WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Authors: Zhengwei Tao
Jialong Wu
Wenbiao Yin
Pu Wu
Junkai Zhang
Baixuan Li
Haiyang SHEN
Kuan Li
Liwen Zhang
Xinyu Wang
Wentao Zhang
Yong Jiang
Pengjun Xie
Fei Huang
Jingren Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: agent
information seeking
data synthesis
llm
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: The advent of Large Language Model (LLM)-powered agents has revolutionized artificial intelligence by enabling solutions to complex, open-ended tasks through web-based information-seeking (IS) capabilities. The scarcity of high-quality training data has limited the development of IS agents.  Existing data synthesis approaches typically adopt an information-driven paradigm that first collects information and then refines question-answer pairs through retrieval. However, this may lead to inconsistency between information structure and reasoning structure, as well as between the question and the corresponding answer. To mitigate, we propose a formalization-driven IS data synthesis framework WebShaper, which systematically formalizes IS tasks using set-theoretic constructs.
Central to the formalization is the concept of Knowledge Projections (KP), which enables precise control over reasoning structure by KP operation compositions.  During synthesis, we begin by creating seed tasks, then use a multi-step expansion process.
At each step, an agentic Expander expands the current formal question more complex through retrieval and validation tools grounded in our formalization. We train our model on the synthesized dataset. Experiment results demonstrate that WebShaper achieves state-of-the-art performance among open-sourced IS agents on competitive benchmarks.

Published as a conference paper at ICLR 2026
WEBSHAPER: AGENTICALLY DATA SYNTHESIZING VIA INFORMATION-SEEKING FORMALIZATION
Zhengwei Tao T 1,2,
Jialong Wu 1.23,4444
Wenhiao Yin
Pu Wu'
Junkai Zhang'
Baixuan Li3,4
Haiyang Shen 1.4 Kuan Li
Liwen Zhang"
Xinyu Wang'
Yong Jiang"1 Pengjun Xie
Fei Huang' Jingren Zhou*
Wentao Zhang .255f
Peking University
Beijing Key Laboratory of Data Intelligence and Security (Peking University) Southeeat University "Tongyi Lab, Alibaba Group ZZhongguancun Academy
tttzwCatu. .kut. edu. wwu.pdu.ccc cn wentao zhang@pku edu. cn
"Equal Contribution
*Corresponding Author
ABSTRACT
The advent of Large Language Model (LLM)-powered agents has revolutionized artificial intelligence by enabling solutions to complex, open-ended tasks through web-based information-seeking (IS) capabilities. The scarcity of high-quality training data has limited the development of IS agents. Existing data synthesis approaches typically adopt
ieformmation-driver paradigm that first collects in- formation and then refines question-answer pairs through retrieval. However, this may lead to inconsistency between information structure and reasoning structure, as well as between the question and the corresponding answer. To mitigate, we propose a formalizaatonn-drivee IS data synthesis framework WebShaper, which systematically formalizes IS tasks using set-theoreti constructs. Central to the formalization is the concept of Knowledge Projections (KP), which enables precise control over reasoning structure by KP operation compositions. During synthesis, we begin by creating seed tasks, then use a multi-step expansion process. At each step, agentic Expander expands the current formal question more complex through retrieval and validation tools grounded in our formalization. We train model on the synthesized dataset. Experiment results demonstrate that Web- Shaper achieves state-of-the-ard performance among open-sourced IS agents on competitive benchmarks.
INTRODUCTION
The emergence of Large Language Model (LLM))powered language agents has marked paradigm- shifting advance artificial intelligence, enabling transformative solutions to previously intractable challenges across domains (Guo et al., 2024; Wang al., 2024; AutoGPT, 2023; Wu et al., 2023; Ye et al., 2023). Information-seeking (IS) represents a core component of the cognitive autonomy of language agents. This capability not only underpins their adaptability in open-ended tasks but also powers a range of powerful commercial systems such as Deep Research of OpenAl (OpenAI, 2025), Gemini (Gemini, 2025), and Perplexity (Perplexity, 2025).
Current agentic systems for unlocking this capability typically follow a well-established pipeline in agent development: (1) First, construct task-specific trajectories of question-answer pairs; (2) Employ supervised fine-tuning (SFT) to acquire foundational skills (Sun et al., 2025). (3) Generalize strategic decision Doaiiin through on-policy reinforcement learning (RL) (Jin et al., 2025). The entire development of the IS agent originates from and its ultimate effectiveness depends on high-quality IS task training data. However, due to its complexity, such high-quality dataset both sparse and difficult to construct through crowdsourcing. Thus, constructing training data through a carefully designed agent pipeline becomes the cornerstone of effective IS agent development.
Existing IS dataset synthesis methods typically involve freely pre-searching for information online and employing LLMs to generate questions from the collected content. These approaches first organize the collected information into structured formats, then prompt the LLM with the structured data to produce natural language (NL) questions. Their core objective is to map information structures into reasoning structures within the resulting NI. questions. Representative methods like WebDancer (Wu

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Published as a conference paper at ICLR 2026
et al., 2025a) and TaskCraft (Shi al., 2025) generate linear information chains, while others construct graphs connected via web links (Wu et al., 2025b) or entity coreference networks (Li et al., 2025a). However, these information-drivee the synthesis using L.LM may struggle to fully comprehend the information structure, resulting in inconsistent reasoning structures or incorrect answers to the generated NL questions. Besides, disordered information retrieval will lead to excessive data processing and will collect redundant homogeneous information structures, which limits the diversity of information structures.
approaches face two critical limitations. First,
To overcome these limitations, we propose WebShaper', a formalization-driven IS data synthesis paradigm. Unlike prior approaches, WebShaper first formalize information seeking tasks and then systematically guide data synthesis through this formalization. During generation, information collection explicitly controlled by formal task requirements. This framework offers three key advantages: Broader Task Coverage: Systematic exploration of task formalizations enables synthe- sizing diverse information-sseeking patterns unconstrained by pre-retrieval content limitations; Task Controllability: Explicit formalization parameters allow precise specification of reasoning structures and complexity levels; Structural and Answer Consistency: Due to the inherent interpretability and verifiability of formalized representations, synthesized outputs exhibit fewer inconsistencies across both information-reasonee structures and dnlestion-asswer pairs. With this structured guidance, we produce consistent reasoning and redundancy while ensuring rich, diverse reasoning logic.
At the core of our framework lies a formalization of IS tasks, which enables principled and systematic generation of task instances with controllable collection complexity and reasoning structures. Unlike relevant fields, where there exists task formalization in advance, such as Lean 4 language (Moura & Ullrich, 2021) in math proving and propositional logic in knowledge-centric question answering (Xia et al., 2025), there's no established formalization for information-seekingg We treat IS as a unified problem space where task systematically derived from compositions of basic units termed Knowl- edge Projections (KP). To aligm with the formalized structure, we initiate synthesis by constructing foundational seed tasks, followed by a multi-step expansion grounded in our formal framework. This process employs a dedicated agentic Expander module designed to interpret task requirements via KP representations. At each expansion stage, the expander transforms the current formal question into a more complicated one. It implements layer-wise expansion mechanisms that minimize redundancy while preventing reasoning shortcuts through controlled complexity progression. This process ensures broad coverage of the formalized task space and the correctness of the question and answer.
We conduct extensive experiments to validate WebShaper dataset by training agents. Comparison with the existing training dataset shows the effectiveness of WebShaper. WebShaper achieves best performances among all open-source IS agents on the GAIA and WebWalkerQA benchmarks. Further discussions demonstrate the validity of each module of our method. We summarize contributions:
We introduce WebShaper, a formalization-driven data synthesis method for information-seeking agents, grounded in our proposed task formalization. Leveraging this method, we construct the WebShaper dataset, which enables systematic generation of IS instances.
We propose an agentic Expander that iteratively generates and validates questions in alignment with the formalization, yielding broad coverage IS training data.
We conduct extensive experiments across multiple benchmarks to evaluate WebShaper. Empirical results demonstrate that models trained with WebShaper consistently outperform baselines, confirming the value of our formalization and synthesis approach.
2 INFORMATION-SEEKKIE FORMALIZATION
In this section, we introduce our formalization of the information anformationnssekkin task. We illustrate an example in Figure 1. An information-seeke
task (T) aims to search for knowledge and facts prompted by given facts and locate the answer entity set T. For basic example also shown in Figure 1: ((T) = Which player of a team in the 2004-05 season, who was born in 90s? This team is founded in 1966 and is an East German football team. To solve it, one should seek information about This team is founded in 1966 and is an East German football team to find that the team is Berliner FC Dynamo. And then seek for players of Berliner FC Dynamo team in 2004 and 2005 respectively and players born in 90s, then reason the answer T = {Robert Rudwaleit, Danny Kukulies, ..}.
Without loss of generality, use WehShaper denote our data method, dataset, and model.
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11 =
R (oundin((1966))
Rua((Fast German football team))
Retarar ((004))
(T) - ?
(playh((Ts)
n
U1900 Raorui (year)
R Rployar((2005)
Question: which player of team the 2004-05 season, who was born 90s? This team is founded 1965 and an East German footboil team.
Answer: Robert Rudwaleit, Danny Kukulies,
A question-answer case in information-seekt formalization. We use the purple diagram to represent knowledge projection, which is a set of entities.
Figure 1:
Let & denote the universal set of entities (e.g., players, teams, years). Let R C & x & denote a subspace of entity pairs where they have a certain relation. For example, the relation bornin, R stands for all pairs of (person, year) where person is born in year.
For subset V and sub-space R. define a Knowledge Projection (KP):
(1) For example, when denotes entity pairs of relation bornin, R R{{90s)) represents the of all people born in 90s. A KP is the set of entities under a certain relation to other entities, which is the basic unit in an information-seekine -eeeaiin---e-inggg task. has two operations:
R(V) {u Er E V, (u, R or (e, u) E R).
R-Union U In IS, the question may be secking for broader condition due uncertainty about the target. For instance, we only know the target player was playing between 2000-2010 rather than the exact year in advance. The condition can not be more specific than year range.
Therefore, given S1, S2 be entity sets and R, then:
R(V)= R(S,) U U.. ((S.))
R(Sm)
(2)
represents R(V) is the union result set which the entities have a certain relation to entries in either S1, S2, ..., Sm. If R stands for relation playAr, then the set of players who play between 2000-2010 is R{{2000}) U R{{2001})) -... - UR((010)))
Intersection n Some IS tasks require the target to satisfy several conditions simultaneously. It's interpreted as an Intersection operation of KP:
R(V) = R1 (S1) (2) (S,)
where R, are about different relations. For example, if R, about playAr and R₂ is about boruIn, then Ri ((2000)) (29(0)))) stands for players playing 2000 and born in 90s.
Based on R-Union and Intersection operations, we define T as target set:
T (R.,)..) (.(22) U.. (.(s.)))))
Siy C. E. More generally, T can be recursivelly derived by replacing Su,, with other target set as: (5)
T R, (ii) (T(T)) n... (TT)))
An IS task (() TT find what entities questioned contains Therefore, the question example can be formalized as shown in Figure 1.
3 DATA SYNTHESIS
In this section, we describe the process of our data synthesis with our task formalization. As Eq, (4) shows, an IS task is recursively composited by knowledge projections. In order to better fit the IS task formalization, we start with constructing a seed task, followed by a multi-step expansion approach. This expansion process is built upon formalization. We then introduce can understand the task formalization with our KP representation. At each expansion step, we implement the layer-wise expansion reduce redundancy and reasoning shortcuts. The Expander
agentic Expander.
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autonomously retrieves knowledge from the internet, constructs and validates the new FPs to obtain the new question. We elaborate on this process in the following sections.
3.1 SEED QUESTION CONSTRUCTION
The first stage is acquiring substantial volume of diverse and non-trivial seed questions. To enhance acquisition efficiency, we constructed an offline Wikipedia database by downloading all URLs corresponding Wikipedia articles while preserving the hyperlinks between them. Subsequently, we perform random walks across these articles through their preserved connections. By aggregating the content from articles traversed during these random walks, we utilize an LLM to generate synthetic data instances. Critically, the generated question-answer pairs must be entirely grounded in the content from the collected articles, without relying on external knowledge sources.
However, the resulting seed questions could be noisy and contain hallucinations. We launch a filtering process. We complete all the seed questions by WebDancer framework (Wu et al., 2025a) based on the QwQ model (Team, 2025). We perform 5 times rollouts for each question and keep the data where there must be at least one rollout correctly answering the question. We finally construct 18k seed questions. We denote the harvested seed question as (T).
3.2 AGENTIC EXPANSION
Subsequently, we progressively expand seed questions into increasingly complex ones through 1-step expansion q +1 (T) Expand(g'([)) guided by the task formalization. However, the IS formalization in Eq. (4) is complicated. The nature of recursion and the composition of multiple operations are hard understand. Besides, since the synthesis relies on retrieving new knowledge online, there are several intermediate processes, such as knowledge filtering and selection. Therefore, we establish an Agentic Expansion. The core of the expansion is the Expander, which is an agent itself to autonomously retrieve information and validate the generation. We introduce the KP representation for the Expander to understand our formalization. Then, we propose the Layer-wise Expansion Strategy to mitigate the limitations of redundant and reasoning shortcuts.
3.2.1 KP REPRESENTATION
Since (T') contains recursion and composition of R- and Intersection operations, it's not trivial to represent (T) in the Expander agent prompt. We introduce our KP Representation. The key to this representation is to: 1) represent a KP unit. 2) can handle R-Union and Intersection operations. 3) can handle recursions of KPs. We introduce Constant and Variable. A constant is a subset of E explicitly defined by its elements, e.g., {90s], {2004, 2005}. A variable is subset of & whose elements are not explicitly given. It may appear as a symbolic placeholder in an expression.
We use triplet [X, S] to represent KP R(S). is the name of the relation R. X is variable while can be a variable or a constant. We use the prefix V@ followed by a variable to denote the variable V. We use the prefix @C' before its natural language description to represent constant. For example, Rooorte (90s}) (90s)) } is represented as [&V, bornin, 90s]. The Intersection operation in E3.(3) can be naturally represented as list of triplets [[X,, r1. S11. [X, 12, S2), .... [X, Tu, S.1].
For the R-Union in (2), simply expressing it in a list-like form will make the representation complicated in recursive R-Union and Intersection. We notice R-Union has the following proposition:
Proposition 1. For a certain R, R-union satisfies the distributive Law:
R(S)) = R(S, U2)
R(S)US))
(6)
We leave the proof in the Appendix C. With this proposition, we represent the R-Union of KP by merge set Si U Sz. In practice, we express the union of sets by induction (eg. {1990} U [199I)U,.... as ...7)),,....,.99999999 U[I999) as (90s)). Or simply add underlines between them (eg. [1990} U [1991}) After that, our representation would only have an intersection between triplets.
By introducing variables, our representation naturally handles KP recursion by faltten it into the intersection of KPs. For example, given recursion R (R?(S)), can represent it as [[V (VV@X, Tı, V@YJ. VV@Y, r2, SI]. Finally, an IS task (TT) can be represented by a list of triplets. The example question in Figure can be represented as:
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Expanded Question (")
Target Variable
O Constant
Seed Question (T)
(a) Random Structure
(b) Sequential Structure
(c) Layer-wise Structure
Figure 2: (a) Random Structure denotes expanding by randomly adding constants. (b) Sequential Structure is expanding on a chain reasoning sequence. (c) Layer-wise Structure traverses layer- wisely on leaf constants and replaces them with variables. "Target" stands for target variable.
((T) 2T s.t. [[V aT, playIn, V@X],
[V playAt, C@2004_000 05), [V@X, foundIn, C@1966), [V@X, isA, C'East German football team]]
[VOT, bornIn, C(990s]
(7)
3.2.2 LAYER-WISE EXPANSION STRATEGY
After representing (T)), outline the deration-based expansion process. Compared to prior methods extending questions in natural language, our IS task formalism enables systematic structural analysis, revealing latent patterns and enabling controlled, optimized expansion. To clearly illustrate the expansion strategy, we show our KP representation in a graph: nodes are variables/comstants from triplets; edges are relations. For example, Eq. (7) maps to the seed question in Figure 2, where the target variable is determined via given constants. Earlier approaches lacked formal structure, producing either Random (Wu et al., 2025b; Shi al., 2025) (adding FP arbitrary nodes, Figure 2(a) or Sequential (Wu et al., 2025a) (linear reasoning chains, Figure 2(b), both with issues:
Redundancy: Constants directly linked to other constants add sentences like "Dynamo Berlin is football club based in Berlin" without extending reasoning.
Reasoning Shortcut: Constants that are close to or directly connected to the target result in skipping deeper reasoming for the agent.
We address this via Layer-wise Expansion illustrated in the Figure 2(c). We layer-wisely traverse the graph to find all leaf constants. When we obtain all the leaf constants of the current graph, an Expander takes each constant once construct this constant into FPs. These FPs form sub-question that regards the constant as the answer. The expander then merges the sub-questions with the current one to form new one. replaces those constants with the sub-questions. Note that the answer for the expanded question always remains. The resulting structure would not have the Redundant and Reasoning Shortcut problems. The number of expanding layers a hyperparameter for controlling the task coverage and difficulty.
3.2.3 EXPANDER AGENT
We now introduce the Expander, autonomous agent designed to enhance question generation through iterative refinement. Given an input constant, the Expander first retrieves relevant information, then formulates semantically coherent sub-question. This sub-question is subsequently integrated with the original query to construct an enriched, context-aware question that better aligns with the underlying information-seckee Thought-Action www.ooonnnnoooinnoeeeeee triples (Te, Qt,, dt, 0r), where each Action (r, σ) specifies a tool and parameters. We equip the Expander with the following tools:
objective. It builds ReAct (Yao et al., 2023), cycling through
Search: It enables Expander to conduct Google search by severl queries about constant and obtains search results. The parameters of this tool are & {queries, filter filter_year), enabling temporal filtering of search results. This tool returns top relevant URLs and their snippets.
Summarize: This the key R-Union oepration. This action allows Expander to visit multiple URLs for the constant and summarize the content. The summarization would integrate the retrieved information to obtain a union constant set as stated in (6). The parameters of this tool are {urls, goal). This tool returns the summarization from the given URLs.
Validate: When Expander completes retrieving and summarizing the KPs of constant, it derives sub-question and uses this tool to validate the results based on formalization. The validation
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Table Main results on GAIA and WebWalkerQA benchmarks. We compare WebShaper with sev- eral cutting-edge baselines methods. bolded number stands for the best results on the corresponding settings. Blue scores are the highest among open-sourced methods.
GATA
WebWalkerQA
Backbone
Framework
Level
Level
Level
Avg.
Easy
Medium
Hard
No Agency
Qwen-2.5-7B
Base
12.8
3.8
0.0
6.8
1.25
0.8
0.7
0.8
Base
20.5
9.6
8.3
13.6
3.8
2.5
3.3
3.1
Qwen-2.5-32B
RAG
12.8
11.8
8.3
11.8
23.1
14.3
11.3
15.3
Qwen-2.5-72B
Base
20.5
13.5
14.6
9.4
7.1
3.3
6.3
GPT-4o
Base
23.1
15.4
8.3
17.5
6.7
6.0
4.2
5.5
Base
30.8
15.4
25.0
22.3
7.5
2.1
4.6
4.3
QwQ-32B
RAG
33.3
36.5
8.3
32.0
36,9
26.1
33.5
31.2
DeepSeek-RI166)
Base
43.6
26.9
8.3
31.1
5.0
11.8
10.0
Close-Sourced Agentic Frumeworky
OpenAl DR
74.3
69.1
47.6
67.4
Open-sourced Agentic Frameworks
Search-ol
33,3
25.0
0.0
28.2
Qwen-2.5-32B
WebDancer
46.1
44.2
8.3
40.7
44.3
46.7
29.2
38.4
WebShaper
61.5
53.8
52,4
58.1
51.4
47.0
51.4
Search-ol
53.8
34.6
16.6
39.8
43.1
35.0
27.1
34.1
WehThinker-Baie
53.8
44.2
44.7
47.2
41.1
39.2
41.9
WebThinker-Bi RL
56.4
50.0
48.5
58.8
44.6
40.4
46.5
QwQ-32B
Simple DS
50.5
WehDancer
61.5
50.0
25.0
51.5
52.5
59.6
35.4
47.9
WebShaper
69.2
50.0
16.6
53.3
55.8
49.2
45.4
49.7
WebSailor
55.4
Qwen-2.5-72B
WebShaper
69.2
63.4
16.6
60.1
56.2
52.1
49.5
52.2
purposes are to determine: 1) whether the derived sub-question approximately consistent with the constants based on the formalization. 2) whether it is too simple that can be directly answered by an LLM. This tool would return detailed validation results as Observation, and the Expander would take the next action according to it.
3.3 TRAJECTORY CONSTRUCTION
We then construct task-solving trajectories using ReAct format. At each step, the agent generates Thought, performs Act Action, receives Observati inn, and chooses the next move. At each time step t, the agent execution loop can formalized as a triple (Tr, a, Or), where т, denotes the free-form Thought, represents the structured Action, and corresponds to the Observation returned by the environment. The Thought component 7; unrestricted natural-language reasoning that model uses for planning, decomposition, self-reflection. grounding intermediate assumptions. The Action a; is further decomposed into an action type at and its parameter set a", i.c., a = (a" aP). The action type a (Search, Visit, Answer) corresponds to the core tool interfaces used in deep information-seeking tasks?
To standardize trajectories and facilitate supervised learning, adopt explicit structural markers for each segment. Thought segments are enclosed by <think> and </think>, Acti segments by <tool call> and </tool_ _aall,call,a___lla and Observation segments by The final Action segment, corresponding to the model's ultimate response to the task, is encapsulated <answer> and </answer>. These markers make agent behavior transparent and machine-parsable enabling precise control, analysis, and dataset construction. Each question gets 5 rollouts. We remove the trajectories where the answers are wrong, contain hallucinated observations, or severe repetition. We finally obtain 5,000 trajectories for supervised and reinforcement learning.
and </tool </tool_responte responssssssse....
"The details of tools shown in App.

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3.4 AGENT TRAINING
To train our information-sseking agent, similar to WebDancer (Wu al., 2025a), implement supervised fine-tuning (SFT) followed by reinforcement learning (RL). In SFT. we mask out loss from observation leading to loss. For RL algorithm, GRPO (Shao et al., 2024). We leave the details in the Appendix
EXPERIMENTS
4.1 EXPERIMENTAL SETUPS
We evaluate WebShaper on two information--sekknng benchmarks: GAIA (Mialon et al., 2023) and Web WalkerQA (Wu et al., 2025b). We use the LLM-as-Judges paradigm to evaluate both tasks using the Pass01 metric, following Li et al. (2025c). We elaborate on the WebShaper data statistics in the Appendix D. To assess dataset quality, conduct comparative study during the SFT stage by training on different data sources. The detailed descriptions of the datasets used in this comparison provided in Appendix E.
4.2 BASELINES
We mainly compare our method to open-source cutting-edge deep research agent frameworks: Search-ol (Li et al., 2025b), WebDancer (Wu et al., 2025a), WebThinker (Li al., 2025c), Sim- pleDeepResearch (Sun et al., 2025), and WebSailor (Li et al., 2025a). As more strict comparing settings, we train baseline models on WebWalkerQA (Wu et al., 2025b), E2HQA (Wu et al., 2025a), and MHQA (Sun al., 2025), respectively.
4.3 MAIN RESULTS
We compare WebShaper with cutting-edge baselines. The results are shown in Table 1. Web- Shaper achieves best performances on open-sourced methods on both GAIA and WebWalkerQA. Among all GAIA results, WebShaper-on Owen-2.5-72BB excels second-best method WebSailor 4.7 score. On Web WalkerQAA WebShaper obtains the highest 52.2 score. WebShaper performs the best on each backbone setting. These results indicate the generalizability of the synthesized data different models. WebShaper is currently the only open source method with a score of more than 60 points, which is close the SOTA OpenAl DR system. WebShaper is implemented fully under open-sourced LLMs, demonstrating that high-quality IS data can deeply stimulate the ability of DR Agents. Notably, find reinforcement learning QwQ-32 is not significant. Therefore, report the SFT result on QwQ-32B in Table 1.
4.4 DISCUSSIONS
4.4.1 DATA COMPARISON
In this section, we compare WebShaper with baseline datasets. We sample 5,000 data points from each dataset. Then we supervised fine- tune Qwen2.5-32B. Qwen2.5-72B (Yang et al., 2024), and QwQ (Team, 2025) on each dataset. The comparative results on GAIA presented in Table 2 demonstrate the superior perfor- mance of WebShaper across all backbone ar- chitectures on the GAIA benchmarks. No- tably, WebShaper achieves the highest average scores for Qwen-2.5-32B, Qwen-2.5-72B,, and QwQ-32B, respectively, significaatly outper- forming baseline datasets like WebWalkerQA and MHQA. Even when comparing models with similar parameter counts (e.g., Qwen-2.5-32BB WebShaper-enabled models show substantial im- provements. The consistency of WebShaper's performance improvement suggests its effective- ness in enhancing model capabilities regardless of architectural design. These findings validate
Table SFT Data Comparison on GAIA bench- marks. The best results are in bolded.
Backbone
Dataset
Avg.
WebWalkerQA
32.0
E2HQA
39.8
Qwen-2.5-32B
MHQA
35.9
WebShaper
43.6
WebWalkerQA
38.8
E2HQA
Qwen-2.5-72B
MHQA
43.6
WebShaper
45.6
WebWalkerOA
45.6
E2HQA
45.6
QwQ-32B
MHQA
41.7
WebShaper
53.3
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Passggt-F 1-L
Pasa@1-MM -NLL
P-ss@D-FL
Paseg3-NL
Pass@114
Pass@118
Passge4L
Passgg55
61.1760.19
Score
56.31
48.54
45.6644.60
66.31
Score
50
56.31
48.04
50.49
$4.37
42.72
42.72
40
40
36.89
30
30
Qwen2.5-32B
Qwen2.5-72B
(a) Formalization ablation analysis.
(b) Layer-wise structure ablation analysis.
Figure Discussions on formalization and layer-wise structure.
the effectiveness of nermalizationddrivee data synthesis, making it superior training data solution for information-secking tasks. More details are in the Appendix I.
4.4.2 RL STIMULATION
We compare GAIA performances between mod- els trained after SFT and reinforcement learning. RL models are trained based on the SFT results. As illustrated in Figure 4, our experimental results demonstrate significant performance improvements across both Qwen2.5-32B and Qwen2.5-72B mod- els after RL training on both GAIA and WebWalk- erQA. The Pass metric shows notable enhance- ments of +7.8 points for the 32B model and even more pronounced + +13.5 points increase for the 72B variant GAIA. On WebWalkerQA, Web- Shaper also improves IS capability on large scale. This substantial gain highlights the critical role of RL in activating advanced information-sede seeking ca- pabilities within LLM. The breadth and complex- ity of tasks introduced by our task formalization stimulate dynamic strategies during RL. Unlike generic datasets, our carefully curated scenarios require the model to iteratively query relevant infor- mation, effectively "training" it to prioritize contex- tually aligned knowledge fragments.
Pass@1-SFT
Pass@1-RL
70
+14.5
+8.8
60.1
60
5cores
52.4
50
43.6
45.6
40
30
Qwen2.5-32B
Qwen2.5-72B
(a) GAIA.
Pass@1-SFT
Pass@1-RL
60
+7.7
+6.8
51.3
52.2
Scores
50
44.6
46.4
30
Qwen2.5-32B Qwen2.5-72B
(b) WebWalkerQA.
4.4.3 FORMALIZATION
Figure 4: Comparison with SFT and RI..
In this part, validate whether formalization truly improves the dataset. We compare our dataset to variation that uses natural language during the data synthesis. We denote our method with formal language FL. while natural language NL. This variation takes the current question in each iteration and also uses the Expander agent to expand it to a new question. The Expander process in natural language as well. We SFT Qwen2.5-32B. Qwen2.5-72B, and QwQ on both datasets. The other training setting remains the same. We compare the training results with the variation as shown Figure 3a. FL excels NL all base model backbones. These results indicate that our formalization language can mitigate the limitations incurred by natural language. Our IS task formalization can synthesize more forms tasks. It also reduces error propagation the synthesis process, leading to consistent and precise question-ana answer pairs
4.4.4 LAYER-WISE EXPANSION STRATEGY
We evaluate the effectiveness of the Layer structure. In order to compare, we set up a variation which uses the same Expander and task formalization but expands the question in sequence as shown in Figure 2. We SFT Qwen2.5-32B, Qwen2.5-72B, and QwQ on both datasets. Other training settings remain the same. We denote method with the layer-wise structure as I,, while the sequential
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6.1
WebShaper -E2HQA Seed
-WebShaper
01
WehShaper
0.08
E2HQA
0.58
E2HQA
2..00
MHQA
g 0.1
MHQA
Seed Task
E..00
Seed Task
MHQA
2.04
0.04
0.04
0.22
15 21
101121211111111110000
Toal Call Count
Tool Call Count
Tool Call Count
(a) Search distribution.
(b) Visit distribution.
(c) Total tool distribution.
Figure 5: Tool call analysis.
structure as S. The results as shown in Figure 3b. The layer-wise structure performs better than the Sequential structure in all base models. The results show that method truly mitigates shortcomings such as Redundancy and Reasoning shortcuts. Our method improves the final performance via the controllable structures.
4.4.5 TOOL CALL ANALYSIS
We show the distribution tool call count of the agent to solve question in different datasets. We illustrate the tool call counts larger than 3, which shows the complicated trajectories proportion. Search Complexity (Figure 5a) WebShaper exhibits a pronounced long-tail distribution. Pretty much tasks requiring over 3 search operations. This is 3-4x higher than E2HQA and MHQA, indicating superior handling of information-ries queries requiring iterative refinement. Knowledge Navigation (Figure 5b) The visit operation distribution shows WebShaper maintains high ratio for trajectories exceeding 3 steps, while competing datasets sharply drop after 10 steps. This sustained capability reflects enhanced navigational intelligence in IS tasks. Composite Reasoning (Figure 5c) In total tool calls, WebShaper's 's doubles the count larger than 3. Notably. it sustains non-zero proportions up to 30 tool calls, demonstrating scalability for highly complex compositional reasoning. These findings underscore WebShaper's unique ability to manage intricate reasoning chains, with statistically significantly higher proportions of multi-hop reasoning trajectories across all modalities. The sustained performance in extended tool call sequences suggests superior architectural capacity for managing complex task decompositions compared to existing benchmarks.
4.5
COST ANALYSIS
Our overall synthesis pipeline is indeed more computationally demanding than traditional information- driven methods. On average, generating a single example requires roughly 20 LLM completions, 6 search calls, 6 visit calls, and about minutes of end-to-end runtime. While approaches such as WebWalkerQA and MHQA incur lower cost by relying on only a few LLM completions, they typically yield simpler multi-hop questions. More advanced methods like E2HQA also rely on multiple L.M and tool calls and are therefore not substantially cheaper when targeting complex reasoning. Importantly, the additional compute is necessary to produce in-domain, high-fidelity GAIA and WebWalker-evell data. As shown in Table 2, SFT on our synthesized data improves GAIA performance by 5-10 points across multiple backbones, consistently outperforming WebWalkerQA, E2HQA, and MHQA. These results underscore that the increased computational cost is well justified by the significant gains in data quality and downstream performance.
4.6 QUANTITATIVE EVALUATION OF QA FACTUAL ACCURACY
For the quantitative evaluation of QA factual accuracy, manual verification of large-scale synthesized QA pairs (thousands of instances) is impractical and resource-intensive. We thus adopt proxy method leveraging the interpretability of our formalization: feeding the formalized structure (including intermediate reasoning steps) of each synthesized question to the QwQ-based solving agent, which provides explicit reasoning path guidance and makes the agent's 's answer accuracy a reliable reflection of the original QA pair's 's factual correctness. Experimental results show the agent achieves over 80 % accuracy on the synthesized dataset, confirming that our nermalizationdriivee synthesis effectively mitigates hallucinations and inconsistencies while ensuring strong factual correctness.
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4.7 ETHIC DISCUSSION
Our synthesis pipeline is specifically engineered to mitigate ethical concerns and data quality risks through three core safeguards. First, seed questions are derived from factual, rigorously curated Wikipedia entries, which effectively mitigates topic drift and minimizes the propagation of inherent biases. Second, the generated tasks are strictly centered on fact-driven reasoning, deliberately steering clear of subjective sensitive domains, where biases are prone to emerge and amplify. Third, our multi-stage verification process systematically uphold factual consistency, while filtering out hallucinatory content and data contaminated by misinformation bias.
RELATED WORK
5.1 INFORMATION-SEER -SEEINNGG DATA SYNTHESIS
Recent advances in information-ssekkng agents aim to integrate web interaction into LLMs' reasoning. While these works exhibit promising capabilities, they predominantly depend on limited or overly simplistic datasets ( (Yang et al., 2018: Joshi al., 2017: Kwiatkowski et al., 2019). Concurrently, several recent benchmarks, such as GAIA (Mialon et al., 2023), BrowseComp (Wei et al., 2025), and BrowseComp-zh (Zhou et al., 2025), provide only test sets, which restricts their applicability for train- ing agents. Early efforts, such as Web WalkaleeQAA explored simulating human-like web navigation to generate QA pairs by constructing linear information chains. CRAWLQA within WebDancer expands simple questions to more complex ones by aggregating external information, while SailorFog-QQ within WebSailor leverages entity coreference networks to support fuzzy reasoning. These methods are predominantly information-dee -rrnmatioo-driven focusing on strategies for retrieving and connecting knowl- edge. contrast, our approach formalization-driin emphasizing the structural representation and principled modeling of the QA process.
5.2 FORMALIZATION-BASSI DATA SYNTHESIS
Recent work exploits formalization to synthesize training corpora for LLM theorem provers. DeepSeek-Mathrrover translates high-school and undergraduate competition problems into Lean4 statements, generates proofs with an LLM, and validates them in the Lean4 kernel (Xin et al., 2024). PeepSeck-MathProww
further decomposes proofs into subgoals and distills subgoal proofs into lightweight model (Ren et al., 2025). Concurrently, Leang et al. (2025) synthesize "prover-as-judee ponver-----udge" data via iterative natural-language=-foooea farmallaanguaee alignment, replacing human feedback in RI.FF and improving DPO outcomes. Goodel-Prover bootstraps sequence of successively stronger provers on a dynamically expanding Lean4 corpus (Lin et al., 2025). A parallel line applies formalization to KBQA. LACT constructs arbitrary first-order logical queries via binary-tree decomposition, yielding an SET dataset that is fine-tuned on an easy-to-hard curriculum (Xia et al., 2025). Departing from propositional FOL formalisms, our work grounds data synthesis in set-theoretic IS.
CONCLUSION
This work presents seradigm-shfftingg framework for synthesizing training data WebShaper for information- seeking (IS) agents through formalization-drive design. By establishing a set theory- based mathematical formalization of IS tasks, address eritical limitations in existing information- driven approaches that suffer from structural inconsistencies, task controllability, diversity, and coverage. The composition of proposed Knowledge Projections enables precise engineering of soning structures and complexity. Our agentic Expander module further ensures systematic expansion of formalized tasks with a layer-wise expansion paradigm, combining autonomous knowledge re- trieval and rigorous validation to minimize redundancy and prevent reasoning shortcuts. Experimental results demonstrate that WebShaper not only achieves state-of-thed performance on GAIA and Web WebWalkerQA benchmarks but also introduces controllability over task design, enabling deliberate engineering of cognitive challenges for IS agents. This formalization-driven paradigm shifts the focus from reactive information organization to proactive task specification, opening new avenues for advancing agent capabilities.
ACKNOWLEDGEMENTS
This work is supported by National Natural Science Foundation of China (92470121, 62402016), National Key R&D Program of China (2024YFA1014003), Zhongguancun Academy (Grant No.s C20250204, C20250602), and High-performance Computing Platform of Peking University.
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A DECLARATION ON THE USE OF LLMS
We affirm that the use of large language models in preparing this manuscript was strictly confined to language-related assistance, including sentence refinement and grammatical correction. All substantive content was independently authored by the authors and subsequently subjected to rigorous review and verification following any LLM-assisted edits. In conducting the experiments, LLMs were employed exclusively for legitimate academic research purposes, with no inappropriate applications. Detailed experimental settings are provided in Sec. of this paper. Beyond the aforementioned language and experimental uses, no other reliance on LLMs was involved in this work.
B ILLUSTRATION OF FORMALIZATTION-DRIN
(a) Information-Driven
(b) Formalization--riven
source
source
4
Synthesized
Organized
Synthesized
Formalization
Information
Data
Data
Figure 6: Data synthesis paradigm shift from information-driven formalization-driven "Source" stands for information sources such as the internet and databases. "Data" represents the synthesized QA data. (a) Previous methods retrieve and organize collected information in advance, then synthesize data according to the information structures. (b) Our method establishes the task formalization first, then collects information, and synthesizes QA data based on the formalization.
Compared with the traditional information-driver paradigm (Figure 6(a), which first collects raw information from various sources (e.g.. the Internet and databases), organizes it into structured forms, and subsequently synthesizes QA data according these information structures, our formalization- driven paradigm (Figure 6(b) begins by establishing formalized representation of the target task (e.g., logical or symbolic specification). Guided by this formalization. we then acquire relevant information from sources and synthesize QA data directly in alignment with the established formal specification. This shift paradigm emphasizes precise task modeling prior to information retrieval, enabling more controlled and consistent data generation.
Our data synthesis framework presents a foundational methodology for constructing training data for intelligent agents, featuring two key innovations: task formalization and agent-driven synthesis. By explicitly modeling tasks as structured, formal representations and leveraging proxy agents to synthesize data, this work provides systematic approach to address the critical challenge of generating training data that transcends the complexity and unpredictability of naturally occurring human-centric environments. Below, discuss the broader implications for agent research.
Implications in Agent Training Data Synthesis Traditional approaches to training agents often rely datasets derived from -eenertde interactions, which are inherently limited in diversity, scalability, and controllability. We emphasize that effective agent training requires explicit formal- ization of task structures prerequisite for achieving precise control over data properties. By decoupling task definitions from data generation, the framework enables:
Targeted Complexity Management: Tasks can be systematically parameterized to adjust difficulty, modality, or compositional structure, ensuring agents are exposed to controlled gradients of challenge. This contrasts with ad-hoc methods that risk overfitting to biases in natural data or failing to stress-test edge cases.
Quality Assurance: Formal task models act "specification" for data synthesis, reducing noise and ensuring consistency. This is critical for applications where reliability and safety are paramount, such autonomous systems medical AI.
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Scalable Data Generation: Agent-driven synthesis eliminates the need for laborious manual annotation or couristic-based pipelines by directly translating formal task representations into training instances. This reduces computational overhead while preserving fidelity to the task's intended design.
Implications for Al Research and Development Our architecture provides insights for advancing Al systems:
Beyond Human-Leve. Complexity: By formalizing tasks independent of human behavioral priors, the framework enables training data to exceed the implicit constraints of natural data. This opens pathways train agents for domains requiring superhuman reasoning (e.g.. advanced scientific modeling, combinatorial optimization).
Cross-Domain/ Generalization: Formal task representations abstract away domain- specific noise, allowing agents to learn invariant principles applicable across diverse contexts.
C
PROOF OF PROPOSITION 1
Here we provide formal proof of Proposition 1.
Proof. Let be element of R(S;) U R(S2). By Equation 1, there exists either a y1 E S1 such that (31,z) E R or (I) E R,
E S₃ such that (y2, E R or (z, W2) E R. Consequently. there exists y S1 S2, e.g., /1 or y2, such that (y.x) x) or (z.y) R. Thus, we have E R(SI U S2), and hence R(S)) U R(S2) R(S, US2).
an element R(1 U.)... Then there exists a E S U.S such that (y.2) E or (2,8) R. If y E S1, then R(S)): ): if Therefore, R(S1 US2))= C R(Si) U R(S2).
Conversely,
S2, then = E R(22). In either case, E R(S:)UR(S:).
Combining both directions, conclude that: R(Si) U R(S2) R(SI US2).
Thus, we end proof of the Proposition.
D DATA STATISTICS
We analyze domain distributions of our dataset. The domain distribution of our dataset demonstrates rather comprehensive coverage across multiple the- matic areas, as visualized in Figure 7. Our construc- tion of seed tasks leads to questions about various topics and entities. Our agentic expansion further strengthens these benefits. The dataset achieves sig- nificant diversity through its balanced representation of major domains such as Sports, Politics, and Entertainment.
Other
5%
Zq
Sports 21%
Culture
Medical
Literature 12%
History
This deliberate design ensures our dataset not only avoids over-reliance on any single domain but also maintains sufficient sample density across diverse topics. The empirical balance between breadth and depth enables robust training of a donain-agnostic information -eeatiinn-eeeinnn agent. Such characteristics po- sition dataset particularly suitable for train multi-domain IS tasks and fostering interdisciplinary research.
Technology
Academic 17%
Economic
Entertainment 13%
Politics
Figure 7: Domain distribution.
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E
COMPARED DATASETS
We compare our synthesized dataset with several datasets:
WebWalkerQA employs random walks over interlinked URLs to synthesize questions based on the visited webpages (Wu et al., 2025b). The dataset includes both single-source questions, generated from a single visited URL, and multi-source questions, which are constructed using information aggregated from multiple visited URLs.
E2HQA is a dataset introduced by WebDancer (Wu et al., 2025a), where simple questions are systematically rewritten into more complex, challenging ones.
MHQA is a composite dataset that integrates existing single-hop and multi-hop question- answering datasets. The majority of the questions are annotated by humans.
F AGENT TRAINING
To train our information-seeking agent, similar to WebDancer (Wu al., 2025a), implement supervised fine-tuning (SFT) followed by reinforcement learning (RL).
In SFT, given trajectory in a sequence of tokens T (T1,01, 01, Tax On on), we mask loss from observation leading to loss:
m
L
[a, o) log no (2 x<e))
EMG Thi E
where To is the model to be trained. Later in RL, we further optimize based on the GRPO algorithm (Shao et al., 2024). For question-answer pair (g, a), sample rollouts {u:) and
|G]
update the policy model by:
I(0)= E (w: 1 - (
M
Ei=n lyvel ΣΣ min (ree(0)Ar.e clip (O;,, 1 Stow: thigh A.t))
(9)
Ti.j (0) = To (o: 9is 0s,21)
Ai. R4 - -eann(((a)))))
Toa.a
(o;
qis 0s,c1)²
std((R.))
where contert includes all the model completions and tool responses. is the clipping range of the importance sampling ratio (0). As, is an estimator of the advantage of the i-th rollout at f-th step.
G AGENT DETAILS
Following Wu et al. (2025a), WebShaper uses two tools, search and visit, which are regarded as fundamental the information seeking process (Zhu al., 2025):
Search interfaces with the Google search engine to retrieve relevant documents given natural language queries. It supports multiple queries in parallel and returns the top- 10 results for each query, where each result includes a title, a snippet, and the corresponding URL.
Visit enables targeted extraction from specific web pages. Each page is paired with a designated visit goal. The full content of the page is first retrieved using Jina (Jina.ai, 2025), after which a summarization model (Qwen-2.5-72B in our implementation) extracts information relevant the specified goal.
H TRAINING DETAILS
H.1 SFT
For SFT, we use a batch size of 32 and a learning rate of 5e-6, warmup plus cosine decay schedule. We also apply weight decay of 0.1.
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H.2 RL
For RL training (Sheng et al., 2025), each group consists of 8 rollouts. The temperature is 1.0, topp 1.0, the batch size is 128, the mini batch size is 32, and the learning rate is le-6.
H.2.1
CASE STUDY
Question Natural Language
What the title the section, where the section is written author who also authored scholarly article analyzing contact between Medieval Norse and Native North Americans published reviewed archaeology journal, which additionally published another article that analyzes Lake Mohave artifacts and Pleistocene lake levels?
Question:
Answer: Thule Prehistory Canada.
Question Formalization
Question In Graph
Dvax, hasTitle, VeTI, [Vex, writtenBy, VgYl,
[veY, hasAuthor, vek], [vek, publishIn, vall, [van, publish, VeH], [VeK, mmalyzecontactBetteen. CoMedieval Norse and Native North Ane Anericans), [VeN, isA, CBA peer-reviewed archaeology journal], IVEN, analyze, CaLake Nohave artifacts and Pleistocene level]
Tarpet Variable Constan
Figure 8: Case studies of our synthesized data. We show question in natural language, formalization, and graph respectively.
We present representative case study in Figure 8. Compared with linear structure and sequential structure, our synthesized data has no problems of redundancy and reasoning shortcuts. The model should strictly seek information and reason alongside all the variables to find the answer. There are no constants directly connected to the target variable or variables close to it. Besides, there are no constants connected to other constants.
Moreover, R-Union effects well in our data. The underlined FP is a summarization of distributed web contents, leading to more difficulty in resolving the variables K, N,, and M. Benefiting from the formalization, our data contains a variety of IS forms, which can fully stimulate the different IS capabilities of the model.
Question In Natural Language
"Strange Stories from Chinese Studio" collection classical Chinese short stories written by the Qing Dynasty novelist Songling. earliest manuscript copies were already in circulation during the Kangxi reign the Qing Dynasty, and the collection comprises over four hundred short stories total. In Volume Ten "Strange Stories from Chinese Studio," there is story titled "The Green-Clothed Girl," In this story, how many sentences did the scholar Yu Jing speak with her?
Question:
Question Formalization
Question In Graph
Icasscs, isA, CgClassic storyl, [casscs, writtenBy, C@Pu Sangling], [casscs, inCirculation, ceging Dynastyl, [oasscs, comprises, Ggever 480 storiesl, [Vax, isInTenValume casscs), [VaX, isTitled, ceThe Green-Clothed GirlS), [Vex, hasSentences, иeт), [VaT, happenedBetween, G@Yu The Green-Clothed Girt]
Target Vanaable Constant
Figure 9: Case comparison. "SSCS" stands for "Strange Stories from a Chinese Studio".
We compare representative example shown by KIMI-Researcher (Kimi, 2025), illustrated in Figure 9, The case includes redundant information, such as multiple constants connected to "SSCS", which contribute little answering the question. Additionally, reasoning shorteut is observed that directly connects to the target variable. Despite the apparent complexity. the underlying reasoning structure is relatively simple, consisting of single-hop reasoning path.
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Table SFT Data Comparison GAIA benchmarks. The best results among all backbones are in bolded.
GAIA
Backbone
Dataset
Level
Level
Level
Avg.
WebWalkerQA
43.5
30.7
0.0
32.0
E2HQA
56.4
36.5
0.0
39.8
Qwen-2.5-32B
MHQA
43.5
36.5
8.3
35,9
WebShaper
56.4
40.3
16.6
43.6
WebWalkerQA
53.8
36.5
0.0
38.8
E2HQA
61.5
38.4
16.6
44.6
Qwen-2.5-72B
MHQA
56.4
44.2
0.0
WebShaper
56.4
48.0
0.0
45.6
WebWalkerQA
66.6
38.4
8.3
45.6
E2HQA
58.9
42.3
16.6
45.6
QwQ-32B
MHQA
51.2
44.2
0.0
41.7
WebShaper
69.2
50.0
16.6
53.3
I DETAILED DATA COMPARISON RESULTS
As shown in Table 3, our proposed WebShaper method consistently achieves the highest average performance across different backbones on the GAIA benchmarks. In particular, WebShaper out- performs other datasets in most settings, with the best results highlighted in bold. For example, with the Qwen-2.5-322 backbone, WebShaper achieves an average score of 43.6, surpassing com- peting datasets by a significant margin. Similarly, for Qwen-2.5-72B and QwQ-32B backhones, WebShaper reaches 45.6 and 53.3 respectively. demonstrating strong generalization capabilities across model sizes and difficulty levels (Level Level 2, Level 3). These results clearly highlight the robustness and superiority of our approach in handling diverse and challenging evaluation settings.
18


Reviews:
[{"review_id": "x0Z5dMd1wx", "reviewer": "Reviewer_8FwL", "summary": "The paper presents WebShaper, a framework for synthesizing high-quality training data for information-seeking (IS) agents. Unlike existing information-driven approaches that retrieve text first and then generate questions, WebShaper adopts a formalization-driven paradigm, starting from a structured definition of the IS task using set-theoretic constructs. Its core unit, the Knowledge Projection (KP), represents entity relations and can be composed through Intersection and R-Union to form complex reasoning tasks. A multi-step Expander agent progressively builds valid and diverse questions while avoiding redundancy and reasoning shortcuts. Models trained on the WebShaper dataset achieve state-of-the-art performance on GAIA and WebWalkerQA, showing improved reasoning consistency and generalization over prior methods.", "questions": "+ How does the formalization handle IS tasks that are difficult to express in set-theoretic form, such as comparative reasoning , temporal reasoning beyond filtering, or counterfactual reasoning？\n+ Could the authors provide an estimate of the computational cost of generating the WebShaper dataset (e.g., time, number of API calls, GPU hours)? How does it compare to the information-driven baselines it outperforms?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "+ The paper presents a meaningful shift from an information-driven to a formalization-driven approach for data synthesis, directly addressing the inconsistency issues observed in prior methods.\n+ The formalization based on Knowledge Projections (KPs) and set operations provides fine-grained control over both the reasoning structure and task complexity of synthesized data.\n+ The Layer-wise Expansion Strategy effectively tackles redundancy and reasoning shortcuts common in previous synthesis frameworks.\n+ Experiments on GAIA and WebWalkerQA benchmarks demonstrate sota performance, reflecting the utility and quality of synthesized data.", "weaknesses": "- The synthesis process—spanning seed generation, multi-agent expansion, and online retrieval—appears computationally expensive. A quantitative comparison of cost (e.g., API calls, runtime, or compute hours) against traditional information-driven synthesis methods would clarify its practical feasibility.\n-  The current set-theoretic grammar (KPs, R-Unions, and Intersections) may not sufficiently capture complex real-world IS tasks, such as those involving temporal, comparative, or counterfactual reasoning.\n-  The paper omits a “Limitations” section, leaving readers uncertain about the known weaknesses, failure cases, or scalability challenges of the proposed method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "szoTFUq0Pm", "reviewer": "Reviewer_GPnM", "summary": "The paper presents a dataset generation approach who's output can be used to\ntrain information seeking agents via the SFT and RL paradigms. The key of the\nmethod lies in its formalization, essentially a blueprint for complex questions\nwhich the method can generate from a simple seed question though expansion. The\nresulting trajectories are then used for training. Their WebShaper model\nachieves the best performance among all information seeking agents on two\npublic benchmark datasets.", "questions": "Q1: Is the dataset and code available? You claim that the method is open-source, but I could not find any supplementary material.\n\nQ2: Can you quantify the training data generation effort with WebShaper compared to traditional methods?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "S1: Novel training data construction method that improves over previous \"information-driven\" paradigms.\n\nS2: Method improves on performance compared to existing IS agents.\n\nS3: WebShaper's training data is more effective compared to the output of other\ntraining data generation methods.", "weaknesses": "W1: It seems the created dataset itself is not available. This would have been a very valuable contributions for the community. The same goes for the code, you claim it is open-source, but it was not provided in the supplement.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0sdawDV60U", "reviewer": "Reviewer_9X3r", "summary": "This paper addresses the lack of training data for information-seeking agents by proposing WebShaper—a formalization-driven data synthesis framework that formalizes search tasks within a set-theoretic framework. WebShaper introduces Knowledge Projection as the fundamental unit to represent complex IS tasks systematically. During data synthesis, WebShaper employs a layer-wise expansion strategy and uses an Expander agent to iteratively generate and validate questions, producing 5K high-quality trajectories for SFT and RL training.", "questions": "1. According to Table 2, QwQ-32B's SFT performance is identical to the post-RL result. Why does QwQ show no improvement after RL? \n2. Figure 3a compares Formalization (FL) vs. Natural Language (NL), and Figure 3b compares Layer-wise (G) vs. Sequential (S), but lacks a baseline that removes both components simultaneously (i.e., the combination of NL + Sequential). This baseline is essential for understanding the independent contributions and interaction effects of the two components.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paradigm shift from \"information-driven\" to \"formalization-driven\" demonstrates originality and theoretical depth. \n2. The paper presents a complete pipeline: the KP representation, layer-wise expansion strategy, and Expander agent collectively implement a closed loop of autonomous data generation and quality assurance, demonstrating engineering rigor.\n3. The paper validates WebShaper's performance across multiple backbones. Ablation studies across various models and additional analyses confirm WebShaper's practical utility and generalizability.", "weaknesses": "1. The paper suffers from serious writing and data consistency issues, with multiple conflicting details: (1) Content misalignment between Sections 4.4.3 and 4.4.4; (2) Inconsistent key performance data: Qwen2.5-32B's SFT performance is reported as 44.66 in Figures 3 and 4, but 43.6 in Table 2; Qwen2.5-72B similarly changes from 46.66 to 45.6. Such conflicts undermine the paper's rigor.\n2. The paper lacks evaluation on benchmarks such as BrowseComp and XBench, limiting fair comparison with concurrent work. Notably, BrowseComp and BrowseComp-zh are explicitly mentioned in the Introduction, but experiments are completely absent.\n3. WebShaper's Visit tool uses Qwen-2.5-72B for content summarization, while baseline methods may not have this design. Part of the performance gains may be attributed to tool design rather than data quality.\n4. While the paper demonstrates the advantages of formalization, certain IS tasks may be difficult to express, such as those that require logical or causal reasoning in GAIA.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BXxsJDHxhM", "reviewer": "Reviewer_Dfzz", "summary": "This paper introduces WebShaper, a formalization-driven framework for synthesizing training data for information-seeking agents. Unlike existing information-driven methods such as WebDancer, it defines IS tasks through a set-theoretic formalization based on knowledge projections, and uses these to guide the synthesis process. A key innovation is the Expander agent, which autonomously performs multi-step, layer-wise expansions of seed tasks by retrieving and validating information from the Web, ensuring consistency between reasoning and information structures.", "questions": "1.\tHow robust is the KP formalization to noisy or incomplete retrieval results? Does (or to what extent) the model degrade under imperfect web data?\n\n2.\tCould you provide a quantitative evaluation of synthesized question quality (e.g., factual accuracy or diversity)?\n\n3.\tTo what extent can WebShaper be generalized to non-English or multimodal information-seeking tasks?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1.\tThe paper introduces a set-based formalization of IS tasks with knowledge projections and compositional operations (Union, Intersection), which provides a clear structure for reasoning.\n\n2.\tThe Expander module is well-motivated and technically detailed, by embedding the Thought–Action–Observation loops and specialized tools including Search, Summarize, and Validate.\n\n3.\tThe experiments are comprehensive, showing consistent improvement across model scales and benchmarks.", "weaknesses": "1.\tThe paper is not self-contained enough, with multiple components (e.g., the WebDancer framework, the QwQ model) depending on specific infrastructures, which limits understanding of general audience and reproducibility. \n\n2.\tThe paper’s notation-heavy exposition, especially in Section 2, hinders accessibility for non-expert readers. The figures and tables, though informative, are dense and underexplained, and the text occasionally assumes familiarity with prior systems like WebDancer.\n\n3. The paper does not discuss potential risks in synthetic data generation (e.g., bias propagation, fact inconsistency, or data contamination). An explicit acknowledgment of these issues and their mitigation strategies would strengthen the credibility and responsibility of the work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)