Empowering Efficiency and Efficacy in WebAgent via Enabling Info-Rich Seeking
Authors: Zhengwei Tao
Haiyang SHEN
Baixuan Li
Wenbiao Yin
Jialong Wu
Kuan Li
Zhongwang Zhang
Huifeng Yin
Rui Ye
Yong Jiang
Pengjun Xie
Fei Huang
Jingren Zhou
Wentao Zhang
Yun Ma
Zhiqiang Gao
Venue: ICLR 2026 Poster (2026)
Keywords: agent
information seeking
data synthesis
llm
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Large Language Model (LLM)-based agents have emerged as a transformative approach for open-ended problem solving, with information seeking (IS) being a core capability that enables autonomous reasoning and decision-making.  While prior research has largely focused on improving retrieval depth, we observe that current IS agents often suffer from \textit{low search efficiency}, which in turn constrains overall performance. A key factor underlying this inefficiency is the sparsity of target entities in training tasks, which limits opportunities for agents to learn and generalize efficient search behaviors. To address these challenges, we propose WebLeaper, a framework for constructing high-coverage IS tasks and generating efficient solution trajectories. We formulate IS as a tree-structured reasoning problem, enabling a substantially larger set of target entities to be embedded within a constrained context. Leveraging curated Wikipedia tables, we propose three variants for synthesizing IS tasks—Basic, Union, and Reverse-Union—to systematically increase both IS efficiency and effectiveness. Finally, we curate training trajectories by retaining only those that are simultaneously accurate and efficient, ensuring that the model is optimized for both correctness and search performance. Extensive experiments conducted on five IS benchmarks—BrowserComp, GAIA, Seal-0, WideSearch, and xbench-DeepSearch—demonstrate that our method consistently achieves improvements in both effectiveness and efficiency over strong baselines.

Published as a conference paper at ICLR 2026
EMPOWERING EFFICIENCY AND EFFICACY IN WE- BAGENT VIA ENABLING INFO-RICH SEEKING
Zhengwei Tao T 13,4,
Haiyang Shen 1,
Baixuan L
Wenhiao Yin
Jialong Wu 1.,4
Kuan Li
Zhongwang Zhang* Jingren Zhou"
Huifeng Yin*
Rui Ye
Yong Jiang Zhiqiang Gao
Pengjun Xie
Fei Huang'
Wentao Zhang
33.
Yun Ma if
PPeking University
Southeast University wentao. zhangêpku. edu. cn
ZZhongguaneun Academy *Togyyi Lab, Alibaba Group mayunêpku. edu. cn
zqgaoêseu. edu. cn
Equal Contribution
'Corresponding Author
ABSTRACT
Large Language Model (LLM)- agents have emerged as a transformative approach for open-ended problem solving, with information seeking (IS) being a core capability that enables autonomous reasoning and decision-making. While prior research has largely focused on improving retrieval depth, we observe that current IS agents often suffer from low search efficiency, which in turn constrains overall performance. A key factor underlying this inefficiency is the sparsity target entities in training tasks, which limits opportunities for agents to learn and generalize efficient search behaviors. To address these challenges, we propose WebLeaper. framework for constructing high-coverage IS tasks and generating ef- ficient solution trajectories. We formulate IS as tree-structured reasoning problem, enabling a substantially larger set of target entities to embedded within a con- strained context. Leveraging curated Wikipedia tables, we propose three variants for synthesizing IS tasks Basic, Union, and Reverse-Union-2e systematically increase both IS efficiency and efficacy. Finally, we curate training trajectories by retaining only those that are simultaneously accurate and efficient, ensuring that the model is optimized for both correctness and search performance. Extensive experiments conducted on five IS Borehaaarrk--BBoserooonnee GAIA, Seal-O, WideSearch, and Seench-DeeeSeeee achieves improvements in both effectiveness and efficiency over strong baselines.
demonstrate that method consistently
INTRODUCTION
Relative Occurrence Density
The LLM -aaeddaddaae agents mark paradigm shift in AI, delivering transformative solutions to chal- lenges once deemed intractable across diverse domains (Guo et al.; 2024; Ye et al., (023).
Among their core capabilities, information seek- ing (IS) plays crucial role in enabling the cog- nitive autonomy of these agents. This ability only drives their adaptability in open-ended tasks but also underpins a new generation of powerful commercial systems, including Ope- nAI Deep Research (OpenAl 20255), Google's Gemini Gemini 2025). and Perplexity Al (Per plexity
0.2
0.8
Valid Action Rate
Figure 1: The distribution of valid actions of the agent based on the GPT model on our synthesized IS task. The valid actions are those seeking the correct target entities required by the question.
2025). Kimi- Kimi-Researcher (Team 2025).
While numerous studies have sought to enhance the capabilities of agents through complex question-answering pipelines and advanced fine-tuning strategies (Wu et al. 2025a Li et al. [2025c]b] b) Tao et al., 2025) Qiao al. 2025 Lu et al., 2025), most existing approaches primarily concentrate on improving the search depth, giving comparatively little attention to search efficiency. Our preliminary experiments indicate that current LLM- M-based agents search inefficiently. As shown in Figure I the distribution of valid actions for competitive IS agent peaks around 0.04, meaning that in most cases, only a small fraction of actions are effective (Wong et al., 2025 Xue et al.) (2025). This low valid-action rate reflects suboptimal search behaviors, including redundant query reformulations,

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Published as a conference paper at ICLR 2026
retrieval of irrelevant information, and unnecessarily long search chains. Such inefficiencies not only increase computational and time costs but also limit the agent's 's overall IS performance.
The design of synthetic training tasks incurs this inefficiency. In typical IS agent setups, the agent begins with a set of known entities and incrementally gathers information to infer all target entities. However, prior work often constructs tasks in which the target entities are overly sparse (Wu et al.. 2025a et et al.) 2025c b). Such sparsity limits the agent's 's exposure to informative cues, reducing opportunities to learn locate relevant information within a constrained context window. As result, the agent spends more actions processing irrelevant content, weakening its search strategies, leading lower performance. Furthermore, can bias the measurement of search efficiency, which we prove in later section. This bias makes it difficult to obtain an accurate training signal, thereby obstructing the systematic learning of more efficient search behaviors. These limitations underscore the need to redesign training tasks, enabling optimized seeking efficiency and stronger IS capabilities.
To address these challenges, we propose WebLeaper, a framework designed with two core objectives: (I) to construct new IS tasks containing a substantially larger number of target entities; and (2) to generate solution trajectories that achieve both high accuracy and high efficiency. For the first objective, we model the IS process modates more target nodes within a limited context. Based on this formulation, we systematically increase task complexity through three dataset variants. First, leveraging curated Wikipedia tables, we synthesize Basic version, which directly addresses the challenge of entity sparsity by creating a high-density search space within single, structured source. To mirror more realistic scenarios that demand integrating information from multiple sources, our Union variant constructs tasks that require synthesizing facts across different sources, thereby increasing search ambiguity. Finally, to mitigate the risk of agents adopting simplistic, keyword-based shortcuts, the Reverse-Unin variant reverses the logical flow, compelling the agent to first deduce intermediate entities from scattered clues before completing the main search task. For the second objective, we construct comklcomnn trajectories that are filtered according to Cittrrmation-Seeiiii Rate (ISR) and Information-Sedd -Seiiinn--Seiig Efficiency (ISE), retaining only those that solve the task both accurately and efficiently. Models trained on this curated dataset yield our final IS agent.
tree-structured reasoning task, which compactly accom-
We conduct extensive experiments to evaluate our approach across five benchmarks: Browser- Comp (Wei et al. 2025), GAIA (Mialon et al. 2023), Seal-0 (Pham et al. 2025), WideSearch (Wong et al. 2025), ) and xbench- DeepSearch Sebench-Team 2025). Our method achieves consistent im- provements on all benchmarks. Ablation studies on the dataset design further confirm the effectiveness of our proposed components. We summarize our contribution as follows:
We design a new infformttion-seekine task formulation on a rrc-structured reasoning problem, leading to the inclusion of substantially larger set of target entities within constrained context.
Based on this formulation, we construct the Basic, Union, and Reverse- Union datasets.
We generate and filter task-solving trajectories using the proposed Information-Seeking Rate (ISR) and Information-Seeking Efficiency (ISE) metrics, retaining only those trajectories that solve tasks both accurately and efficiently.
We conduct extensive experiments on five public IS benchmarks, BrowserComp, GAIA, Seal-O, WideSearch, and Xbench- Deephe,r,,,,,, achieving consistent improvements over strong baselines.
2 DEFINITIONS
An Information-Seckin (IS) task challenges an agent to answer a complex natural language question by navigating a vast information space to assemble a complete set of required entities. This process is inherently sequential, involving the progressive discovery of entities, understanding their properties (attributes), and leveraging relationships between them to uncover further entities. This section formally defines the components of such a task and the metrics for evaluating an agent's performance, emphasizing the importance of identifying both final and intermediate entities in the reasoning chain.
2.1 INFORMATION- SEEKING TASK
An entity e E E is the fundamental unit of information. An Information-Seekiiie (IS) task is the process of identifying and collecting specific set of target entities from E, based on question. Formally, an IS task is a tuple: T = (g. R). where q is the natural language question and R C E is the set of the target entities that collectively satisfy the conditions posed by q-
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Critically, the required set R includes not only the final, explicit answers but also all intermediate entities that are necessary stepping stones in the reasoning process. Consider the question:
q Which player of a team in the 2004-05 season, who was born in the 1990s? This team was founded in 1966 and is an East German football team. To solve this, an IS agent must seek for information online, and find the target entity set answer: = {Robert Rudwaleit, Danny Kukulies....).
(1)
2.2 INFORMATION- SEEKIING AGENT
We focus on an InformattionSeeekinee Agent that interacts with web environment to solve an IS task T within the ReAct framework (Yao et al. 2023). The agent's operation is sequential maiiinnnmmaiing process occurring over discrete time steps f = 1..... T. At each step, the agent analyzes its current state (including the initial question and all previously gathered information), generates a thought for planning its next move, executes tool-based action to seek new information, and receives an observation from the environment. This entire process captured in the agent trajectory is defined as
Hr (4, T1, (K1, 01. T2, Q22 .2..... TT, Orp; or), where Tú is the planning thought, a; is the seeking action, and og is the resulting observation at step i. At the end of the process, the agent has obtained set of entities O E, which is the union of all unique entities discovered across all steps.
(3)
2.3 QUANTIFYING INFORMATION COLLECTION AND EFFICIENCY
To guide an agent towards successfully solving IS tasks, its performance framework must value the entire reasoning process, not merely the final output. Our central thesis is that by explicitly quantifying the value of all required information discovered, we can create a stronger signal for learning effective search strategies. To this end, we define principles formalize the performance (the total information gain) and the efficiency (the gain per action) of the agent's 's collection process.
Information-Seeking Rate (ISR) Recall that R denotes the set target ground-truth entities for the task, with cardinality = | O is the set of entities actually obtained by the agent during its operation. The intersection R O therefore contains all required entitics that were successfully retrieved. The information collection rate directly measures the fraction of required entities successfully obtained by the agent:
RNO Rno
ISR
|R
ISR E [o, 1], and higher values indicate more thorough coverage of the required information.
Information-Seeking Efficiency (ISE) While ISR measures completeness, the information collec- tion efficiency reflects the average number of action steps to discover the target entity:
n
ISE
(5)
T
where T' is the total number of steps of the solving trajectory. Higher ISE implies greater IS efficiency. The stability of measuring ISE is important for providing unbiased training signals.
Proposition 1 (Variance of ISE). Let X, denote the number of steps the agent takes to discover the i-th new entity in R. Therefore ISE = 7 5m X. Assume XI...., n be i.i.d. random variables
with finite mean μ >> 0 and finite variance a². X, >>> O almost surely, then:
Var(ISE) = o
(6)
This proposition shows that as the number of target entities n grows, measuring ISE becomes more stable and reliable performance metric. The detailed proof provided in Appendix
METHOD
To enhance the information efficiency of the IS agent, our approach trains the model on calibrated task = (q. R) together with the corresponding task-solving trajectory H. In prior IS agent training setups, the dataset typically contained only limited number of target entities (R). This design substantially restricts the potential improvement in information-seeking efficiency and, in turn, limits the agent's overall capability. The limitation incurs two problems:
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this tree structure for its compact and Published as a conference paper at ICLR 2026
Question Entities
Target Entities
Fuzzed Question Entities
Version 1: Basic
Version 3: Reverse Union
Example:
Primary Key
Table Title
Who the Nobel
Prize
Literature between
1980 and 1990?
include name,
country, award year and gender.
Properties
Fuzz
Version 2: Union
Example:
Example:
Who are the authors from the same country as the 1980s prize winner that wrote a novel about group of Brilish boys stranded on an uninhabited island. and who have also wan both this reward and the Booker Prize? for each them. what their respective years they
Which authors have won
both the Nobel Prize
Literature and the Booker
their nationality and
Prize? For each, provide
OO O0 O
year they the
country, and
Find subtrees with common set of relations
An overview of WebLeaper. The reasoning structure modeled as a tree. A root entity (question entity) connects to a set of second-layer entities. (a) Version-I (Basic) constructs simple reasoning tree from single information source. (b) Version-II (Union) creates a complex task by finding a maximal union between two trees that share common set of relations within their subtrees (e.g., both have has nationality). It provides fuzzed clues (third-layer entities) as question entities, forcing the agent to first deduce second-layed anchor entity (an entity from the second layer), then other relevant subtrees.
Figure 2:
(c) Version-Ill (Reverse-Uniee
reverses the reasoning process.
With small volume of Rt it is difficult train the agent to retrieve information efficiently within limited context length.
Our method relies on measuring the information-sedt seekiigg efficiency ISE. As shown in Eq. (6) small set of target entities introduces measurement bias the ISE metric.
To overcome these shortcomings, introduce WebLeaper, novel data synthesis framework specifi- cally designed to boost ieformmtiionseekiing efficiency. Our method consists of two main components: (I) QA synthesis pipeline for generating calibrated tasks, and (2) trajectory construction process for producing realistic task-solving sequences. We describe the QA synthesis pipeline and trajectory construction process in detail in the following subsections. For detailed walkthroughs of the examples for each synthesis version, please refer to Appendix A.8
3.1 ENTITY-INTENSIVE TASK SYNTHESIS
3.1.1 VERSION-I: Basic
In an information-seeking task, the reasoning structure matters. We use tree, denoted as Tü, to represent this structure, where nodes are entities and edges are relations between them. The IS agent must start with some known entities in the tree and reason along the edges to determine the target ones. To incorporate many target entities possible, hierarchical organization.
Synthesizing such task T = (q, R) requires large volume of relevant entities, which is non- trivial. Following the one-entity-ll-a-iine collection strategy of prior work is prohibitively expensive. Therefore, we exploit the structured tables contained in Wikipedia articles, which encapsulate rich relational information. These tables naturally provide groups of entities connected by specific relationships, enabling us to efficiently construct the reasoning tree T. We crawled approximately million tables from Wikipedia and applied multi-stage cleaning procedure, retaining only large, well- formed, and structurally homogeneous tables. The detailed data cleaning procedure and construction rationale described in Appendix A.6
To construct the reasoning structure illustrated in Figure a), we populate its layers using information from single table. The entities extracted from the table title form the root of the tree (i.e., the question entities). Next, we employ an LLM to select the most representative, non-redundant column of values from the table ttypiclllyl the primary key- as the second-layer entities (e.g., "Czestaw Mitosz'), ). An edge between the root entity and a second-layer entity indicates that the table contains this entity. The third-layer entities are derived from the remaining columns of the table, with their
2[a)
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values representing attributes of the corresponding second-layer entity (e.g., "country: Poland', 'year: 1980'). In this layer, an edge signifies that the second-laye -laer entity possesses the given property defined by the third-layer entity.
Each second-layer entity and its associated third-layer entities form a subtree, which we denote as S.,j. These subtrees, each possessing set of relations Re((S..)) that connect its layers, represent cohesive units of information (e.g. a specific laureate and all their details). The full reasoning tree T; is thus composed of set of such subtrees {. The question provides the root entities, while all entities in the subtrees (both second and third layers) constitute the final answer. The detailed construction process and the required reasoning path for the example task are explained in Appendix A.8.1
Notably, we use Wikipedia primarily for two reasons. First, it is a large, comprchensive, widely-used encyclopedic resource with broad domain coverage. Second, its semi-structured tables offer rich, clean, reliable relational data-ideal for entity-intensiv task synthesis- sufficient quality and diversity to build various complex meaningful tasks.
3.1.2 VERSION-II: Union
While effective, the reasoning structure of our basic tasks is derived from single sources, limiting their structural complexity and the scope of questions we can pose. To address this, we aim to construct tasks with more intricate reasoning structure that spans multiple information sources by uniting reasoning trees from our Basic version that share similar themes and structures.
To generate more challenging questions, we propose uniting reasoning subtrees in Basic version that share similar themes and structures. A naive approach, such as randomly combining subtrees, often results in semantically incoherent questions. To systematically discover the most substantial integration opportunities, our approach models this as a Union operation, which identifies multiple reasoning trees whose respective subtrees share some common relations.
The primary challenge is to systematically search the entire collection of trees to find all groups that suitable for union. To avoid combinatorial explosion from enumerating all possible combinations, we develop an algorithm to efficiently discover only maximal unions. This problem is formally modeled as Maximal Biclique Enumeration (see Appendix A.7). which effectively identifies groups of reasoning subtrees and their shared subtree relations.
As illustrated in Figure 2 ()) the reasoning trees for "Nobel Prize in Literature laureates" and "Booker Prize winners" both contain subtrees where second-layer entities (authors) are connected to third-layer entities via relations like 'has nationality' and 'has name'. Our method identifies this shared subtree structure. Relations not shared across all sets of subtrees, such as "has_ 'has_gender" (present only in the Nobel tree), are discarded during the union.
Once a maximal union is identified, we leverage an LLM to synthesize question based on the common features of the selected subtrees. For instance, the question "Which authors have won both the Nobel Prize in Literature and the Booker Prize?" requires identifying the two sets of laureates as intermediate 'Target Entities' and then finding their intersection to produce the final "Target Entities'. The complete walkthrough is in Appendix A.8.2
3.1.3 VERSION-III: Reverse-Union
While the Union method generates complex, multi-source tasks, a vulnerability remains: an agent could solve the query and use direct keyword searches on the constituent sources (e.g., search 'Nobel Prize winners," then 'Booker Prize winners'), ). This approach circumvents the intended synthesis of information, reducing the cognitive load and failing to stimulate true reasoning capabilities similar to WebSailor (Li et al. 2025c). To address this, we introduce Reverse-Union, paradigm designed to enforce more robust cognitive workflow by reversing the standard reasoning flow. As illustrated in Figure this method combines two stages to construct challenging task:
Deductive Fuzz: This stage implements the fuzz by defining the 'Question Entities' as set of descriptive third-layer entities. Instead of being named directly, a central 'anchor' entity (an entity from the second layer) is described through its corresponding third-layer entities. In the example, the description "the 1980s grize-winner that wrote a novel about a group of British boys stranded uninhabited island" serves as clues in the form of 'Question Entities'. An agent must first deduce from these clues to identify the anchor entity, 'William Golding'.
Unson-based Search Construction; After fuzzing the anchor, this stage constructs the expansive search part of the task, ensuring the anchor serves only bridge to the final answer. To achieve
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this, we first select specific third-layer entity from the anchor's subtree (e.g., his country) to act as a pivot. We then formulate the remainder the question to compel an agent to use this pivot to launch search across the unified trees. The final Target Entities are thus defined as the set of second-layer entities that share this pivot attribute (i.e., are also British) and satisfy the original intersection condition (i.e., winning both prizes).
By structuring tasks this way, Reverse-Union prevents agents from succeeding with simple keyword searching and mandates a more robust, multi-step reasoning process. The detailed process of question generation and the required reasoning path are explained Appendix A.8.3
3.2 INFORMATION GUIDED TRAJECTORY CONSTRUCTION
After synthesizing the task, this section elaborates on the construction of task-solving trajectories. As shown in 33, our agent solves a task within the ReAct framework (Yao et al. (0033). We equip the agent with the following tools:
Search This action enables the agent to conduct Google search by several queries. The parame- ters of this tool are filteer,,iter___rrrrr would return the top relevant URLs and their snippets as the observation.
enabling temporal filtering of search results. This tool
Visit This action enables the agent to visit multiple URLs. The parameters of this tool are [urls, goal). This tool would return the summarized visited paragraphs as the observation.
After generating large of trajectories by executing constructed tasks with an open-source model, we apply a filtering procedure to select high-quality examples for training. Our goal is to retain trajectories that demonstrate both accuracy collecting the required entities and efficiency in the use of actions, in accordance with the metrics defined in Section 2.3 Specifically, we impose the following selection criteria:
Coverage Criterion. We require that the trajectory achieve sufficient completeness in information collection. Formally, we keep only those trajectories whose ISR satisfies ISR. a, where a is predefined coverage threshold. To compute ISR, we accumulate the obtained target entities in all actions. We compute ISR as Eq. (4).
Efficiency Criterion. We further require that the trajectory maintain high efficiency in discovering useful entities. This translates into selecting those trajectories whose ISE satisfies ISE > B, where is predefined efficiency threshold. For ISE, we accumulate the obtained target entities in Visit actions. The reason for not including Search ISE that we observe entities found in Search are less precise and would be updated by the following Visit action. We compute ISR as Eq.
Through this filtering process, we ensure that the retained trajectories are both accurate in acquiring the target entities and efficient in their action usage, providing strong supervision signals for training agents to perform precise and effective information--eekingg This efficiency-orrented filtering is effective because it selects trajectories with high valid action density to teach focused planning and reduce redundancy, while our entity-intensioe tasks provide sufficient high-quality trajectories and stabilize ISE (Var(ISE) (+)) as reliable signal, forming a mutually reinforcing cycle.
4 EXPERIMENTS
4.1 SETUP
Benchmarks We conduct extensive evaluations our method on five challenging QA benchmarks that demand complex information-seeking capabilities, namely BrowseComp (Wei et al. (025). GAIA (Mialon et al. 2023). xbench- Deepeeh (xbench-DS) (Xbench-Teane et al. 2025), and WideSearch (Wong et al.) 2025). For GAIA, adopt the 103 103-sample text-only validation subset (Li et al. 2025d). while for all other benchmarks, we utilize their complete test sets.
2025). Seal-O (Pham
Baselines We select representative set of mainstream and competitive information--sekkng agents as our baselines, including proprietary agents Silaude-4-Sonne (Anthropic) (025). OpenAI-o3 (OpenA] (025a)) OpenAI DeepResearch (OpenAl (025b)) and open-source agents (ASearcher (Gao et al. 2025). DeepDive (Lu et al. 2025). DeepDiver-V2 (Team).
MoThinker (Team et al. (2025)), Kim KK (Team al. 2025a). WebExplorer (Liu al. 2025
WebDancer (Wu et al. 2025a), WebSailor (Li et al. 2025c), WebShaper (Tao et al. (025)).

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Published as a conference paper at ICLR 2026
Table 1: Main results on multiple benchmarks. All benchmarks except WideSearch report Pass@1. WideSearch reports Success Rate (SR), Row F1, and Item F1. Bold scores indicate the highest values among all open-source agents.
WideSearch
Model Framework
BrowseComp
GAIA
xbench-DS
Seal-0
SR
Row F1
Item
Proprietary Agents
Claude-4-Sonnet
12.2
68.3
64.6
2.3
31.7
57.9
Ope
49.7
70.5
66.7
18.9
4.5
34.0
52.6
OpenAI DeepResearch
51.5
67.4
Open- SperreSuure Agents
ASearcher-Web-328
5.2
52.8
42.1
DeepDive-328
14.8
50.5
Deep0iver-V2-388
13.4
53.0
MiroThinner-332--PP0--0
13.0
64.1
Kinj-K2-Inntrrutt-1
14.1
57.7
50.0
1.1
29.7
54.4
WebExplorer-88
15.7
50.0
53.7
WebDancer-Q0Q-328
3.8
51.5
38.3
0.0
9.3
34.5
WebSailor-328
10.5
53.2
53.3
21.3
0.0
2.1
5.5
WebShaper-OOQ-328
53.3
35.0
0.0
9.9
31.5
WebLeaper-Unicr
22.1
69.9
62.3
35.1
4.0
22.2
34.5
Webl.saper-Reverse-Uhiioo
23.0
67.0
66.0
37.2
4.0
25.8
40.8
Training Configurations To maintain the basic deep search ability, combine data with 5,000 WebSailor-V2 (Li et al.] 2025b) data to train the model. We separately merge 5,000 WebSailor-V2 data with Basic, Union, and Reverse-Une to a larger degree (with a ISR set to 0.3 and /3 in ISE set to 0.1). On this basis, we employ wwen3-30B-ABB-hhnkkin
data of WebLeaper, which stimulates the IS ability
[ as the base model, trained using Megatron framework The training follows a standard SFT procedure. In our experiments, SFT on the final dataset (approxi- mately 15k samples)
completed in about 6 to hours on a cluster of 64 H20 GPUs.
Evaluation Metries and Inference Hyper-parameters The overall evaluation follows the settings specified by each benchmark. For BrowseComp, GAIA, xbench-DS, and Seal-O, we report the pass@1 scores obtained via LLMMaa--a-judee evaluation as the final results. For WideSearch, we report the success rate (SR) for fully retrieving all target results, along with two Fl scores-Roo F1 and Item F1-which are computed using a combination string matching and LLM-as-a-judee evaluation, in alignment with the official evaluation protocol. During LLM inference, configure the sampling parameters (temperature and top-p) to 0.6 and 0.95, respectively.
4.2 OVERALL PERFORMANCE
As shown in Table WebL achieves state-of-the-art performance compared to mainstream open- source agents on five challenging information-seekinn seeking QA benchmarks. Notably, on benchmarks other than BrowseComp and WideSearch, it even delivers performance comparable of agents built on Claude-4-Sonnet and OpenAI-o3. Even on the highly challenging BrowseComp benchmark, WebL significantly outperforms Kimi Simi-K2-Tin 1K2-Instruct-1, despite the latter having much larger parameter scale. also worth noting that the Reverse-Union data, which incorporates greater task complexity top of the Union data, employs fuzz strategy that further facilitates the model's s ability to integrate information-seeking with planning and reasoning, thereby enhancing its overall information-seekin QA capability.
surpassing, that
Crucially, WebLeaper's robustness is demonstrated by its strong performance on test sets that are not limited Wikipedia. While our agent trained on Wikipedia-derived data. is evaluated on five diverse, real-world benchmarks that require querying and reasoning over the entire live web. The consistent and significant improvements across these varied benchmarks strongly suggest that our method learns generalizable skill of efficient information seeking, rather than overfitting to the specific structure Wikipedia.
http:://huaugnngaaecccc////////a Twii--iiiiiiiniine 2507

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Table 2:
Ablation study on training results across different data sources (for efficiency considerations. we use the WideSearch (English subset) and BrowseComp (200 subset), while the full sets are used for the other benchmarks). Numbers parentheses denote the difference compared to training only with the WebSailor-V2--1 data. i denotes mixed version that includes the WebSai WebSailor-V2 1or-V2-5k data.
Data Source
BrowseComp
WideSearch
GAIA
Seal-0
xbench-DS
Avg.
WebSailor-V2--1
25.17
33.15
67.69
34.23
60.00
44.05
1or-2-1or-
24,50
38.91
66.02
33.93
62.67
45.21
Basic-5k!
20.67 (-4.50)
32.26 (-0.89)
40.78 (-26.91)
30.03 (-4.20)
58.33 (-1.67)
36.41 (-7.64)
Unian-5k!
27.50 (+2.33)
41.70 (+8.55)
69.90 (+2.21)
35.14 (+0.82)
62.33 (+2.33)
47.31 (+3.26)
Reverse -ni--ennnnn-----
27.67 (+2.50)
44.07 (+10.92)
66.99 (-0.70)
37.24 (+3.01)
66.00 (46.00)
48.39 (44.34)
BrowseComp
WideSearch
Performance
Only
(SE-Only
ISK ISE
ISR-Only
15E-0nly
ISE
15R.Enly
ISE-Only
1SR
Figure 3: Ablation study results on information-guided trajectory construction strategics.
Overall, the observed performance improvements validate that our proposed approaches- entity- intensive task synthesis and cnermattionguideed trajectory construction--siinffiaalll enhance the agent's 's information-seeking capabilities, even under modest parameter budget.
4.3 CAPABILITY GAINS INDUCED BY ENTITY- INTENSIVE TASK SYNTHESIS
To investigate the effectiveness of our entity-intensive task synthesis method, we conduct compara- tive analysis against training solely on the WebSailor-V/ dataset (using 5,000 and 1,000 samples, respectively), ), a synthetic corpus specifically designed to stimulate the agent's deep search capability.
As shown in Table 2 we investigate the impact of different entity- intttt-iintessive task synthesis strategies through an ablation study on all these benchmarks. The Basic setting exhibits substantial drops across all three datasets compared WebSai 1o-Sailoo-V255k This poor performance to the inherent limitations of the Basic data construction method: tasks generated under this setting tend to be overly simple, allowing the model infer complete answers from only few information sources. Such shortcut patterns encourage the model to overfit to superficial cues rather than learning integrate diverse information, ultimately impairing generalization.
be attributed
In contrast, the Union strategy consistently outperforms WebSailor-V2-Sit achieving an average improvement of +3.26. By combining heterogeneous information sources and increasing the com- plexity of task construction, Union mitigates the shortcut problem inherent in Basic, forcing the model reason over dispersed and complementary evidence. This leads more robust performance across datasets and demonstrates the effectiveness of the proposed data construction approach.
Furthermore, compared to Union, Reverse-Union introduces certain degree of reasoning com- plexity into the information-seeking process, making it more challenging for the model to readily identify where to begin entity retrieval. This design particularly enhances the model's 's planning and decision-making capabilities in information-seeking tasks. The improvement in these abilities is clearly reflected in performance, leading to substantial and widespread gains across all benchmarks.
4.4 IMPACT OF INFORMATION- TRAJECTORY CONSTRUCTION
We compare the proposed information-guide trajectory construction strategies across ISR-Only, ISE-Only, and ISR+ISE on three representative benchmarks SrrcmmarkssGAAA, BrowseComp. and WideSearch- examine the independent and combined effects of ISE and ISR.
On GAIA and BrowseComp. ISR+ISE achieves the best performance, suggesting that integrating precision and efficiency constraints produces trajectories that are both goal-directed and concise, thereby reducing redundant exploration. This indicates that in more complex browsing tasks, relevance and efficiency constraints complement each other generate higher-quality trajectories.
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WedSallarV2 WebLesper
WeaSaliorz
WideSearch
WebLeaper
Parformance
274
Xbench-DS
BrowseComp
Average Action Rounds
Average Action Rounds
Figure 4: Effectiveness and efficiency comparison between WebLeaper and WebSailor-V2.
In contrast, WideSearch, the three strategies deliver comparable results, with performance differ- ences falling within the margin variance. This suggests that for broad search tasks, the specific choice of trajectory filtering plays a less critical role-likely because training entity-intensive synthesized data already provides strong broad search capabilities.
4.5 JOINT GAINS IN EFFICIENCY AND EFFECTIVENESS
As illustrated in Figure 4 WebLeaper consistently outperforms the baseline in terms of both effec- tiveness and efficiency. In the WideSearch and BrowseComp benchmarks, our approach achieves markedly higher performance scores while requiring fewer average action rounds, indicating that the search process not only more accurate but also more efficient. Similarly, in the GAIA and xbench-De DS tasks, our method improves effectiveness while simultaneously reducing the operational cost. This demonstrates that our design enables more targeted search strategy, resulting in reduced interaction steps without sacrificing- and in fact enhancing the quality of the results.
Overall, these results validate that our proposed method achieves superior joint optimization of information-seekinn efficiency and task performance compared to the baseline. This reflects our key insight: an agent should not merely learn to search, rather learn to search efficiently and wisely, thereby achieving better balance between efficiency and effectiveness.
4.6 EXPL.ORATION ON
ISR AND ISE 3
To validate the robustness of our information- guided trajectory construction (Section 3.2). we conduct sensitivity analysis of the core filtering hy- perparameters: a (Information Seekinn Rate, ISR. threshold) and 3 Unformation-Seeking Efficiency, ISE, threshold) (both defined in Section 2.3). a ensures trajectory accuracy by retaining only se- quences with sufficient target entity coverage, while enforces efficiency by excluding overly time- consuming trajectories.
Table Performance (Pass 1) on Browser- Comp (200 subset) for different a (ISR thresh- old) and / (ISE threshold) combinations. value of ( disables the filter.
B B\o
0.3
0.1
27.5
22.2
0
14.0
13.7
We trained weebeeeper-RRverse---tre ent 3 combinations, using BrowserComp (200 subset) for evaluation; threshold of 0 disables the corresponding filter. Tahle the results: the highest performance achieved with a = 0.3 and 3 = 0.1 (our current configuration). Disabling either filter degrades performance, and no filtering yields the lowest score, confirming both constraints are critical for high-quality training data.
with differ-
The selection of 0.3 and 8 0.1 balances data quality and quantity. Excessively high thresholds would shrink the dataset below sample size baselines, while overly low thresholds introduce low-quality trajectories. This "sweet spot" preserves comparable sample size while removing poor-quality sequences, ensuring fair baseline comparisons and validating our parameter choice.
4.7 RESULTS ON DIFFERENT BACKBONE
To showcase its broader applicability across diverse model architectures, we evaluate our method on multiple backbones, placing particular emphasis the Owen3-4B-Thinking 2500 model. For this comprehensive evaluation, we adopt two training configurations: the bascline setup entails exclusive fine-tuning 5,000 high-quality samples from the WebSailor-v2 dataset, while our proposed

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Table 4: SFT results on Qwen3 WWen3-4B-Thinking-2250 Bold scores indicate the highest values.
WideSearch
Model Framework
BrowseComp
GAIA
xbench-DS
Seal-O
SR
Row F1
Item F1
Oween3-4B-TTinking--25
WebSailor-V2 Weblesper-Reverrse-uninn
16.2
55.3
53.3
30.9
1.5
15.7
30.3
17.7
58.3
54.7
32.1
3.5
21.1
38.1
method leverages mixed training corpus that integrates 5,000 WebSailor-V2 samples with our carefully curated WebLeaper-Reverse--niio data.
As illustrated in Table 4 even when deployed on the relatively compact Qwen3-4B-Thinking training augmented with WebLeaper data delivers consistent and substantial performance enhance- ments across all evaluated benchmarks relative to the baseline. This result underscores the funda- mental nature of the advantages brought by entity-intensive task synthesis and information-guu -uidedd drategies-aadvantages advantages that transcend constraints imposed by specific model scales or architectural paradigms, thus validating the generalizability of our approach.
trajectory construction strategies
RELATED WORK
5.1
INFORMATION SREKING AGENT
LLM-poowreed information-seekiinn seeking agents can be broadly categorized into streams: (1) enhancing core models via supervised fine-tuning (Zeng et al. 2023 Wu et al. 2025a Li et al. 2025c b Tao
al. 2025 Su et al., 2025 Fang et al. (025a); (2) advancing agent architecture for improved planning and robustness QQao et al. 2025 Xu et al. 2025a Li et al. 2025a); and (3) developing multi-agent systems for collaborative problem-son solving (Wu et al. 2023 Hong et al., (024)) Our work
aligns with the first category but addresses a key limitation. Prior methods often train on tasks focused on correctness with single-fact answers, which is insufficient for large-scale information gathering. We posit that the number of entities in answer-its its entity richness-is evaluating an agent's ' completeness and efficiency. This paper aims to bridge this gap by creating and utilizing entity-rich QA data enhance agent capabilities for comprehensive information acquisition.
critical dimension for
5.2 AGENT DATA SYNTHESIS
Synthetic data generation is pivotal for agent training, with primary applications in tool use (Wu
ct al., 2025a; Tao et al. 2025
Shen et al. 2025 Fang et al.) 2025b). code generation (Jimenez
et al.
2024 SHEN et al. 2025: Xu et al. 2025c Shao et al. (205)), and GUI automation (Xu et al.
2025b Sun al. 2025 Pahuja al. 2025). These efforts primarily combat data scarcity. Within the information-seekin
domain, existing data synthesis approaches increase task difficulty through multi-step reasoning (Wu al. 2025b Tao et al., 2025) long-horizon planning (Qiao al. 2025). We contend that such methods often overlook the semantic richness of the training data itself. In contrast, our approach centers on synthesizing QA data with high entity-level complexity. We hypothesize that this focus on data semantics is a crucial and complementary path to improving agent reasoning and world knowledge alignment.
CONCLUSION
In this paper, we addressed low search efficiency in LLM based information seeking agents. We argued that the sparsity of target entities conventional training tasks is primary contributor to this inefficiency. To overcome this, we introduced WebLeaper, framework for constructing entity- intensive IS tasks and generating efficient solution trajectories. By formulating IS as tree-structured reasoning problem and scaling task complexity through Basic, Union, and Reverse-Union synthesis, we built a rich training environment. Our ISR/SEE-guide trajectory curation further ensures training on solutions that are both accurate and efficient. Extensive experiments five benchmarks show that wrebLeapercoee consistenny improves performance, confirming that improving search efficiency is a strong lever for enhancing overall IS-agent capability.
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ACKNOWLEDGEMENTS
This work is supported by National Natural Science Foundation of China (92470121, 62402016. 62595734), National Key R&D Program of China 2024YFA1014003) Zhongguaneun Academy (Grant No.s C20250204, C20250602), and High-performance Computing Platform of Peking Univer- sity.
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A APPENDIX
A.1 DECLARATION ON THE USE OF LLMS
We declare that the use of LLMs during the preparation of this manuseript was strictly limited to language-related assistance, such sentence refinement and grammatical correction. All substantive content was independently authored by the authors and rigorously reviewed and verified following any LLM-assisted modifications. During the experiments, all usage of LLMs solely for academic research purposes, with no inappropriate applications. Detailed experimental settings are provided in the Experiments section of this paper. No other reliance on LLMs is involved in this work.
A.2 PROOF OF PROPOSITION 1
This appendix provides the detailed mathematical derivation for Proposition as presented in Section 2.3 The purpose of this proof is to formally establish that the variance of the Information- Seeking Efficiency (ISE) metric is inversely proportional to n, the number of required entities. This property, Var(ISE) (1/n), demonstrates that ISE becomes increasingly stable and reliable performance measure as the complexity of the task (i.e., the size of n) grows.
Proof. Recall from the main text that the Information Seeking Efficiency (ISE) is defined as ISE T
where T is the total number of action steps taken to discover all required distinct entities in the set R.
Step 1: Modelling the random variables. Let X; denote the number of steps the agent spends to discover the i-th new required entity in R. By assumption:
X1,.... Xn are independent and identically distributed.
# E[X,) > 0 (finite mean).
a² Var Xx) < (finite variance).
X; > 0 almost surely (each discovery takes positive number of steps).
The total number of steps is
==1
Step 2: Sample mean and basic properties. Define the sample mean
T
X
X₁-
n
n
By standard properties of i.i.d. random variables:
σ²
E μ4,
Var (X)
(8)
From the ISE definition Eq. we have the exact identity
ISE
X
Step 3: Reduction to the variance of a smooth function. We have ISE f(X) with f(x)
Since a and X; a.s. the function f is infinitely differentiable a neighborhood of μ.
Write
8, =
so that E[,,] = and Var(š,) o² /n. By the Strong Law of Large Numbers, 8, 0 almost surely as n + 00. Thus, for large n, with high probability 8,,1 < p/2,, ensuring that a Taylor expansion of around y is valid.
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Step 4: Taylor expansion with remainder control. On the event {lô,1 1/2), expand
f(u + 6n) using Taylor's theorem to second order:
On 62
+ Ra(ón);
(9)
1 On where the remainder term satisfies Rs(6,)) S ClS for some C depending only on p.
Step 5: Mean and second moment calculations. From Eq. ():
E
EE
R3
(6,))),)) )],
(10)
1
E[
E
+
19)
(11)
n²2
By Eq.8)..110.
80.
16 = d²/n. Also, finite variance plus Hölder's s inequality yields E||6,, 9] O(n 3/23.
Therefore:
1
o²
(12)
/
11
71
71
1
2
E[1/X²
+
(13)
112
4
72
Step 6: Computing the variance. Using Var(Y)
E
-
(E) with Y 1/X:
Var(ISE)
1
+
o²
+
-
1
(
o²
1
112
1
ni
n
0²
+
where the constant term 7 cancels exactly.
Step 7: Conclusion. We have shown that
Var(ISE)
+ 0
1 1
which in particular implies the order bound Var(ISF) 0(n This completes the proof.
A.3 REINFORCEMENT LEARNING EXPERIMENTS
To further explore the potential of WebLeaper's data, we conduct reinforcement learning (RL) experiments by applying GRPO to advanced SFT model. The core goal is to verify whether integrating RL can further boost the performance of our best-performing SFT model, leveraging RL's strength in optimizing action quality.
RL has proven effective in advanced reasoning tasks by exploring logical step spaces, but its objective in the information-seeking domain is distinct: optimizing the quality of external tool actions (e.g., search, visit). We design a dense, granular reward signal based on soft F-score that combines recall (ISR) and precision, creating a rich and accurate learning signal. This reward mainly encourages the agent to prioritize "better steps", which is our first motivation. The length of the reasoning content main also varies properly for this purpose.
Table prrssents the performance comparison between the SFT baseline and SFT+RL. Results show that RL significantly improves performance across all benchmarks: GAIA sees 3.3-point gain, xbench-DS improves by 3.0 points, and WideSearch's 's Success Rate (SR) jumps by 2.5 points. This validates that RI,, when combined with WebL WebLeaper's high-quality synthetic data, effectively enhances the agent's action optimization capability in information-seeking tasks.
A.4 CLARIFICATION ON TooL. CALL EFFICIENCY AND REASONING CONTENT LENGTH
Our core objective in introducing ISE filtering is to enhance the quality and efficiency of tool calls, not to restrict the length depth of reasoning content. These dimensions fundamentally
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Model
BrowseComp
GAIA
Xbench-DS
WideSearch (SR)
WideSearch (Item FI)
SFT
37.8
69.9
69.0
1.5
45.4
SFT+RL
38.8 (+1.0)
73.2 (+3.3)
72.0 (+3.0)
(+2.5)
48.5 (+3.1)
Table 5: Performance comparison between SFT and SFT+RL across benchmarks.
distinct: tool call efficiency focuses on minimizing redundant, irrelevant, or unnecessary interactions with external tools (c.g., redundant searches, unfocused URL visits), while reasoning content length refers the performance of the agent's internal cognitive process (e.g., step-by-step chain-of-thouge (CoT) reasoning, intermediate deduction for complex constraints). The ISE metric targets the cemner--nsuring each tool call contributes meaningfully to entity retrieval-witte without imposing arbitrary limits on the latter.
Notably, the length of reasoning content dynamically adapts to serve our efficiency goal and the inherent complexity of the task. For tasks requiring deep exploration (e.g., multi-step logical deduction, adaptive constraint satisfaction), the agent's reasoning process naturally expands to accommodate structured planning (c.g.. decomposing complex queries into subgoals, verifying intermediate entities). However, this expanded reasoning directed toward optimizing tool call precision- e.g. generating more targeted search queries or validating the relevance of retrieved information-rann than indulging in unfocused divergent exploration. For complex tasks like AIME-style problems or long-form CoT reasoning, our framework preserves the necessary cognitive depth by allowing reasoning content to scale with task difficulty, while ISE filtering only suppresses unproductive tool calls (e.g., repetitive searches with minor keyword variations) that do not advance the reasoning process.
In summary, strict 1SE constraints do not hinder adaptive exploration or complex reasoning. Instead, they decouple tool call efficiency from reasoning length: reasoning content evolves to support targeted, high-quality tool interactions, and task complexity inherently shapes the depth of reasoning all while ISE ensures tool usage remains purposeful and resource-efficier This design balances exploration and efficiency, avoiding both tool call redundancy and overly rigid reasoning suppression.
A.S DATA STATISTICS
Geography Sports History
T
Law
Travel
aducation
Science
Business
Environment Art Entertainment
Culture
Othe
Politi c S
Military
Health Food
Religioo
Figure 5: The distribution of our training data.
Figure illustrates the distribution of our training data.
Figure displays the entity count distribution of our training data. sigmificant portion of our samples contain at least 100 entities, underscoring the inherent difficulty of our dataset. As formalized in Equation [ this complexity is crucial for robustly measuring efficiency, which in turn leads to improved overall performance.
A.6 DATA CLEANING AND BASIC TASK CONSTRUCTION
This section elaborates on the data processing and construction methodology for the Basic version tasks introduced in Section 3.1.1
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Distribution of Entities
Percentage (%)
10
10
10
Entity Number
Figure 6: Entity Count Distribution in Training Data. significant portion of our samples contains at least 100 entities, underscoring the inherent difficulty of our dataset. This complexity, formalized in Equation equation 6 is crucial for robustly measuring efficiency, which in turn contributes to improved overall performance.
Rationale for Tree Structure In information-seeking tasks, the reasoning structure is paramount. We chose tree structure for our basic tasks because it ofters compact and hierarchical organization of entities. This structure is highly efficient for representing large number of interconnected entities that stem from common query concept, mirroring many real-world cooomattiongatheree scenarios. A reasoning tree is composed of root (question entity) and a set of subtrees, where each subtree represents a cohesive unit of information.
Multi-Stage Table Cleaning To ensure the quality and suitability of the data used for task synthesis, we crawled approximately million tables from Wikipedia and subjected them to a rigorous multi- stage cleaning procedure. This was essential because raw web tables are often noisy and inconsistent. The stages were as follows:
We first discarded tables that were either too small (fewer than 10 rows or 3 columns) to capture meaningful relational information, or too large (more than 200 rows or 20 columns) to be processed efficiently and form coherent task.
Size Filtering:
Semantic and Structural Filtering: We then removed semantically irrelevant columns that frequently appear in web tables, such those containing serial numbers, notes, references. Tables with significant formatting errors (c.g.. numerous merged cells that disrupt the relational structure) were also excluded.
Isomorphism and Homogeneity: Finally, we retained only groups of isomorphic tables (tables sharing the same column headers and structure). This step was crucial for ensuring structural homogeneity across our dataset, which is prerequisite for identifying common subtree structures needed for the Union operation described later.
The resulting collection contains clean, well-structured tables with set of meaningful fields as columns and multiple rows, where each row can be transformed into a subtree.
Reasoning Tree Population To construct the three-layer reasoning tree from single table, we populate the layers as follows:
First Layer (Question Entities): Entities mentioned in the table's 's title or caption are extracted to form the root of the tree.
Second Layer (Roots of Subtrees):
We employ an LLM analyze the table's columns and select one that contains no duplicate entries. This column is treated as the key, and its values become the second-layer layer entities of the tree. Each of these entities serves as the root of subtree. The LLM is effective at identifying columns like 'Name' or 'Title' that serve this unique identification purpose.
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Third Layer (Leaves of Subtrees): The values in the remaining columns of the table constitute the third layer, representing the leaf entities associated with each second-layer entity.
A.7 MAXIMAL UNION ALGORITHM FOR TASK SYNTHESIS
This section provides the formal definition and algorithmic implementation for discovering maximal union groups, as introduced Section 3.1.2 The core of our approach is reformulate the search for compatible reasoning trees as Maximal Biclique Enumeration (refer to problem on bipartite graph.
Problem Formulation
Let Tuese (T1, Ta......TTT Tx} be our collection of basic reasoning trees. We first construct bipartite graph G (U, E)), where U Thsse is the set of all trees, and V is the set of all unique relation names found within the subtrees across all trees in Thase. An edge (T), B3) E E exists if the relation By is present in any subtree of tree T; (i.e., M Rel(T.), where Rel(T.) U₁ Re)(S
In this construction, a maximal union directly corresponds to a maximal biclique (4, V), where u C U is set of trees and V V is a set of their common relations. Our goal is to find all such maximal bicliques that satisfy certain size and semantic constraints. Formally, we seek to find all maximal pairs (4, V) that satisfy:
find maximal subject to
(u. V)
VT, E U, V = Rel(T,), 4 2 Kmin. [V] 2 mmin-
(14)
Here, maximality means that no other tree can be added to l4 and no other relation can be added to V without violating the biclique property. Solving this by reformulating it as standard maximal bielique enumeration problem is computationally efficient compared to an exhaustive search.
Algorithm and Implementation Details
Input: A collection of base reasoning trees Towse minimum number of trees for a valid union, kmin' minimum number of common relations, mmin-
Goal: To find all maximal union groups, which the solutions (21, V) Eq. 14] that also
satisfy the semantic matching criteria below.
To ensure the semantic coherence of unions, we impose strict matching criteria. For relations connecting the second and third layers, we require they share the same standardized name, data type, and domain. For the second-layer entities themselves (the roots of the subtrees), we relax this constraint, requiring only a match in data type and domain. This flexibility allows for the union of trees with conceptually similar but differently named second-layer entities (e.g.. fusing a tree where entities are 'Authors' with another where they are 'Writers').
Subtree Relation Matching Criteria:
Output: A set of maximal union groups F, where each element is tuple (U", V*) that meets the specified criteria.
The process is detailed in Algorithm I
A.8 DETAILED EXAMPLES OF TASK SYNTHESIS
This section provides detailed explanations and reasoning walkthroughs for the examples of the three task synthesis versions presented in Section 3 and Figure 2
A.8.1 VERSION-I: BASIC
The goal of the basic version is to create a task with a clear, hierarchical reasoning structure derived from single, self-contained set of entities.
Example Question: Who were the Nobel Prize winners in Literature between 1980 and 1990? Please include their name, country, award year, and gender.
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Algorithm 1: Maximal Union Identification Algorithm
Input: collection of base reasoning trees Thase minimum trees kmin, minimum common
relations min--
Output: set maximal union groups F.
F +0;; 0;
// 1. Construct the bipartite graph from trees and subtree relations
Let U be the set of trees from Thase and V be the set of unique standardized relation names found within the subtrees all trees in Tasse:
Construct the graph G (U, V, E) where edge (u, v) E exists if tree u contains the
relation in its subtrees (i.e., v E Rel(u));
// Enumerate maximal bicliques from the graph
B EnumeraceMxximaaiiciqsss.
// Leverages standard algorithme like MICA or Eclat // 3. Filter and validate bicliques to form final union groups
for each maximal biclique (U", V') in do
// Check size constraints from Eq. (1)
[U'| < kimin or [V| < Mmin then
continue:
// Validate semantic compatibility of second-layer entities Let Tia, Dia the type and domain of the second-layer entities of the first tree U": is_ compatible +- true; for each tree u U
10
if u's second- layer entity type * Ta or domain o Du then
12
is_ si_compaatblee + false;
break;
// If all checks pass, add to the set of valid union groups is compatible then
14
15 L F FU (U', (U',
16 return F:
Construction Process: The task is constructed from single Wikipedia table, forming reasoning tree. The layers shown in Figure a) are populated as follows:
First Layer (question entities): Derived from the table's s title and specified constraint, forming the query's 's scope: Literature Nobel Prize, year 1980-1990.
Second Layer (subtree roots): Populated from the table's key column (e.g., author names):
Czeslaw Milosz, William Golding.
Third Layer (subtree leaves): Consists of values from the remaining columns, represent- ing attributes for each second-layer entity. For example: man, Poland, 1980 for Czestaw Milosz. The edges connecting the second to the third layer represent relations like "has maa__generr,, "has_ cus__counryy' "has sol_awardd__eee
Reasoning Path: An agent expected to follow this hierarchical structure:
Identify Scope: Recognize the 'Question Entities' from the query: Nobel Prize in Literature, 1980-1990.
Retrieve Second-Layer Entities: Retrieve the second-layer entities, which the authors: Czestaw Miłosz, William Golding,
Gather Attributes: For each sayend-layer entity, follow the relations retrieve their associated third-layer entities, such as Poland, 1980, man for Czeslaw Milosz.
A.8.2 VERSION-II: UNION
This version increases structural complexity by requiring the agent to perform relational operations across distinct reasoning trees.
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Example Question: Which authors have won both the Nobel Prize in Literature and the Booker Prize? For each, provide their name, nationality and the year they won the Nobel.
Construction Process: Once maximal union is identified (e.g., between the reasoning trees for "No- bel Prize laureates' and 'Booker Prize winners," which share common relations like 'has nationality" within their subtrees), prompted to find an interesting relationship, such as the intersection of the two sets of second-layer entities (authors), and then weave this logic into natural language question.
LLM generates task requiring information integration. The LLM is
Reasoning Path: The task is constructed from a maximal union of two distinct reasoning trees. To solve this, agent must:
Retrieve First Entity Set: Identify the first concept, 'Nohel Prize in Literature," and retrieve the full set of corresponding second-layer entities from the first tree, RNobel (TI)-
Retrieve Second Entity Set: Identify the second concept, 'Booker Prize,' and retrieve its full set of second-layer entities from the second tree, RBooker (12)-
Find Intersection: Perform a relational join to find the intersection of the two sets of second- layer entities based on name. The final "Target Entities' are the entities present in both sets, such as Wiilliam Golding, J.M. Coetzee, ), along with their requested third-layer attributes.
A.8.3 VERSION-III: REVERSE-UNION
This version introduces a challenging cognitive workflow by intentionally obfuscating the query's entry points.
Motivation and Design: The Union method, while creating multi-source source tasks, has a vulnerability: an agent could solve it with simple keyword scarches for cach source, bypassing deeper reasoning. Reverse-Unis inverts the information flow, forcing an agent to first deduce a core 'anchor' entity (a second-layer entity) from descriptive clues and then that entity a pivot to expand its search.
Who are the authors from the same country as the 1980s prize-winner that wrote novel about a group of British boys stranded on an uninhabited island, and who have also won both this reward and the Booker Prize? For each of them, what is their name, country, and the respective years they won each award?
Example Question:
Construction Process:
The construction builds upon the unified space from Version-Il with
'reverse" logic:
Source: We the unified information space from the Nobel and Booker prize union.
Select Anchor: An entity at the intersection of the second layers chosen as the 'anchor,' e.g., William Golding.
Instead of naming the anchor, unique descriptive clues based on its third- layer attributes are generated: "the 1980s prize-winner" and "wrote novel about... British boys..." " These clues become the 'Question Entities'.
Obfuscate Anchor:
Create Union Trigger: A third-layer attribute of the anchor, his nationality (British), is selected as the pivot for the next stage of the query.
Required Reasoning Process: To solve this task, an agent must execute two-stage process:
Deduction Stage:
The agent must first resolve the descriptive clues (which are third-layer entities) to identify the second-layer anchor entity. The clues "1980s prize-winner" and "novel about stranded British boys" uniquely point to William Golding. This inferential step crucial.
Union Stage: Having deduced William Golding, the agent identifies his nationality (a third-layer entity in his subtree): British. This becomes the pivot for the main query. The agent must then find all second-layer entities who (1) share this third-layer attribute (British) and (2) have won both the Nobel Prize and the Booker Prize. This requires filtering the unified entity space find the final set of 'Target Entities", which includes authors like William Golding, Kazuo Ishiguro. and J.M. Coetzee.
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Toolcall Count Density
Search Count Density
Visit Count Density
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Figure 7: Distribution of Search, Visit, and total tool call.
A.9 TOOL CALL ANALYSIS
As shown in Figure 7 our method involves a significantly large number of actions, including Search, Visit, and total tool calls. The density distributions indicate that tool calls often exceed several dozen per instance, with many cases surpassing 50 actions. This high frequency of actions reflects the intensive interaction and comprehensive exploration carried out by our approach, ensuring that the method thoroughly leverages available tools to achieve optimal performance.
A.10 CLARIFCATIONS BETWEEN
COOMMATTOONSEEKKIIN TASK AND ENTITY MINING
This section clarifies the scope of entity-centric information--seking (IS) framework and distin- guishes it from mere "entity mining". " We argue that modeling IS through an entity-centric lens is reasonable, generalizable, and widely-adopted foundation for tackling complex IS tasks, rather than restrictive simplification.
IS task is tuple T' = (g, R)), where an agent interprets a natural language question and navigates the web to collect a complete set of required entities R. We define "entities" broadly to include not only final answers (e.g.. "Alan Turing") but also intermediate reasoning stepping stones and attributes (e.g.. "1950 Turing Award") that are critical for coherent inference.
Definition and Rationale of Entity-Centria IS As formally defined in Section 2.
This ceettic task modeling is powerful for three core reasons: ENTITIES AS ATOMIC INFOR- MATION UNITS. Abstract questions (e.g., about events, procedures, or concepts) ultimately rely on grounding entities (e.g.. "subprime mortgages" for the 2008 financial crisis) as building blocks for answers. UNAMBIGUOUS SEQUENTIAL REASONING. Discrete entities enable objective progress tracking, such deducing anchor entities before retrieving related targets (a core mechanism in our Reverse- Union tasks). CASK VERSATILITY. It generalizes to descriptive, causal, and procedural IS tasks, as all inherently involve collecting and connecting relevant entities.
Community Consensus and Universality Our entity-centric view aligns with growing consensus in the field. Leading works such WebResearcher (Qiao al., (025) model IS as collecting "reasoning entity chains" (analogous to our R2, DeepDive (Lu et al. (2025) uses "entity coverage rate" (a precursor our ISR), and industrial systems like MiroThinker (Team al. 2025b) conceptualize IS as assembling "concept entity graphs". This paradigm is empirically validated and widely adopted for its efffectiveness.
In all, the definition of information-seekkng task is different from traditional entity mining, It's wildly adopted by current agent community.
A.11 DISCUSSION PERFORMANCE ON MULTILINGUAL, PRACTICAL SCENARIO
AND
WebLeaper's generalization capability in real-world scenarios is ensured through multiple inherent mechanisms and empirical validations: the agent architecture based on the ReAct framework Yaoo ct al. 2023) possesses inherent noise resilience, as its "thinking" stage prior to action enables the model to proactively assess the relevance and quality of retrieved information, identify and filter
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redundant noise such ads and irrelevant snippets, and dynamically adjust search strategies adapt to the messy nature the real web.
Centered on real-world and multilingual applicability, experimental design adopts five benchmarks constructed from real web content- Browssrooop (Wei et al. 2025). GAIA (Mialon et al.) 2023). Seal-O (Pham al. 2025), WideSearch (Wong et al. 2025)) and xbench- DeepSeachh (Xbench-'Teee 2025), which cover common real-world challenges including ambiguous expressions, truncated entities, and scattered information. Furthermore, portion of tasks in xbench-DeepSearch and WideSearch are presented in Chinese, making these benchmarks multilingual. Most of the selected benchmarks are not derived from Wikipedia, resulting an out-of distribution (OOD) evaluation setting. This helps mitigate bias in our synthetic data, as the evaluation is decoupled from the source of our training data synthesis. WebL eaper consistently outperforms strong baselines across these benchmarks, providing direct and robust empirical evidence for its generalization ability.
Furthermore, the dual-metric trajectory filtering mechanism of ISR (Information- seeformation-Seeking Rate) and ISE iiformmttion-eeeiing Efficiency) further enhances the model's robustness: ISR requires model to fully cover target entities, fostering its persistence and thoroughness in mining key information amid noise, while ISE suppresses ineffective "noise-chasing" behaviors and trains the model to prioritize high-value actions. Collectively, these designs ensure that WebLeaper can effectively excel complex, noisy real-world application scenarios.
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Reviews:
[{"review_id": "aIl6k2JLYM", "reviewer": "Reviewer_vV8B", "summary": "This paper proposes WebLeaper, a data-centric framework for improving the efficiency of web-based information-seeking agents. The authors construct dense, entity-rich tasks from Wikipedia tables (Basic, Union, Reverse-Union), generate ReAct trajectories using a strong model, and filter these trajectories with two empirically motivated metrics: Information Seeking Rate (ISR) and Information Seeking Efficiency (ISE). The filtered trajectories are used for supervised fine-tuning. Experiments on five benchmarks show solid and consistent improvements in both accuracy and efficiency, supported by ablations.", "questions": "1. Why does efficiency-based filtering have such a large impact?\nIt is unclear whether the gains come from richer sub-queries, reduced long-context degradation, or other behavioral shifts (eg, more focused planning).\n2. How does this reconcile with RL models that benefit from long chains?\nIf shorter, cleaner trajectories are key here, why do math/reasoning systems still require RL-driven long CoT (eg, AIME-style problems)?\n3. Is strict efficiency optimal for tasks requiring exploration?\nThe ISE constraint may suppress necessary divergent reasoning (eg, adaptive exploration), and it is unclear whether the model loses performance on complex tasks such as AIME24/25.\n\nI would be happy to engage with the authors during the rebuttal phase regarding these concerns, and I am open to revising my score should the responses address them satisfactorily.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Novel task synthesis channel using structured Wikipedia tables to create dense, multi-entity information-seeking tasks.\n2. Empirically grounded filtering strategy (ISR/ISE) that directly addresses observed inefficiencies in existing agents.\n3. Strong empirical results across five benchmarks, with clear gains and well-supported ablations.", "weaknesses": "1. Limited insight into the underlying mechanism of why the efficiency-oriented filtering leads to such large improvements; the explanation remains at an engineering level.\n2. The claim that long trajectories are inefficient conflicts with recent RL-based reasoning advances, where longer, adaptive chains often improve quality.\n3. The ISE filtering may be too rigid, potentially suppressing necessary complex or difficulty-adaptive reasoning (e.g., long-form CoT or AIME-like tasks).", "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": "TZ2v3owCdT", "reviewer": "Reviewer_GntM", "summary": "This paper addresses the critical inefficiency of IS agents, identifying \"entity sparsity\" as a key bottleneck. It proposes two metrics ISE and ISR to quantify the IS efficiency, and also shows that the variance of ISE is negative correlated to target entities. The authors propose WebLeaper  to automatically synthesize entity intensive training tasks from wiki tables. It models IS as a tree-structured reasoning problem. The framework increases task complexity through three variants: basic, union and reserse-union. WebLeaper also generates solution trajectories and curates them using ISE and ISR to ensure efficiency. Experiments over challenging QA tasks demonstrate that WebLeaper-trained agents significantly outperform open source baselines, achieving both higher accuracy and greater efficiency.", "questions": "It would be helpful to run the experiments mentioned in weakness part. \n1. finetune over a different base model\n2. enrich ablation study", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper highlights the low efficiency of the current IS agents, providing evidence that most of actions are often invalid. It formally defines two metrics, ISR and ISE to quantify this problem. The authors provide Proposition 1 that the variance of the ISE metric decreases as the number of target entities n grows.\n2. The paper proposes an innovative tree-based pipeline, WebLeaper, to generate entity-intensive training data from Wiki tables. This method systematically increases task complexity through three variants (Basic, Union, and Reverse-Union). The framework also curates solution trajectories by filtering for high ISR and ISE, ensuring the agent learns from optimal, efficient examples.\n3. WebLeaper demonstrate better performance in challenging QA tasks comparing to the open source IS agents.", "weaknesses": "1. The WebLeaper is finetuned on a single base model (Qwen3-30B-A3B-Thinking-2507). The observations may change with a different base model.\n2. The ablation study could include the analysis of how the average action rounds change with different data sources (similar to table 2).", "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": "G2qaIeRn9L", "reviewer": "Reviewer_cPZV", "summary": "This paper presents WebLeaper, a framework aimed at enhancing the efficiency and effectiveness of information-seeking (IS) agents based on large language models (LLMs). The authors identify the problem of low search efficiency in current IS agents, attributing it to the sparsity of target entities in training tasks. To address this issue, they propose a novel approach that constructs high-coverage IS tasks using a tree-structured reasoning model, allowing for a greater number of target entities within a limited context. The framework includes three dataset variants—Basic, Union, and Reverse-Union—to systematically increase task complexity. Additionally, the authors curate training trajectories based on the Information-Seeking Rate (ISR) and Information-Seeking Efficiency (ISE) to ensure that the model is optimized for both accuracy and efficiency. Extensive experiments on five IS benchmarks demonstrate that WebLeaper consistently outperforms strong baselines, validating the effectiveness of the proposed method.", "questions": "None", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The paper addresses an important issue in most current LLM-based information-seeking agents, specifically the problem of low search efficiency. The focus on efficiency is a valuable contribution to the field, as it complements existing efforts that primarily target search depth.\n- The proposed tree-structured reasoning framework can support more comprehensive IS tasks through more structured trajectories, which may lead to better learning of search strategies.\n- The use of ISR and ISE metrics to curate training trajectories is a valuable contribution that ensures the model is trained on high-quality data.", "weaknesses": "- The paper claims to present an information-seeking agent; however, the agent mainly focuses on finding target entities via web search given artificially constructed complex questions. This is more akin to entity mining tasks rather than general information-seeking tasks. The authors should better clarify the definition of an information-seeking agent in this work and discuss the limitations of the proposed method in broader information-seeking scenarios—particularly, how the three proposed dataset variants can help the agent seek information beyond entity finding.\n- The proposed tree-structured reasoning framework is interesting, but the paper lacks a detailed analysis of how this structure is advantageous compared to other possible structures, such as graphs. The authors also overlook the potential limitations of tree structures in capturing complex relationships among entities.\n- Since the proposed method mainly focuses on synthetic dataset construction and trajectory filtering, it may not generalize well to real-world applications. For example, web content is often noisy, and retrieved documents may lack crucial clues or entities. This suggests that the proposed method might not perform well in practical scenarios. The authors should discuss these limitations in more depth and provide analyses or experiments to verify the robustness of their method under more realistic conditions.", "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": "cmmE92cQ7D", "reviewer": "Reviewer_ZRih", "summary": "This paper introduces **WebLeaper**, a framework to enhance **efficiency and efficacy** of LLM-based information-seeking (IS) agents. It reformulates IS as a **tree-structured reasoning** task, addressing entity sparsity in existing datasets. Three task variants—**Basic**, **Union**, and **Reverse-Union**—progressively increase reasoning depth and realism.\nTo ensure learning from high-quality data, trajectories are filtered via **Information-Seeking Rate (ISR)** and **Information-Seeking Efficiency (ISE)** metrics, balancing correctness and search economy.\nExperiments on **BrowserComp, GAIA, Seal-0, WideSearch, and xbench-DeepSearch** demonstrate consistent improvements in both accuracy and efficiency .", "questions": "* Can ISR/ISE thresholds adapt dynamically during training?\n* Does Reverse-Union risk overfitting due to fuzzy clue anchoring?\n* Could integration with knowledge graphs further enhance reasoning coverage?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 4, "strengths": "* Addresses a neglected but critical dimension—efficiency;\n* Clear theoretical justification and measurable impact;\n* Well-designed ablation studies and visualization;\n* Comprehensive benchmark evaluation with strong improvements;\n* Highly reproducible (data construction and algorithmic details disclosed).", "weaknesses": "* Lack of hyperparameter sensitivity analysis for α and β;\n* Dataset bias (Wikipedia-only) not discussed;\n* No multilingual or real-world deployment tests;\n* Missing analysis on computational training overhead.", "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)