types of DPO pairs?\n\nBelow are the detailed quantities and proportions of each data type in our training set:\n\n| Dataset | Training Pairs | Percentage |\n|---------|---------------|------------|\n| SuperGPQA | 2,973 | 32.0% |\n| WebWalkerQA | 2,891 | 31.1% |\n| OpenThoughts | 1,637 | 17.6% |\n| NaturalReasoning | 1,108 | 11.9% |\n| NuminaMath | 842 | 9.1% |\n| **Total** | **9,451** | 100% |\n\nAs shown, SuperGPQA and WebWalkerQA contribute 2,973 and 2,891 data points, respectively, while OpenThoughts, NaturalReasoning, and NuminaMath provide 1,637, 1,108, and 842 data points, for a total of 9,451. These cover a wide range of scientific, knowledge-intensive, mathematical, logical reasoning, and open-ended tasks, aiming to encompass as many real-world task types as possible.\n\nTo investigate the impact of training duration and data volume on model effectiveness, we trained the model on a single node with 8 NVIDIA H800 80G GPUs using the above 9,451 data points (batch size 32), and evaluated performance on the GAIA and WebWalkerQA datasets every 200 steps. The table below shows accuracy progression with training steps:\n\n| Step | GPQA | GAIA | WebWalkerQA | HLE  |\n|------|------|------|-------------|------|\n| 0    | 68.7 | 44.7 | 41.9        | 13.0 |\n| 200  | 69.1 | 46.5 | 44.1        | 14.4 |\n| 400  | 69.1 | 47.6 | 44.3        | 15.2 |\n| 600  | 70.2 | 48.5 | 45.4        | 15.8 |\n| 800  | 71.2 | 48.5 | 46.5        | 15.8 |\n\nThe results indicate that as training progresses, the model’s accuracy on both datasets steadily improves and converges around 800 steps, suggesting that the current data volume and training epochs are sufficient to fully realize the model’s potential. Notably, even after just 200 steps, the model exhibits significant performance gains, demonstrating that the current training data is already highly effective.\n\n> ### Q5: (Minor) Novelty: One has to admit that many components in WebThinker are not new and explored in the previous works, but in general this looks like a useful approach. Therefore the issue is a minor concern to me.\n\nThank you for raising this point. The primary goal of WebThinker is to equip large reasoning models with deep research capabilities, with web exploration and report writing being the two most critical functionalities. While prior work has addressed these tasks, most existing methods rely on predefined workflows, making it difficult to seamlessly integrate with extended reasoning processes.\n\nWebThinker addresses this by abstracting web exploration, report writing, and editing as \"research tools,\" enabling the reasoning model to autonomously invoke these tools within a single, end-to-end agentic reasoning process to accomplish complex tasks. This approach—especially in the context of report generation and tool integration—remains underexplored in the current literature.\n\nWe hope the above clarifications and experimental results address your questions. Thank you again for your valuable feedback.\n\nBest regards,\nAuthors of WebThinker"}, {"review_id": "oytDMY2jAR", "reviewer": "Reviewer_sk4D", "summary": "This paper introduces a novel framework named WebThinker, designed to address the limitations of Large Reasoning Models (LRMs) that stem from their reliance on static, internal knowledge when confronted with complex, knowledge-intensive tasks. WebThinker transforms an LRM into an autonomous agent capable of conducting deep research.", "questions": "See weaknesses above.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The WebThinker framework is a significant advancement over existing Retrieval-Augmented Generation (RAG) paradigms. It evolves from the conventional \"retrieve-then-generate\" model to a dynamic and autonomous \"think-explore-generate\" loop. \n2. The choice of evaluation benchmarks is excellent. GPQA, GAIA, HLE, and WebWalkerQA are recognized in the community as demanding datasets for assessing advanced reasoning and information-processing capabilities.\n\nWeaknesses:\n1. WebThinker is a fairly complex framework, and the paper does not discuss the framework’s inference lantency, computational cost, or efficiency comparison with the standard RAG approach.\n2. The performance of the system is highly dependent on external tools, such as the Bing search engine and the Crawl4AI crawler. The quality of the results returned by the search engine, the stability and coverage of the web crawling tools, will directly affect the final performance of WebThinker. The paper does not provide ablation experiments in this regard.\n3. Webthinker is limited to processing static web pages based on text and links, which reduces its ability to be used in real-world scenarios.", "was_revised": true, "final_justification": "Regarding my concern about efficiency and computational cost (W1), the authors have demonstrated through new comparative experiments, particularly the one using a smaller auxiliary model, that their framework can maintain high performance while keeping latency and cost within an acceptable range.\n\nRegarding my concern about the dependency on external tools (W2), the authors have verified through a new ablation study that their system is robust to different search engines and crawlers, with limited impact on performance.\n\nRegarding my concern about the limitation to static web pages (W3), the authors have also provided a reasonable justification for their research scope.\n\nGiven that the authors' response and new data have successfully addressed my primary concerns, I am raising my score for this paper.", "rebuttal": "Thank you very much for your valuable and detailed review. We sincerely appreciate your constructive feedback and the opportunity to further clarify and strengthen our work. Please find our point-by-point responses below.\n\n> ### W1: WebThinker is a fairly complex framework, and the paper does not discuss the framework's inference lantency, computational cost, or efficiency comparison with the standard RAG approach.  \n\nThank you for this important question. Computational overhead and efficiency issues are common pain points for all current Deep Research related methods.\n\nWebThinker's design aims to balance performance improvement and efficiency, which is a challenging issue when leveraging large amounts of web information. Specifically, we designed an additional deep web explorer module to understand and integrate external web page information, returning refined helpful information rather than directly inserting all web pages into the reasoning chain. This would damage reasoning coherence and produce extremely long contexts, leading to poor performance and efficiency. This design philosophy is also applied to report generation, which utilizes additional LLMs for writing, checking, and editing report content instead of directly inserting the complete report into the reasoning chain.\n\nTo verify this point, we conducted experiments to compare the inference time consumption of different methods. All methods use 32B models as backbone, tested on the GAIA benchmark using one node of 8 NVIDIA H800 80G GPUs. The results are as follows:\n\n| Method | Avg. Inference Time (s) | Avg. Retrievals | Avg. Seq. Length (tokens) | Accuracy (%) |\n|--------|------------------------|-----------------|---------------------------|--------------|\n| Direct Generation | 11.1 | 0 | 5531 | 22.3 |\n| Standard RAG | 23.2 | 1.0 | 9246 | 32.0 |\n| Iterative RAG | 34.4 | 2.3 | 16734 | 35.0 |\n| WebThinker | 40.8 | 4.9 | 8732 (main) + 17374 (aux) | 48.5 |\n\nFrom the experimental results, it can be seen that WebThinker achieves higher answer accuracy (48.5%) while maintaining inference time and sequence length at the same order of magnitude as traditional RAG methods. Notably, WebThinker's main reasoning chain length is even lower than Standard RAG and Iterative RAG methods that directly stack retrieved content. In actual deployment, we can completely adopt smaller auxiliary LLMs to further reduce inference time and computational costs, as auxiliary LLMs only handle simple sub-tasks.\n\n\n> ### W2: The performance of the system is highly dependent on external tools, such as the Bing search engine and the Crawl4AI crawler. The quality of the results returned by the search engine, the stability and coverage of the web crawling tools, will directly affect the final performance of WebThinker. The paper does not provide ablation experiments in this regard.  \n\nIndeed, WebThinker's performance is affected by the type and quality of search engines, as well as the success rate of web crawling, but this is also a problem that all web search-based agents must face. Our experiments use Bing Search API and Crawl4AI crawler, and in actual deployment, we can completely use other search engines and web crawling methods.\n\nTo verify the impact of search engines and crawler tools on WebThinker's performance, we conducted the following ablation experiments:\n\nWe systematically evaluated WebThinker's performance on four benchmark datasets (GPQA, GAIA, WebWalkerQA, and HLE) under different search engines (Bing, Google Serper API) and whether to integrate Crawl4AI crawler tools. The experimental setup is as follows:\n\n- **Bing**: Use Bing Search API for web retrieval.\n- **Google Serper**: Use Google Serper API for web retrieval.\n- **Crawl4AI**: Based on retrieval results, further utilize Crawl4AI tools to crawl web page content, improving information coverage.\n- **Request**: Only use the requests library to obtain web page content.\n\nThe table below summarizes the accuracy (Accuracy, %) of WebThinker on each dataset under different configurations:\n\n| Configuration | GPQA | GAIA | WebWalkerQA | HLE |\n|---------------|------|------|-------------|-----|\n| Bing + Crawl4AI | 70.7 | 48.5 | 46.5 | 15.8 |\n| Bing + Request | 69.2 | 47.6 | 46.8 | 15.4 |\n| Google Serper + Crawl4AI | 70.2 | 48.5 | 46.5 | 15.6 |\n| Google Serper + Request | 69.7 | 46.6 | 45.9 | 15.4 |\n\n**Analysis:**\n- **Impact of Search Engines**: Experiments show that Google Serper API and Bing Search API have similar overall performance, proving that WebThinker's performance has low dependency on search engine types and has good adaptability.\n- **Impact of Crawler Tools**: Compared to using only the Request library, after adopting Crawl4AI to crawl web page content, all datasets achieved slight improvements in accuracy. This indicates that more efficient web page acquisition capabilities do help enhance system performance, but the improvement is limited, indicating that WebThinker has relatively low dependency on specific crawler tools.\n\n\n> ### W3: WebThinker is limited to processing static web pages based on text and links, which reduces its ability to be used in real-world scenarios.  \n\nWebThinker indeed primarily processes static web pages based on text and links, but this does not significantly reduce its applicability in real-world scenarios for the following reasons:\n\nFirst, most knowledge and information on the internet still exists in the form of text and hyperlinks. Internet content is still primarily text-based, which means WebThinker can process the vast majority of information resources on the internet.\n\nSecond, our system design considers the diversity of practical application scenarios:\n\n1. **Value of Static Content Processing**: In key application scenarios such as academic research, fact-checking, and knowledge-intensive Q&A, text information is the primary information carrier. WebThinker demonstrates excellent performance in these scenarios, as shown by the GPQA and GAIA benchmark results.\n\n2. **Extensibility for Dynamic Web Pages**: WebThinker adopts a modular design and can extend its ability to process dynamic content through plugins. In our follow-up research, we have begun integrating JavaScript execution environments to enable the system to interact with dynamic web page elements. The current version focuses on establishing robust text understanding and link navigation foundations, which are necessary prerequisites for handling more complex web interactions.\n\nAdditionally, we discuss this limitation in detail in Section 5.3 of the paper and propose clear future work directions, including integrating web rendering engines and adding multimodal understanding capabilities to expand the system's processing scope.\n\nTherefore, although the current version of WebThinker does focus on processing static web page content, this design choice is based on a trade-off between practical application needs and technical feasibility, and does not significantly limit its applicability in real-world scenarios. On the contrary, it lays the foundation for building more complex web agent interaction systems.\n\nThank you again for your valuable comments and suggestions. We hope our responses have addressed your concerns and clarified the strengths and limitations of our work. We look forward to further feedback and discussion.\n\nBest regards,\nAuthors of WebThinker"}, {"review_id": "2GtQ45vQ0j", "reviewer": "Reviewer_WwxH", "summary": "This paper introduces WebThinker, a framework that enhances Large Reasoning Models (LRMs) with autonomous web research capabilities by integrating a Deep Web Explorer module and an Autonomous Think-Search-and-Draft strategy directly into the reasoning process. Unlike traditional RAG approaches that use predefined workflows, WebThinker allows LRMs to dynamically search the web, navigate through web pages by clicking links and buttons, and draft comprehensive research reports in real-time during their reasoning. The system operates in two modes: Problem-Solving Mode for complex reasoning tasks requiring external knowledge, and Report Generation Mode for creating detailed research documents. The authors enhance the framework through reinforcement learning using iterative online Direct Preference Optimization (DPO) to improve tool utilization. Experimental results on complex reasoning benchmarks (GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks demonstrate that WebThinker significantly outperforms existing methods and even some proprietary systems like o3-mini, achieving state-of-the-art performance among 32B parameter models.", "questions": "1. In Figure 2(b), what would happen if we replaced \"Traditional LLMs\" with \"LRMs\"? Although there are subsequent experimental validations, the figure here cannot highlight the advantages of your paper's architecture/training method because it introduces a second variable. I suggest optimizing this in future versions.\n\n2. Line 192, why not test on AIME24/25, AMC23?\n\n3. Line 200, for report generation, shouldn't you test the effectiveness of LLMs as evaluators, i.e., compare the difference between LLM as evaluator and human scores?\n\n4. For particularly long reports, how should this be handled if the LLM's context window is not large enough?\n\n5. What are the specific metrics for report evaluation? I think this should be clearly explained in the main text.\n\n6. For the reasoning task, should we apply pass@k as a metric? \n\nIf my concerns can be addressed, I am willing to increase my score.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\nWebThinker addresses a critical limitation of current LRMs by integrating web exploration directly into the reasoning process rather than using separate preprocessing steps. The Deep Web Explorer module shows genuine innovation by enabling autonomous web navigation and link clicking, going beyond traditional RAG approaches. The experimental evaluation is comprehensive across multiple challenging benchmarks (GPQA, GAIA, WebWalkerQA, HLE) with consistent improvements over strong baselines including proprietary systems. The RL-based training strategy using iterative online DPO is well-motivated and effective. The paper achieves strong empirical results and provides clear methodology with good implementation details for reproducibility.\n\n**Weaknesses**\n\nThe evaluation methodology has significant concerns, particularly heavy reliance on LLM-based evaluation without human baselines, which may introduce systematic biases. Figure 2(b) is misleading as it compares WebThinker (using LRMs) against traditional LLMs, introducing confounding variables. The experimental scope is limited - missing tests on established benchmarks like AIME24/25, and report generation uses only 30 questions.", "was_revised": true, "final_justification": "The response from the authors solves my concerns and questions. So I would like to raise my score from 3 to 4.", "rebuttal": "Thank you very much for your thorough review of our paper. We greatly appreciate the opportunity to address your questions and concerns. Please find our detailed responses to each of your points below.\n\n> ### Q1: In Figure 2(b), what would happen if we replaced \"Traditional LLMs\" with \"LRMs\"? Although there are subsequent experimental validations, the figure here cannot highlight the advantages of your paper's architecture/training method because it introduces a second variable. I suggest optimizing this in future versions.\n\nThank you for your insightful question. You are correct that in Figure 2(a) and (b), the backbone model used is a traditional LLM, whereas in Figure 2(c) and (d), the backbone model is an LRM. This inconsistency may cause confusion.\n\nThis is primarily because the workflow-based RAG methods depicted in Figure 2(a) and (b) are typically paired with traditional LLMs. In these approaches, each LLM generation predicts a single action for the current state based on a specific instruction (such as query planning or search necessity prediction). Each task step only requires the LLM to make a brief prediction, without the need for the LRM to generate lengthy chains of thought at every stage.\n\nIn contrast, the end-to-end agentic reasoning proposed by WebThinker in Figure 2(c) is better suited for LRMs. Here, LRMs can autonomously invoke research tools throughout a single, extended reasoning process, enabling them to complete an entire task within one coherent generation.\n\nTo resolve this inconsistency, we will update Figures 2(a) and (b) in the revised paper to use LRMs as the backbone model, thereby more clearly highlighting the unique advantages of WebThinker.\n\n\n> ### Q2: Why not test on AIME24/25, AMC23?  \n\nThe primary reason is that WebThinker is designed to enhance the LRM reasoning process by incorporating search, web exploration, and report writing capabilities, with the goal of enabling Deep Research.\n\nAs demonstrated in prior work such as Search-o1 [1], experimental results indicate that mathematical tasks benefit only marginally from search operations, with relatively few search calls being made. This is because success in mathematical tasks typically depends more on the correctness of the problem-solving approach than on access to external knowledge. Consequently, we did not include mathematical tasks in our main evaluation, which is consistent with the methodology adopted by most deep research works, including OpenAI Deep Research [2], Kimi Researcher [3], Search-R1 [4], DeepResearcher [5], and WebDancer [6].\n\nTo more precisely assess the impact of WebThinker’s design on reasoning tasks such as AIME24/25 and AMC23, we conducted targeted experiments, summarized as follows:\n\n| Method | AIME 2024 | AIME 2025 | AMC 2023 | Average |\n|---|--|-|-|-|\n| QwQ-32B | 76.7% (23/30) | 50.0% (15/30) | 92.5% (37/40) | 73.1% |\n| Search-o1-32B | 80.0% (24/30) | 46.7% (14/30) | 90.0% (36/40) | 74.4% |\n| WebThinker-32B | 83.3% (25/30) | 53.3% (16/30) | 92.5% (37/40) | 77.5% |\n\nFrom these results, we observe that on the three mathematical reasoning datasets—AIME 2024, AIME 2025, and AMC 2023—WebThinker-32B achieves performance comparable to or slightly better than QwQ-32B and Search-o1-32B, with improvements of 1–2 questions in the AIME series. This suggests that WebThinker’s design exhibits a certain degree of generalization to mathematical tasks. However, since mathematical problems generally require less external knowledge, the overall improvement remains limited.\n\nLooking ahead, we plan to integrate additional tools such as calculators and Python code executors to further enhance WebThinker’s capabilities on mathematical and other specialized tasks.\n\n> ### Q3: For report generation, should we test the effectiveness of LLMs as evaluators, i.e., compare the difference between LLM evaluators and human scores?  \n\nThank you for this important question. In our work, we employ the LLM-as-a-Judge approach, where large language models are used to evaluate reports generated by different systems in a listwise fashion, assigning comparative scores to assess their relative quality. We initially opted not to use human evaluation because many of our colleagues are already familiar with the report generation features of products like OpenAI, Gemini, and Grok Deep Research, which could introduce bias into their scoring.\n\nTo validate the reliability of the LLM-as-a-Judge method, we invited four volunteers from finance, literature, and pharmacy backgrounds—individuals who rarely use Deep Research products and are thus unfamiliar with the report generation characteristics of each model—to manually score all reports (on a scale of 1–10) produced by each system. We then calculated the average score for each evaluation metric. The table below presents the results of this human evaluation:\n\n| Method | Comprehensive | Thoroughness | Factuality | Coherence | Avgerage |\n|-|-|-|-|-|-|\n| RAG-DeepSeek-R1 | 6.6 | 6.3 | 7.0 | 6.8 | 6.7 |\n| Grok3 DeeperSearch | 6.4 | 6.7 | 7.3 | 7.0 | 6.8 |\n| Gemini2.0 Deep Research | 8.0 | 7.8 | 7.6 | 7.7 | 7.8 |\n| WebThinker-32B-RL | 8.1 | 8.0 | 7.6 | 7.9 | 7.9 |\n\nAs shown, the average human evaluation scores across various metrics are largely consistent with the trends observed in LLM-as-a-Judge scoring. For instance, both WebThinker-32B-RL and Gemini2.0 Deep Research significantly outperform RAG-DeepSeek-R1 and Grok3 DeeperSearch on all indicators, with WebThinker-32B-RL being comparable to or slightly surpassing Gemini2.0 in terms of comprehensiveness and thoroughness. This strongly suggests that the LLM-as-a-Judge method can accurately reflect the actual quality of the reports.\n\nOne notable difference is that Grok3 DeeperSearch receives higher scores than RAG-DeepSeek-R1 in human evaluation, but lower scores in LLM-as-a-Judge assessments. This may be because Grok3 DeeperSearch produces reports that are more aligned with human reading preferences, even if the content is not as detailed as that of RAG-DeepSeek-R1.\n\nIn future revisions, we will incorporate these human evaluation results into the main text, along with a more detailed discussion and analysis.\n\n> ### Q4: For particularly long reports, how should this be handled if the LLM's context window is not large enough?  \n\nThe report generation mechanism in WebThinker is specifically designed to address the challenge of generating entire reports in a single pass, which can easily exceed the model’s context window. In our approach, whenever the main LRM invokes the \"write section\" operation, the auxiliary LLM is only responsible for generating the content of the current section, rather than the full report. Similarly, for \"edit section\" operations, the auxiliary LLM produces only the revised content based on the relevant section of the report, not the entire document. As a result, as long as the report length remains within the LLM’s context window (for example, Qwen2.5 supports up to 128k tokens), the system functions smoothly.\n\nIn practice, we have rarely encountered reports exceeding 128k tokens—our average report length is around 10k tokens. Should such a scenario arise, particularly during edit operations, we can segment the report, locate the relevant paragraph within these segments, and perform the necessary edits accordingly.\n\n> ### Q5: What are the specific metrics for report evaluation? I think this should be clearly explained in the main text.  \n\nThank you for this valuable suggestion. Previously, due to space constraints, we included all specific evaluation prompts in Appendix C.3.2 and did not elaborate on them in the main text. If possible, we will provide a detailed explanation of these metrics in the main text of the camera-ready version.\n\n> ### Q6: For the reasoning task, should we use pass@k as a metric?  \n\nWe currently use the Pass@1 metric, as in practical applications, user experience is typically determined by whether the system can generate a correct response in a single attempt, rather than across multiple generations. However, to better reflect the system’s potential reasoning capabilities, evaluating Pass@k for k > ### 1 is certainly meaningful.\n\nAccordingly, we also conducted experiments with Pass@3 and Pass@5, with the following results:\n\n| Method | GPQA | GAIA | WebWalkerQA | HLE | Average |\n|-|-|-|-|-|-|\n| Pass@1 | 71.2 | 48.5 | 46.5 | 15.8 | 45.5 |\n| Pass@3 | 78.3 | 56.3 | 52.8 | 19.2 | 51.7 |\n| Pass@5 | 82.8 | 59.2 | 55.9 | 21.4 | 54.8 |\n\nThese results show that, across all datasets, Pass@3 and Pass@5 accuracy rates are noticeably higher than Pass@1. Specifically, Pass@3 improves by approximately 6 percentage points on average over Pass@1, and Pass@5 provides a further 3–4 percentage point increase. This demonstrates that WebThinker is capable of generating more diverse answers across multiple attempts, thereby increasing the likelihood of producing a correct response. Nevertheless, it is important to note that even with Pass@5, the improvement on challenging datasets such as HLE remains limited, highlighting the intrinsic difficulty of these tasks. This observation also points to future directions, such as end-to-end RL training and hierarchical RL training, to further enhance the model’s reasoning abilities.\n\nWe hope the above responses address your questions. We sincerely appreciate your valuable feedback and continued support for WebThinker. We look forward to further feedback and discussion.\n\nBest regards,\n\nAuthors of WebThinker\n\n---\n\n### References:\n\n[1] Search-o1: Agentic Search-Enhanced Large Reasoning Models\n\n[2] Introducing deep research.\n\n[3] Kimi-Researcher: End-to-End RL Training for Emerging Agentic Capabilities\n\n[4] Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning\n\n[5] DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments\n\n[6] WebDancer: Towards Autonomous Information Seeking Agency"}, {"review_id": "5Nkz86lPBD", "reviewer": "Reviewer_qgwT", "summary": "The authors propose the WebThinker framework for answering user queries as well as writing reports. The framework augments an RLM with additional tools: web search capabilities to gather additional knowledge for a given query as well as writing tools for the second use case (write a specific chapter, check the report, and edit the report). The web search tool includes a search engine as well as a navigational tool and the ability to summarize the returned content to put less burden on the context of main RLM. The writing tools are implemented as language model-based tools with another language model. The framework works by using a single long context inference pass of the RLM, where the RLM is allowed to produce special tags to invoke tools with the RLM inference being suspended if necessary, while the tool is executed. Certain tools can be run concurrently. This leads to what the authors call \"Autonomous Think-Search-and-Draft strategy\", where the reasoning, knowledge acquisition and report writing (for the second use case) can be interleaved. The framework requires training to improve usage of the tools for which the authors use DPO where they select the preference pairs based on (in descending order) accuray, fewer tool calls and conciseness. The authors then proceed to evaluate both use cases, especially the question answering on a variety of benchmarks. Answers are submitted to a language model for accessing correctness (comparing to ground truth) or by using the LLM-as-a-Judge approach for the reports on a variety of metrics.", "questions": "* Please elaborate on the costs (time/Dollar amount/token usage) for the training as well as the evaluation tasks?\n  * Did you conduct any experiments/analyzed the latency of the answer generation?\n* What is the token limit for the language models in the tools?\n* What are the differences to other web search implementations used for example in KGoT [1] or Hugging Face Agents [2]? I think [1] was inspired by [2] if I recall correctly.\n* document memory M: How does this knowledge base work?\n* While the authors use certain evaluation benchmarks that have not been used during the training, how much of out of distribution can they be considered?\n\n[1] Besta et al. \"Affordable AI Assistants with Knowledge Graph of Thoughts\" (2025) arXiv:2504.02670\n\n[2] Aymeric Roucher and Sergei Petrov. \"Beating GAIA with Transformers Agents.\" (2025) https://github.com/aymeric-roucher/GAIA", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "strengths:\n* good evaluation\n\nweaknesses:\n* at times, the information regarding the workflow seems to be repeated multiple times without much additional details\n* very few technical details in general (in the main body)\n* limited novelty (other web search tools for language models exist, see references in questions)\n\npage 25, lines 743/744: potential breach of anonymity - https://github.com/sunnynexus/WebThinker", "was_revised": true, "final_justification": "I appreciate the additional experiments/data shown during the rebuttal phase, that illustrate that WebThinker provides improvements in quality while maintaining reasonable resource usage. The additional clarifications on the novelty convinced me, so most of my concerns regarding the manuscript have been addressed, which is why I will raise my score to \"accept\". I suggest however that authors working on improving the clarity of their writing before submitting the camera-ready version.", "rebuttal": "Thank you very much for your valuable and detailed review. We sincerely appreciate your constructive feedback and the opportunity to further clarify and strengthen our work. Please find our point-by-point responses below.\n\n> ### Q1: Please provide more details about the cost of training and evaluation tasks (time/dollar amount/token usage)?  \n\nWe provide a breakdown of the estimated costs for training, inference, and evaluation, using H100 80G GPU hours as the unit. The actual dollar amount will depend on current market prices.\n\n- **Training Cost**: This consists of two main components: data construction and model training.\n  - (1) *Data Construction*: This is the most time-consuming phase. We utilize 4 nodes, each with 8 NVIDIA H100 80G GPUs, running vLLM to serve all required models. Data collection is performed over 3 rounds, corresponding to 3 DPO training iterations. Each round of data collection takes approximately 25 hours, totaling about 2400 H100 GPU hours.\n  - (2) *Model Training*: For each DPO iteration, we use 1 node with 8 NVIDIA H100 80G GPUs. Each iteration takes about 18 hours, and with 3 rounds, this sums to roughly 432 H100 GPU hours.\n  - (3) *Total*: The overall training cost is therefore about 2832 H100 GPU hours (equivalent to 3.7 days using 4 nodes of 8 GPUs).\n\n- **Inference Cost**:\n  - Inference is performed on 1 node with 8 NVIDIA H100 80G GPUs using vLLM. For GPQA, GAIA, and WebWalkerQA, each dataset requires about 1.2–2 hours; HLE takes about 5 hours; and Glaive report generation takes about 1 hour. The total inference time is approximately 10 hours.\n\n- **Evaluation Cost**:\n  - For evaluation, we use DeepSeek-R1-671B and GPT-4o. GPT-4o processes about 1.6 million tokens, while DeepSeek-R1-671B processes about 1.8 million tokens.\n\n> ### Q2: Did you conduct any experiments or analyses regarding the latency of answer generation?  \n\nThank you for this important question. Computational overhead and efficiency are common challenges for all current deep research methods.\n\nWebThinker is designed to balance performance improvements with efficiency, which is particularly challenging when leveraging large-scale web information. Specifically, we introduced a deep web explorer module to intelligently process and integrate external web content, returning only refined, relevant information rather than indiscriminately inserting all retrieved pages into the reasoning chain. This avoids excessively long contexts and maintains reasoning coherence and efficiency. A similar approach is used for report generation, where auxiliary LLMs are employed for writing, checking, and editing, rather than inserting the entire report into the main reasoning chain.\n\nTo empirically assess efficiency, we compared the inference time of different methods. All methods use 32B models as backbone and are evaluated on the GAIA benchmark with a single node of 8 NVIDIA H800 80G GPUs. The results are as follows:\n\n| Method            | Avg. Inference Time (s) | Avg. Retrievals | Avg. Seq. Length (tokens)      | Accuracy (%) |\n|-------------------|------------------------|-----------------|-------------------------------|--------------|\n| Direct Generation | 11.1                   | 0               | 5531                          | 22.3         |\n| Standard RAG      | 23.2                   | 1.0             | 9246                          | 32.0         |\n| Iterative RAG     | 34.4                   | 2.3             | 16734                         | 35.0         |\n| WebThinker        | 40.8                   | 4.9             | 8732 (main) + 17374 (aux)     | 48.5         |\n\nThese results show that WebThinker achieves significantly higher answer accuracy (48.5%) while keeping inference time and sequence length within the same order of magnitude as traditional RAG methods. Notably, the main reasoning chain in WebThinker is even shorter than in Standard and Iterative RAG, which directly stack retrieved content. In practical deployment, smaller auxiliary LLMs can be used to further reduce inference time and computational cost, as these models only handle simple sub-tasks.\n\n> ### Q3: What is the token limit for the language models in the tools?  \n\nWe use the Qwen2.5 architecture (QwQ and Qwen2.5), which supports a maximum context length of 128k tokens.\n\nHowever, in practice, the actual sequence lengths are much shorter than this limit. For specific average sequence lengths, please refer to the statistical table in the answer to Q2.\n\n> ### Q4: What are the differences to other web search implementations used for example in KGoT [1] or Hugging Face Agents [2]? I think [1] was inspired by [2] if I recall correctly.\n\nBoth KGoT and Hugging Face Agents are primarily based on the Surfer Agent from Hugging Face Agents for web search. The Surfer Agent provides core functionalities such as:\n- Google search (via SerpAPI)\n- Wikipedia page access\n- Web navigation (including page scrolling, keyword search, URL access, etc.)\n\nThese systems employ an iterative tool-calling framework with a variety of tools, but integrating them coherently with the reasoning chain of the main model can be challenging.\n\nIn contrast, WebThinker is designed to tightly couple web exploration with the reasoning process, enabling model-driven, autonomous deep exploration. It can thoroughly analyze webpage content, identify useful information, and decide whether to continue searching or follow links for deeper information. WebThinker departs from predefined workflows and emphasizes autonomy. Nevertheless, the web exploration tools in KGoT and Hugging Face Agents are valuable references, and we plan to further adapt and cite these related works.\n\n> ### Q5: Document memory M: How does this knowledge base work?  \n\nIn WebThinker, **Document Memory M** is a dynamic cumulative knowledge base used to store and manage all the web page information obtained through the Deep Web Explorer tool. Its working mechanism is as follows:\n\n**1. Storage Method**\n- **Source**: All web page content obtained through Deep Web Explorer search or link clicks (such as title, summary, full text summary, etc.) will be automatically stored in M.\n- **Update Timing**: After each call to the search tool ($T_{exp}$), new web page information will be appended to M, forming an incremental knowledge base.\n\n**2. Retrieval and Use**\n- **Retrieval Trigger**: When the main model (Main LRM) calls the report writing tool (such as $T_{draft}$, $T_{edit}$), the assistant model (Assistant LLM) will retrieve **Top-K relevant documents** (based on semantic similarity) from M.\n- **Retrieval Basis**: When retrieving, reference two parts:\n  - **Current Writing Instruction** (such as chapter topic).\n  - **Historical Report Content** (to avoid repetition or contradictions).\n\n**3. Role and Advantage**\n- **Global Consistency**: Ensure that information sources referenced in different chapters are unified, avoiding contradictions.\n- **Dynamic Expansion**: With the deepening of search, the knowledge base continues to enrich, supporting deep writing in subsequent chapters.\n- **Efficient Access**: Through Top-K retrieval, avoid injecting all webpage content into the main model context, reducing redundancy.\n\nThrough this design, **Document Memory M** acts as a bridge between dynamic information acquisition and high-quality report generation, balancing information coverage with computational efficiency.\n\n> ### Q6: Although the authors use some evaluation benchmarks that were not used during training, to what extent can these be considered out-of-distribution?  \n\nTo better illustrate the contribution of each data component, we trained WebThinker on each individual dataset and evaluated performance across all benchmarks. The results are as follows:\n\n| Training Data Set | GPQA | GAIA | WebWalkerQA | HLE | Average |\n|-------------------|------|------|-------------|------|---------|\n| SuperGPQA         | 68.7 | 41.7 | 40.0        | 14.2 | 41.2    |\n| WebWalkerQA       | 65.7 | 44.7 | 45.0        | 13.0 | 42.1    |\n| OpenThoughts      | 64.1 | 43.7 | 41.5        | 13.6 | 40.7    |\n| NaturalReasoning  | 66.2 | 41.7 | 40.0        | 12.4 | 40.1    |\n| NuminaMath        | 63.6 | 39.8 | 39.0        | 13.8 | 39.1    |\n| All Data          | 71.2 | 48.5 | 46.5        | 15.8 | 45.5    |\n\nThe experimental results indicate:\n\n1. Models trained on a single dataset perform best on their corresponding evaluation task. For example, training on WebWalkerQA yields 44.7% accuracy on the WebWalkerQA evaluation set, outperforming other single-dataset trainings on that benchmark.\n\n2. Different types of training data contribute variably to generalization. For instance, models trained on SuperGPQA and WebWalkerQA also perform well on other tasks, suggesting these datasets contain more generalizable reasoning patterns.\n\n3. Training on the combined dataset yields the best overall performance across all evaluation tasks, with average accuracy improvements of 3–6 percentage points. This demonstrates the complementary nature of different data types in enhancing overall model performance.\n\nWe hope these responses have addressed your concerns and clarified the design and evaluation of WebThinker. Thank you once again for your insightful feedback and suggestions. We look forward to further discussion.\n\nBest regards,\n\nThe Authors of WebThinker"}]

Author rebuttal:

Meta-review: This paper introduces WebThinker, a framework that augments Large Reasoning Models (LRMs) with autonomous web exploration and report generation capabilities through a Deep Web Explorer module and an Autonomous Think-Search-and-Draft strategy. WebThinker enables dynamic web search, navigation, and real-time report drafting within the reasoning process, enhanced via reinforcement learning with iterative online DPO.

The paper demonstrates strong empirical results across multiple benchmarks (GPQA, GAIA, WebWalkerQA, HLE) and report generation tasks. All four reviewers acknowledged the comprehensive evaluation and practical value of the system. During the rebuttal, authors provided extensive additional experiments addressing efficiency concerns, showing that WebThinker achieves 48.5% accuracy on GAIA with inference time comparable to traditional RAG methods (40.8s vs 34.4s for Iterative RAG), and that using smaller auxiliary models (7B) can reduce latency to 29.2s with minimal accuracy loss. Authors also validated their LLM-as-Judge evaluation through human studies with four volunteers from different domains, confirming similar quality rankings.

However, reviewers raised concerns about novelty and clarity that were partially addressed. Reviewer 6BBj noted that many components are not new, though acknowledged the system's usefulness. The claim that "most existing methods rely on predefined workflows" was questioned as not entirely fair given recent advances in the field. Reviewer qgwT pointed out limited technical details in the main body and repetitive information presentation. While authors clarified their contributions, the novelty concerns regarding the integration of existing techniques remain valid.

The consensus among the engaged reviewers supports acceptance. The paper makes solid empirical contributions with practical value, despite limited conceptual novelty. The extensive experiments, including those added during rebuttal, demonstrate the system's effectiveness and efficiency trade-offs.

The recommendation is to accept this paper.
Decision: Accept (poster)