WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning
Authors: Kuan Li
Zhongwang Zhang
Huifeng Yin
Rui Ye
Yida Zhao
Liwen Zhang
Litu Ou
Ding-Chu Zhang
Xixi Wu
Xinmiao Yu
Jialong Wu
Xinyu Wang
Zile Qiao
Zhen Zhang
Yong Jiang
Pengjun Xie
Fei Huang
Zhi-Qin John Xu
Shuai Wang
Minhao Cheng
Jingren Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Web Agent
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: To significantly advance the capabilities of open-source web agents, we present WebSailor-V2, a complete post-training pipeline encompassing data construction, Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL). Our methodology features two key innovations: (1) On the data front, we developed SailorFog-QA-2, a novel dataset built from a densely interconnected knowledge graph that introduces a wide variety of uncertainties beyond simple obfuscation, fostering more sophisticated reasoning. (2) For training, we engineered a dual-environment RL framework, combining a high-fidelity simulator for rapid, low-cost algorithmic iteration with a robust, managed real-world environment for stable final policy training, all integrated within a symbiotic data-policy feedback loop. Trained on the Qwen3-30B-A3B model, WebSailor-V2 achieves state-of-the-art results, scoring 35.3 on BrowseComp-EN, 44.1 on BrowseComp-ZH, and 30.6 on Humanity's Last Exam (HLE). Notably, our 30B-A3B MOE agent significantly outperforms all existing open-source agents and surpasses even the 671B DeepSeek-V3.1, demonstrating performance competitive with leading proprietary systems.

Published as a conference paper at ICLR 2026
WEBSAILOR-V V2:
BRIDGING THE CHASM TO PROPRIETARY AGENTS VIA SYNTHETIC DATA AND
SCALABLE REINFORCEMENT LEARNING
Kuan Lit.3.| Zhongwang Zhang Huifeng Yin Rui Ye Yida Zhao";Liwer ; Litu Ou
Dingchu Zhang Xixi Wu Xinmiao Jialong Wu Xinyu Wang Zile Qiao Zhen Zhang".
Yong Jiang Pengjun Xie Fei Huang Zhi-Qin John X Wang Minhao Cheng Jingren Zhou
1 The Hong Kong University of Science and Technology
2 School of Mathematical Sciences, Institute of Natural Sciences, Shanghai Jiao Tong University
3 Tongyi Lab, Alibaba Group
4 Penn State University
ABSTRACT
To significantly advance the capabilities of open-source web agents, we present WebSailor-V2, a complete post-training pipeline encompassing data construction, Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL). Our methodology features two key innovations: (I) On the data front, developed SailorFog-QAA QA-2, novel dataset built from a densely interconnected knowledge graph that introduces wide variety of uncertainties beyond simple obfuscation, fostering more sophisticated reasoning. (2) For training, engineered dual- environment RL framework, combining hhighfidelityy simulator for rapid, low-cost algorithmic iteration with robust, managed real-world environment for stable final policy training, all integrated within a symbiotic data-polic feedback loop. Trained the Qwen3-30B -BBBBBBBBBBBBBBBB model, WebSailor- V2 achieves state-of-the oire--ofthhearrr results, scoring 35.3 on BrowseComp-ENN, 44.1 I on BrowseComp-Z44 and 30.6 on Humanity's Last Exam (HLE). Notably, our 30B-A3B MOE agent significantly outperforms all existing open-source agents and surpasses even the 67 IB DeepSeek- V3.1, demonstrating performance competitive with leading proprictary systems.
INTRODUCTION
BrowseComp-EN
xbench-DeepSearch
36
35.3
32.6
30.0
28.3
26.4
AA
70
73.7
70.0
66.7
64
K
64-
18
15.7
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13.4 13.0
58
53.0
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52-
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WatSataar22 100
Deepsten 1-7710
Grok4
Opensi mini
GLA 5-3350
Ciaude-t-Ope Opus
Deap:Deet22 328
KimiC2 instroch-11 I7
Drepthvar/22 300
Airo Miioteeniee- 22th 090--9..
Wistaalnnn22.00 363
40-
Kini Raseerchher
opeatis
Drestinent2.0 363
Derethie 328
Cieasted
Figure 1: Performance on the BrowseComp-EN and xbench-DeepSeaard benchmarks.
In the pursuit of Artificial General Intelligence (AGI), autonomous Al agents represent critical milestone, with "Deep Research" emerging as core paradigm for achieving more generalized
"Equal contribution (the six authors contributed equally).
'Corresponding authors: kuanli @ unipat.ai, rinhuifeng.andy @ gmail.com, yongjiang.jj jy @alibaba-inc.com. archhigin@su..ddu.coo shuaiw @ese.ust.hk, mmc71 149 @4.@psu.eduu psu.edu

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Published as a conference paper at ICLR 2026
capabiliitis. By leveraging external tools like search engines and web browsers, these agents can autonomously conduct systematic and in-depth analyses to tackle complex, multi-step research tasks through dynamic reasoning and iterative information retrieval (OpenAl 2025a Al 2025). Despite recent advancements across the research community, spanning improvements from both perspectives of data and training (Wu et al. 2025b [i et al. 2025b Liu el. 2025a Nguyen et al. 2025 Li et al.)
2025c; Wu et al. 2025a] Tao et al. (025), ) a considerable performance gap still persists between open- source solutions and proprietary systems (e.g., OpenAI DeepResearch (OpenAl 2025a leading to
a bottleneck in democratizing powerful research capabilities.
This performance disparity primarily stems from fundamental challenges in two of the most critical stages for developing powerful agents: data and training. (1) Data: insufficient diversity and monolithic definitions of uncertainty. information-seeking relies on the agent's s ability to leverage existing information and logical relationships to infer or acquire new, reliable knowledge. If the training data lacks a sufficiently broad and complex range of logical structures, the model will struggle to generalize to novel and intricate problems. Existing methodologies often rely
narrow set
of uncertainty definitions, such as obfuscation (Li et al. 2025b; Gao et al.) 2025 Shi et al., 2025). A wider variety of uncertainty types is needed to elicit more diverse and sophisticated reasoning behaviors from the base model, better preparing it for the ambiguity inherent in real-world research. (2) Training: lack of scalable reinforcement learning (RL) training environment. Creating scalable and robust RL training environment for agentic systems poses a significant challenge, which typically demands massive rollouts, each potentially involving numerous tool calls. The high cost and engineering complexity of high-coneurrency requests to external APIs can lead to practical issues like tool latency, API failures, and inconsistent outputs. These issues would contaminate the training data, degrade the model's ' learned policies, and severely hinder iteration of RL training algorithms.
In this paper, we present WebSailor- V2, a comprehensive framework designed to overcome these barriers through topological data synthesis and envirommenta stabilization. (1) Topological Strategy for Reasoning: To resolve the insufficient diversity inherent in linear or tree-like training data, we introduce Sail an enhanced dataset built upon SailorFog-QA QA (Li et al. 2025b). Unlike conventional "casy-to-hard" expansion, our method constructs densely interconnected knowledge graphs. By specifically sampling trajectories that require resolving cyclic dependencies and traversing critical "cut vertices", we force the agent to learn abstract graph-search patterns. This mechanism ensures the model acquires robust reasoning capabilities that generalize beyond simple information retrieval. (2) Stabilization Strategy for RL: To mitigate the "signal-to-noise ratio issue caused by real-world volatility, we propose a Symbiotic a high-fidelity simulator as an algorithmic "Wind Tunnel", we decouple policy learning from environmental stochasticity. This allows for high-frequency, stable policy iteration, ensuring that the agent establishes a robust policy before adapting to the noisy, managed real-world interface. First, we develop a dedicated simulated environment from the ground up, based on a large- scale offline Wikipedia knowledge base (Vrandečic& & Krötzsch, 2014). This environment is designed for high-frequency algorithmic experimentation and data curation, providing a low-cost, exceptionally fast, and fully controllable platform. Through meticulous design, it achieves high fidelity, ensuring that the agent's 's interaction dynamics, state transitions, and reward mechanisms closely mirror those of a real-world setting. Second, recognizing that RL training in real environment is complex engineering cospeillsyyciilllll concerning the consistency of tool returns after toolset expansion, the reproducibility of trajectory sampling, and the need for high concurrency and fault tolerance, we build a unified tool execution interface that utilizes a scheduling and management layer to incorporate different tools' quality measures and protocols. Finally, our data construction and RL training pipelines are integrated into a symbiotic feedback loop. This dynamic mechanism allows the system to synthesize and filter high-quality data based on training dynamics, enabling the model to continually refine its policies and learn from stream of relevant information. This co-evolution of data and policy therefore promotes building deep research agents more effectively and efficiently.
Enviroment framework. By utilizing
To demonstrate the efficacy of SailorFog- QA- V2 and training strategies, we build our agent upon the foundational ReAct framework (Yao et al. 2023). Trained on Qwen3-30B- -0ee3-33B--RR ( YYang et al. achieves scores of 35.3 on BrowseComp-EN (Wei et al.) (025] and 44.1 on Browseeoomp-Zu (Zhou et al., 2025a). alongside a score of 30.6 on HLE (Phan et al.. 2025), significantly outperforming all existing agents built on open-source models. Remarkably, our 30B-sized agent outperforms the previous best performnnnn agentic 67 B-sizeed LLM DeepSeek- V3.1 (Team 2025b), which achieves 30.0 BrowseComp-EB EN and 29.8 on HLE, respectively.
2023), our WehSailor-
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2
AGENTIC FRAMEWORK
ReAct. 2023):
We adopt the ReAct framework as the foundation for our agent's architecture
Yao et al.
(I) where T3, ds O, represent thought, action, and observation in the i-th iteration, respectively. At step t, the agent's 's thought тr and subsequent action a, are sampled from policy π conditioned on the complete preceding context, defined as T(a Ti (H4-1).
HT- (To, ao, 00. ..... Tıx dis 0i. TT, aT);
While more complex single and multi-agent paradigms have emerged, our choice of ReAct is a deliberate one, rooted in its simplicity and alignment with fundamental principles. This decision is heavily informed by "The Bitter Lesson" (Sutton 2019). which posits that general methods leveraging scalable computation ultimately outperform approaches that rely complex, human-engineereed knowledge and intricate designs. Frameworks that require extensive, specialized prompt engineering possess rigid operational structures risk becoming obsolete as the intrinsic capabilities of models scale (Li et al. 2025a).
Action space. The action space is composed of four primary tools: search, visit, Google Scholar, and Python interpreter, along with the terminal action final answer. Details of tools are provided in the Appendix
3 SAILORFOG-QA-V?
This section focuses on the data construction of SailorFog-QA--2 where we introduce how we construct dense knowledge graph containing real internet information and how generate question- answer (QA) pairs based on this data structure.
3.1 GRAPH CONSTRUCTION
An information retrieval problem, its core, can be conceptualized as navigating complex web of entities and their interrelationshis.. To effectively address such problems, especially in the context of advanced AI agents performing "Deep Research", it crucial for models comprehend and leverage these underlying structural connections. Therefore, to ensure our generated QA pairs encompass rich and diverse spectrum of logical relationships, our foundational approach involves constructing a comprehensive knowledge graph. This graph serves as robust substrate from which we can sample various structurally distinct subgraphs, each forming the basis for generating questions that probe different reasoning patterns.
Recent advancements data construction for web agents have also aimed at acquiring such structured information. These methods typically initiate from simple "seed" question, progressively expanding the graph by employing external tools (e.g., search or browsing) to discover related entities and facts (Gao al. 2025 Liu et al., 2025a Tao al. 2025). However, significant drawhack of this "easy-to-hard" or iterative expansion strategy is its inherent tendency to produce predominantly tree-like acyclic logical structures. While effective for certain types of information retrieval, this approach inherently struggles to capture or generate scenarios involving complex cyclic relationships, feedback loops, or intricate interdependencies that are common in real-world knowledge graphs.
Building upon the foundational framework of SailorFog-QA (Li et al. 20255), V2 still starts with seed entity and leverages web tools discover related entities and extract their corresponding information. However, to achieve a more comprehensive topological coverage to overcome the limitations of acyelic graphs, we introduce significant enhancements the graph expansion phase. Specifically, we actively seek out and establish more dense connections between nodes, intentionally creating cyclic structures. This ensures that the resulting graph is not merely a sprawling tree but richly interconnected web, more accurately reflecting the complex, non-linear nature of real-world knowledge. Beyond these structural improvements, we now preserve more complete procedural information, such as the specific search queries used and the source URLs that led to Furthermore, we compute and store various statistical features for each entity, which are instrumental for the subsequent QA generation phase, enabling us craft more nuanced and challenging questions.
discovery.
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3.2 SUBGRAPH EXTRACTION
In the previous version, our subgraph sampling strategy relied on random sampling, with an attempt enumerate all possible substructures of fixed edge count. However, the graph in V2 has become substantially denser, such an exhaustive enumeration is computationally infeasible due to combinatorial explosion. To overcome this scalability issue, we adopt random-waa based approach for subgraph extraction. Ultimately, this strategy enables us to efficiently gather a sufficient quantity of non-isomorphic (verified by Weisfeiler-Leman algorithm (Weisfeiler Leman [968)), connected subgraphs that collectively represent the full spectrum of structural complexities, without the prohibitive cost of a brute-force search.
3.3 QA GENERATION
When generating QA, do not directly feed the subgraph into the LLM end-to-end to produce the result. Instead, we first analyze how many non-isomorphic nodes exist in a given topology, so that the QA focus be evenly distributed across all orbit nodes (i.e., nodes that occupy different structural roles). Moreover, obfuscation has become one of the most common methods for introducing uncertainty and eliciting high-order reasoning patterns in the construction of challenging information-
seeking tasks (Li et al. 20255b Gao et al. 2025: Shi et al., 2025; Liu et al. 2025a, Geng et al.) 2025). Specifically, obfuscation corresponds to the reasoning behavior required when query's key
dements-suche as specific entities, dates, or values- are replaced with more general or ambiguous descriptions. Answering such questions compels the model move beyond simple keyword matching. engaging in contextual inference to disambiguate underspecified entities, generating and verifying hypotheses through iterative information gathering, and synthesizing evidence from multiple sources to converge conclusive answer. However, this set of skills, while crucial, represents only a subset of the capabilities required for a truly super-human web agent. To this end, we introduce a wider array of defined uncertainties beyond simple obfuscation. These include: (1) Semantic Ambiguity: We deliberately under-specify key entities (c.g., "the mathematician who generalized this result" instead of the specific name) dates. This forces the agent to reason over the graph structure to disambiguate entities via context rather than keyword matching. (2) Distractor Noise: We inject plausible but factually incorrect distractors into the context (e.g., publication year of related paper by the same author). This requires the agent to perform active cross veriffciiii and contradiction detection. (3) Structural Constraints: By analyzing non-isomorphic nodes-node occupying distinct structural roles- we identify critical paths (e.g., bridges or cut vertices). We generate questions that require traversing these specific "cut" edges, ensuring the agent engages in global graph exploration rather than local greedy search.
3.4 DATASET STATISTICS
The resulting SailorFog- BailorFog-QA-22 dataset used for SFT and RL consists of over 30,000 high-quality instruction-tuning pairs. The underlying knowledge graphs are densely connected, with an average of approximately 30 nodes and an average degree of 2.5 per connected component. The random- walk sampling strategy, verified by the Weisfeiler-Lemal algorithm, ensures diverse coverage of topological structures, ranging from simple chains to complex cycles and dense clusters.
4
AGENTIC POST-TRAINING
4.1 SET COLD START
The initial phase of our agentic post-training pipeline Supervised Fine-Tuning (SFT) stage, designed to provide the base model with robust initial policy before the commencement of reinforcement learning. To ensure a controlled and high-quality training regimen, our SFT dataset constructed entirely from synthetic data derived from the SailorFog-QA-Y generator. The training trajectories are produced by high-performing, open-source models solving the generated QA tasks, with quality maintained via rejection sampling. In key architectural decision, and departure from foundation
prior work like WebSailor, our agent built upon the Qwen3-30B 1-8-A----Tinnkee model (Yang et al. 2025), with the context length deliberately extended to 128k.
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Utilization
Preservation
Automatic Synthetic
Asyne Call
Collect
Asyne Rollout Service
Rollout Worker
Trajectory
Reward Service
Update
Action
Observation
Simulated Environment
Search
Google Scholar
Environment
Visit
Python Interpreter
Figure 2: An overview of our Reinforcement Learning framework. The agent is trained in closed loop where the policy continuously updated through interactions with simulated or real-world environments. A key component is the automated data synthesis and filtering pipeline, which dynamically curates training data based on the training dynamics.
4.2 AGENTIC REINFORCEMENT LEARNING
Our RL strategy employs a dual-environment approach, leveraging the benefits of both controlled simulation and real-world deployment.
Simulated Environment.
The practice of training in simulation for subsequent policy transfer or
algorithm validation is common and essential strategy in research and development (Da et al. 2025).
successfully applied across domains like robotics and perception (Osinski et al. 2020: Haiderbhai et al. 2024 Ho et al. 2021). Reliance commercial real-world web APIs (e.g.. SerpAPI (SerpAPI
2025) or Jina (Jina.al ai 2025) introduces significant practical constraints, including high operational costs, restricted Queries Per Second (QPS), and output inconsistencies. During the critical initial stages of algorithm research and data curation, these real-world limitations can severely decelerate the development cycle and compromise the reliability of ablation studies.
To circumvent these issues, we constructed a fully controllable simulated environment utilizing offline Wikipedia database and corresponding suite of simulated web tools. We adapted the SailorFog-QA-v2 generation pipeline to operate exclusively on this offline corpus, thereby synthesizing dedicated, structurally complex training and testing dataset that perfectly aligned with the simulation's 's capabilities. This methodology provides cost-efficient and fully observable platform, significantly accelerating our algorithmic experimentation and iteration process.
We utilize this simulator not as a direct content replica of the live web, but as "wind tunnel" for algorithmic stability. Our internal validation demonstrates a high correlation in training dynamics (specifically Reward and @ass@1 1 trends) between the simulator and the real environment. Algorithmic improvements that stabilize learning in the simulator consistently translate to stability in the real world. Consequently, we adopt a staged training strategy: we perform high high-frrquency hyperparameter tuning and reward shaping validation in the simulator, and then execute the final production training run in the managed real-world environment using the validated configuration.
Real Environment. While simulation is invaluable for rapid prototyping, the ultimate objective is real-world performance. Transitioning to a real environment, however, introduces considerable engineering challenges. Our agent relies on multifaceted toolkit integrating various search sources, diverse webpage parsers, and a code execution sandbox. The inherent volatility of external APIs (e.g.. latency, failure, or inconsistent returns) within this composite system poses a significant risk of data contamination, which can obscure the true source of performance degradation -making it difficult to isolate algorithmic deficiencies from environmental instability. To ensure stability, we architected robust, unified tool execution interface. This interface includes a scheduling and management layer that orchestrates tool execution and incorporates sophisticated concurrency handling and fault- tolerance mechanisms. These include QPS constraints, result caching, automated timeout-and-reed protocols, service degradation strategies for non non-critical failures, and seamless switching to backup data sources. This multi-layered design effectively abstracts the tool invocation process into
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deterministic and stable interface from the agent's perspective, successfully insulating the training loop from real-world stochasticity while simultaneously optimizing operational costs.
Data Curation. Data quality is the central driver of enhanced model capability; its importance often surpasses that of the algorithm itself. The quality of the training data directly establishes the upper bound for the model's ability to generalize To address this, we implemented a data optimization loop guided by real-time training dynamics. This optimization achieved via a fully automated data synthesis and filtering pipeline that dynamically curates the training set. By establishing this closed loop between data generation and model training. our approach not only ensures training stability but also yields substantial performance gains by continually feeding the model with the most informative trajectories.
oitoo--distributionn scenarios through self-exploration.
RL algorithm. Our RL algorithm a tailored adaptation of GRPO (Shao et al. 2024):
[(0)-
E ((..)) an (cloontext)
O min (rir(0)AA... clip aip(rr.(0)), (r,,,(0), Etoow: Elow: 1 + Ehigh dne)
G lod
(2)
where (q. y) is the question-anse answer pair, (() is the importance sampling ratio, and At, is an estimator of the advantage at time step t:
Te (o, context) (0,
Åi, Ra mean mean((R.)]
To.. (oi, conteat)
We employ strictly on-policy training regimen, where trajectories are continuously sampled using the most up-to-date policy, ensuring that the learning signal is always relevant to the model's s current capabilities. Following DeepSwe (Luo et al. 2025) and DAPO YYu et al.) 2025b), the training objective is optimized using token-level policy gradient loss. Second, to further reduce variance in the advantage estimation, we adopt leave-one-out strategy (Chen et al.] 2025). Furthermore, we employ conservative strategy for negative samples, having observed that an unfiltered set of negative trajectories significantly degrades training stability. This can manifest as a "format collapse" phenomenon after extended training. To mitigate this, we employ a targeted filtering strategy. We selectively mask the loss for trajectories that exceed the length limit without yielding a final answer, these provide ambiguous learning signals. In contrast, trajectories containing explicit format emros or invalid tool calls are assigned negative rewards rather than being masked, allowing the model actively learn avoid such behaviors. This strategy effectively prevents format collapse while maintaining training stability. For the sake of efficiency, we do not employ dynamic sampling, We instead leverage larger batch and group sizes, which serve to maintain smaller variance and provide adequate supervision.
However, we consider that the algorithm is important but not the only decisive factor in the success of Agentic RL. We have experimented with many different algorithms and tricks, and find that data and stability of the training environment are likely the more critical components in determining whether the RL works. Interestingly, we have tested to train the model directly on the BrowseComp testing set, but the results are substantially poorer than when using our synthetic data. We hypothesize that this disparity arises because the synthetic data offers more consistent distribution, which allows the model to be more effectively tailored. Conversely, the human-annotated data (such as BrowseComp) is inherently noisier. Given its limited scale, is difficult to approximate a learnable underlying distribution, which consequently hinders the model to learn and generalize from it.
EXPERIMENTS
5.1 SETUP
Research Questions To verify our methodological claims, our evaluation is designed to answer two core scientific questions:

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Published as a conference paper at ICLR 2026
(RQ1) Topological Generalization: Does training data derived from dense, cyclic knowledge graphs -aroorooo------oo V2) yield stronger reasoning capabilities compared to standard linear or tree-like data expansion used by baselines?
(RQ2) Environmental Stability:
Does decoupling training into dual-ennironment (Simulation-to- Realy framework effectively mitigate the instability of real-world RI. and lead to better convergence?
We perform SFT and RL training on Qwen3-30B-A3B-2500 Yang et al. 2025). We mainly evaluate our method on six representative and challenging benchmarks:
Models and Benchmarks
One of the most challenging benchmarks introduced by OpenAl to evaluate the proficiency of Al agents in locating hard-to-find, often multi-faceted, information across the internet, which demands sophisticated browsing strategies and reasoning capabilitiis.
BrowseComp-EN Wei et al,, 2025):
BrowseComp-TH (Zhou et al. 2025a): Similar to BrowseComp-EN, but the QAs are in Chinese.
GATA (Mialon al. 2023 A benchmark that requires multi-modality and tool-use abilities. We
only use a subset of 103 cases from the text-only validation subset (Li et al. 2025c Wu et al. 2025a).
Seeaee Mhench-Teamm that focuses on evaluating Al agents' tool usage capabilities, specifically in deep information retrieval and complex search tasks.
2025): : A new, dynamic, professionally allinee benchmark
Humanity's s Last Exam (HLE) (Phan et al. 2025): HLE is a global collaborative effort, with questions from nearly 1,000 subject expert contributors affiliated with over 500 institutions across 50 countries - comprised mostly of professors, researchers, and graduate degree holders.
DeepResearch Bench (Du et al. 2025): This benchmark is comprised of numerous PhD-level research tasks designed to evaluate the performance of deep-research agents, specifically focusing on the quality of their generated research reports and their proficiency information retrieval and collection.
Baselines For proprietary models accessed via API manual testing, maintained consistent evaluation conditions to ensure fairness, enforcing 128k context token limit and a maximum budget of 100 tool calls, consistent with our agent's settings. We compare our method with the following paradigms:
Proprietary Browsing Agents: We test Gemini-2.5-pro- pro--Deppeeeer (Team] 2025c), Claude-
Research TTem 2025a), Doubao- Deepresarc (Doubaol 2025), Perplexity- 2025g). Grok-Deeper-Search (Team 2025d), Claude-4-Sonnet (anthropic 2025). OpenAT- o3 (OpenAl 2025b), OpenAI DeepResearch (OpenAl 2025a); however, not all of them are fully accessible via API, they were not tested across all benchmarks and experiments.
(Team)
Open-Source Agents:
We compare our method with recent open-source web/search agents, including Nee (Gao al., (025). Minoohhnnker-33--POOO.
(Team] (025e) 2025e).
(Team, 2025f), DeepDive-32B (Lu et al. 2025), Kimi-K2- (Tea et al. 2025 GLM-4.5 Zeng el al. 2025), DeepSeek-V3. (Team 2025b
WebSailor-72B, WebExpporer-SB, DeepDiver-V2-22
Training Data Our training data is primarily composed of SailorFog-QA (Li et al. 2025b) and SailorFog-QA-Ve In addition, we supplement this data with IterBench (Qiao et al., (025) to bolster the model's proficiency in mathematical and academic reasoning. Qualitatively, distinct data sources target different capabilities. SailorFog -AA provides the foundational seaffold for web navigation; SailorFog QA- with its dense cyclic structures and injected uncertainties, critical for the complex reasoning and error-correction observed BrowseComp tasks; and the supplementary IterBench data specifically bolsters the mathematical and academic reasoning required for benchmarks like HIE..
observe that
Metric and Hyper- paramters We default to pass @ l evaluation (Chen et al. (021) and report pass @ 1, and temperature and top-p set 0.85 and 0.95. For accuracy, L.I a judge (Liu

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Table I: Graph-based training data enables superior reasoning generalization (Evidence for RQ1), WebSailor- V2 outperforms baselines trained on linear/tree-like synthetic data by significant margin on complex benchmarks like BrowseComp. indicates that these proprietary methods are manually evaluated through their websites (some are reported in the corresponding papers). means that we do not have the results due to cost constraints.
Backhone
BrowseComp-Ei
BrowseComp-ZH
xbench-DeepSeare
GAIA
HLE
Proprictary Agents
Clande-4-Sonet
12.2
29.1
64.6
68.3
20.3
Claude-4-Opus
18.8
OpenAl-o3
49.7
58.1
66.7
70.5
20.2
OpenAl DeepResearch'
51.5
42.9
67.4
Kimi Semi-Researcher"
69.0
26.9
Open-Source Agents
ASearcher-Web-355
5.2
15.6
42.1
52.8
12.5
MinoThinker:32--DPP--000
13.0
17.0
64.1
11.8
WebSailor-72B
12.0
55.0
55.4
WebExplorer-8B
15.7
32.0
53.7
50.0
17.3
DeepDiver-V2-11 V2-38B
13.4
34.6
$3.0
DeepDive-32B
14.8
25.6
$0.5
Kimi-K2-Instruue
28.8
50.0
57.7
18.1
GLM-4,5-355B
26.4
37.5
70.0
66.0
21.2
DeepSeck-V3.116777
30.0
49.2
71.2
63.1
29.8
DusSailo--2230B-AABB (SFT)
24.4
28.3
61.7
66.0
23.9
WebSailor- -2B-3BB--- (RL)
35.3
44.1
73.7
74.1
30.6
ct al. 2024 Wang et al. 2024), strictly utilizing the official evaluation prompts and designated model versions to ensure comparability. The pass is computed as:
3
pass
Pi
where p; denotes the correctness of the i-th response. For @ass@k that k >> we repeatedly generate for & times.
5.2 MAIN RESULTS
Our main experimental results, summarized in Table I unequivocally demonstrate the superior performance of WebSailor- V2---BB--------- Across a diverse suite of web-agent benchmarks, our model consistently achieves state-of-the-ar results among open-source solutions and proves highly competitive with top-tier proprietary agents. On the extremely complex BrowseComp-EN and BrowseComp-ZH benchmarks, which demand sophisticated, multi-step reasoning and information synthesis, WebSailor-Ve V2 scores 35.3 and 44.1 respectively, significantly outperforming all other open-source agents. On relatively more straightforward but still challenging benchmarks like xbench- DeepSearch and GAIA, our agent not only leads the open- sper-source field but surpasses even the strongest proprietary systems.
Another compelling result is on HLE, a benchmark designed to test deep academic and logical reasoning. Here, WebSailor-V2 achieves score 30.6, establishing new state-of-the-ar.. This is particularly noteworthy as it exceeds the performance of much larger and more powerful models, including 67IB parameter DeepSeek- 13.. and proprietary models like OpenAl-o3. This result strongly validates our core hypothesis: equipping model with exceptionally strong information retrieval and synthesis capabilities can profoundly enhance logical reasoning abilities, allowing it effectively "reason over" externally acquired knowledge and overcome the limitations of its intrinsic seale. We believe agentic paradigm a good way to close the gap between strong and weak models.
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These results highlight the indispensable role of the SFT cold-start stage, especially for relatively scall-scale models. As evidenced in Table our model after SFT alone already exhibits formidable capabilities, achieving a score of 24.4 on BrowseComp-En and 23.9 on HLE, surpassing many fully-trained open-source agents. This strong initial policy is not merely an intermediate checkpoint but a critical prerequisite for the success of reinforcement learning. The complex, open-ended nature of these tasks means that rewards are often sparse. Without competent initial policy from SFT, an agent would struggle to conduct meaningful exploration, rarely completing tasks successfully and thus failing to receive the positive feedback needed for learning. The SFT phase ensures the agent starts with robust enough policy to explore the problem space effectively, providing sufficiently dense reward signal for the RL algorithm to stabilize and converge towards superior final policy.
5.3 MORE COMPARISON WITH PROPRIETARY AGENTS IN DEEP-RESEARCH TASK
Evaluating proprietary agents is inherently challenging, particularly for those available exclusively through web interfaces. To validate that WebSailor- V2 (based on Qwen3-30B-A3B- performs on par with much larger proprietary models, we select the DeepResearch Bench. This benchmark is ideal as its leaderboard provides direct comparisons to leading closed- source agents and assesses key capabilities in both information retrieval and report generation. The results (Figure B) underscore our model's competitive performance.
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WebSailor- V2 achieves score of 47.7, ranking second only to the state-of-the-art Gemini-2.5-pro. DeepResearch (49.7). We attribute this narrow gap to our training strategy, which prioritizes core information retrieval and reasoning over the stylistic polish of the final report. Therefore, this gap reflects a targeted area for improvement in the presentation layer, rather than fundamental limitation in its research capabilities.
Figure 3: Comparisons with proprietary agents. The metric here the overall score defined in DeepResearch Bench.
5.4 DETAILED ANALYSES
Training dynamics. The training dynamics of our RL process are depicted in Figure 4 As illustrated, stabilized environments lead to consistent policy improvement (Evidence for RQ2). The clear upward trend in reward (left) confirms that our dual-enviromment approach successfully mitigates the "destructive updates" often seen in noisy real-world RL. The agent is effectively learning and refining its policy within the training distribution. This improvement successfully translates to our validation benchmarks, where performance on both BrowseComp-Eii and BrowseComp-2D ZH shows a corresponding, albeit oscillating, upward trajectory.
However, we observe noteworthy divergence in learning patterns between difficult and simpler benchmarks. On challenging benchmarks like BrowseComp. both pass@1 and pass@3 scores demonstrate a distinct and concurrent rise (shown in Fig. ( This suggests that for complex tasks, RL is genuinely expanding the model's fundamental problem-solving capabilities, increasing the overall likelihood of finding correct solution path within a few attempts. In contrast, for simpler benchmarks such as DeepSS and GAIA, we see a significant improvement in pass @ I, while the gains in pass@3 marginal. This indicates that for tasks already well within the model's base capabilities, the primary role of RL is to enhance sampling more reliably select the optimal path its first attempt (uue et al. 2025). For these simpler problems, the model is already likely to find a solution, so RL's main contribution is making that initial attempt more robust. This also implies that for truly difficult problems, even pass@) may not be sufficient to fully reflect the upper bounds of the model's enhanced capabilities.
the agent to
Context scaling of WebSailor-Ve V2. Figure J the relationship between accuracy, context length, and the number of tool calls the BrowseComp-ENN In this setup, cases where the context

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BrowseComp-EN Avg Pass@1
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Figure 4: Training dynamics of rewards curve and performance testing benchmarks.
Accuracy vs. Budget
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Figure 5: Effects of context and tool call budget for agent.
Figure 6: Accuracy improvements by RL across four benchmarks.
length or the tool call budget exceeds the predefined limit all counted as incorrect answers. The results show clear positive correlation: as the available context length increases, the agent's 's accuracy progressively rises before gradually converging. We observe that nearly 90% of the correctly solved instances completed within context of 64k. Notably, achieves an accuracy around 16 on BrowseComp-EN. This marks significant improvement over its predecessor, WebSailor- V1. The advancement is particularly compelling given that WebSailor-V V1 built on a 72B dense model, which, in principle, possesses greater intrinsic capacity than the 30B MoE model used here. This highlights the profound impact of our improved data and training pipeline on the agent's fundamental reasoning and tool-use capabilities, allowing a smaller model to achieve superior performance.
32k context limit, WebSailor- V2
CONCLUSION
In this work, present WebSailor-V2s V2, framework that bridges the gap between open-source models and proprietary deep research agents. Beyond achieving state-of-the-ar performance with 30B model, our work validates two critical scientific insights regarding agentic learning: (1) Topological Generalization: We demonstrate that the logical structure of training data is as critical as its scale. By shifting from standard linear data expansion cyclic, graph-based synthesis (SailorFog- QA-V22, we show that forcing agents to navigate dense, interdependent relationships during training significantly enhances their ability to generalize to complex, multi-step reasoning tasks. (2) Environmental Stability in RL: We identify environmental stochasticity as a primary bottleneck for on-policy reinforcement learning. Our results confirm that a symbiotic dual-environment wotwwook-decoupling algorithmic learning in high-fidelity simulator from adaptation in the managed real world-is essential for mitigating destructive updates and ensuring stable convergence. Ultimately. WebSailor-V2 V2 proves that with high-density data topology and stabilized training environment, moderately sized open-source models can rival significantly larger proprietary systems, offering reprodueible path toward democratizing deep research capabilities.
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ACKNOWLEDGMENTS
Zhi-Qin John Xu is sponsored by the National Natural Science Foundation of China Grant No. 92270001, 2025 Key Technology R&D Program "New Generation Information Technology" Project of Shanghai Municipal Science and Technology Commission.
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A THE USE OF LARGE LANGUAGE MODELS
We confirm that LLMs were used solely for proofreading and language polishing of the manuseript. They were not involved in any aspect of research ideation, experimental design, data analysis, interpretation of results, or the generation of original content. The intellectual contributions and scientific conclusions presented in this work are entirely the authors' own.
B RELATED WORK
The field of autonomous web agents has witnessed a surge of progress in recent months, with the open-source community rapidly advancing capabilities along three primary axes: data construction, training methodologies, and inference paradigms.
Data construction for web agents. High hugh-quality data is the bedrock of capable agents. Recent methodologies for constructing agent training data can be broadly categorized into two main approaches. The first, pioneered by WebSailor (Li et al.) (2255)) with its SailorFog-QA dataset, graph-based. This approach begins with seed entities and uses web tools to build a knowledge graph, from which complex question-answer pairs are sampled. The second, paradigm, is employed by works like WebShaper (Tao et al. 2025). ASearcher (Gao et al., 2025). and WebExplorer (Liu al. 2025a). These methods typically start with a simple seed question and iteratively expand its complexity, resulting in tree-like logical structures. common thread connecting many of these recent efforts, starting with WebSailor, is the integration of live web tools into the data generation process and the introduction of uncertainty, most notably through obfuscation, elicit more advanced reasoning. In contrast these works, our SailorFog QA-V2 achieves more comprehensive coverage of complex logical relationships that better mirror real-world information webs and more definitions uncertainty.
messytto-hare
Agent training strategies. A two-stage training pipeline has become the de facto standard for developing powerful agents: SFT "cold start" phase followed by RL phase for policy refinement. The majority of recent RL implementations are based on variants of GRPO (Shao et al.) (024), often incorporating algorithmic enhancements and tricks from methods like DAPO (Yu et al. (025b) and Dr.GRPO (Liu et al.] 20255). While these algorithmic nuances exist, our extensive experimentation suggests that the specific RL algorithm is not the primary bottleneck for agentic RL at this stage. Instead, we find that the quality and distribution of the training data fundamentally determine the upper bound of the training's ss effectiveness. The careful selection of training samples, particularly how negative trajectories are handled, appears to be one of the most critical factors for stable and effective learning. Continual pre-training another specialized training paradigm that can further enhance reasoning abilities (Su et al. 2025).
Inference paradigms. The choice of inference paradigm significantly impacts agent's performance. WebSailor and WehShaper are built upon the vanilla ReAct framework (Yao et al. 2023) for its simplicity and effectiveness. Concurrently, context engineering (Yu et al. 2025a Zhou et al., (025b) has emerged as crucial area of innovation. Works such as ASearcher and Kimi-Researcher (Kimi 2025), as well as GUI-focused agents like UI-TARS-2 (Wang et al. 2025). have demonstrated that sophisticated context management strategies built on top of ReAct can yield sigmificant performance improvements. For WebSailor- V2, we deliberately adopt the standard ReAct framework. This choice is intended to isolate and evaluate the intrinsic capabilities of the model itself, minimizing the confounding effects of intricate prompt engineering or framework design. By establishing this strong baseline, we pave the way for future work to explore how advanced context strategies or plug-in modules can further unlock the model's full potential.
Despite the rapid proliferation of open-source agents, considerable performance gap has persisted when compared to proprietary systems like OpenAl's DeepResearch (OpenAll (025a). WebSailor- V2 represents dedicated effort to bridge this divide. demonstrating for the first time that meticulously trained agent built on a moderateey-sizerr open-souree model can achieve performance that is highly competitive with, and in some cases superior to, its closed-source counterparts.
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C
EXPERIMENTAL DETAILS
Tools WebSailor- V2 uses four types of tools, search, visit, Google Scholar, and Python interpreter:
Search is used to access the Google search engine for information retrieval. The parameters Search are the search queries. allows searching multiple queries simultaneously and returns the top-10 results for each query. Each result contains title, snippet, and the corresponding URL..
Visit is used to access specific web pages. The input consists of several web pages and their corresponding visit goals, with each page having dedicated goal. First, Jina (Jina.ai 2025) is used retrieve the full content of the web page, and then summary model extracts relevant information based on the goal. In this work, we use Qwen3-30B-Ar Yang et al. (2025) the summary model.
Google Scholar a specialized search tool that accesses the Google Scholar search engine. It is designed for information retrieval within the academic domain, allowing the agent to find and access scholarly literature such as articles, theses, books, and conference papers.
Python interpreter is sandboxed environment that allows the agent to write and execute Python code. This tool enables the agent to perform complex computational tasks, such as mathematical calculations, data analysis, and logical reasoning, by running self seff-generated code in a secure and isolated setting.
Training hyper-parameters We use Megatron (Shoeybi et al. 2019) for SFT and rLLM Tan et al. 2025) for RL training. For SFT. we use batch size 64, learning rate of 5e-6 with a minimum of 1e-10, warmup plus cosine decay schedule, and weight decay of 0.1. For RL training, the temperature 1.0, topp = 1.0, the batch size 128, and the learning rate is le-6.
D ENTROPY DYNAMICS
The entropy dynamics, shown Fig. 7 provide further insights into the learning process. We find that the policy entropy remains consistently high level throughout the training process, indicating that the agent maintains strong capacity for exploration and avoids premature convergence to deterministic policy. This behavior contrasts sharply with trends observed in tasks like mathematical RL training, where entropy often decreases significantly as the model learns to exploit a narrow of solution paths. In our case, the entropy oscillates without clear upward or downward trend. Consequently, our algorithm design intentionally omits any explicit entropy regularization or bonus, as the agent naturally sustains sufficient exploration. We hypothesize that this sustained high entropy is direct consequence of the environment's non-stationary nature. Unlike mooeed-wrldd problems, the observations returned by web tools (e.g., search results, webpage content) do not follow fixed distribution. This inherent stochasticity and complexity of the real-world web environment prevent the policy from fully converging to a stable, low-entropy state, instead fostering more robust and adaptive policy.
Entropy
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Figure 7: Training entropy dynamics
E
CASE STUDY
We present from the BrowseComp benchmark, wherein the agent successfully identified the correct company after a comprehensive reasoning process spanning 29 steps. This case demonstrates series of advanced reasoning patterns executed through efficient tool invocation.
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1. Clue Decomposition and Structuring: In its initial step, the agent deconstructed the user's unstructured, multi- fallti- query into a set of clear, verifiable, and structured conditions. This foundational process of decomposition is essential for solving complex problems by breaking them down into manageable sub-tasks.
2. Initial Exploration and Strategy Adjustment: The agent did not arrive at the correct answer immediately. Its initial search queries were broad and exploratory, such as "former employee class action settlement $1.5 million 2015". These searches returned irrelevant results pertaining to companies like McDonald's and FedEx, which were too generic to be correlated with the other specific clues. This demonstrates the agent's ability recognize unproductive search paths and adjust its strategy accordingly.
3. Identifying the "Golden Clue":
Following the unsuccessful initial attempts, the agent identified the need to pivot to more targeted approach. It reasoned that the most effective strategy was to focus on the most unique and casily locatable piece of information: the leadership change. Consequently, it constructed highly precise search query: "founder" "will become" "Chairman" "effective" "third quarter" "2008". This query targets a specific corporate event within narrow timeframe, significantly increasing the probability of relevant hit.
4. Target Acquisition (The Breakthrough): precise query successfully identified the target. The search results pointed directly to two press releases from FormFactor Inc., detailing that its founder, Igor Khandros, would become Executive Chairman while new CEO would be appointed at the beginning of the third fiscal quarter of 2008. At this juncture, the agent had identified high-potential candidate.
5. Systematic Verification: Identifying candidate was merely the first phase. The subsequent steps showcased the agent's rigorous and methodical verification capabilities. Instead of prematurely concluding, the agent systematically validated every other clue against FormFactor, Inc. This involved a series of targeted searches and document analyses to confirm details about its incorporation, financial history (customer concentration), and legal proceedings.
6. Synthesis and Final Response: Only after every clue independently and irrefutably verified did the agent synthesize all the evidence. It then constructed a coherent, logically structured final answer, complete with citations and summaries of the source material for each key fact.
This case perfectly illustrates the core competencies of an advanced Al research assistant. The process highlights its ability to engage strategic adaptation and Iear from initial failures halimarks of true reasoning process rather than simple answer-retrieval mechanism. The entire workflow-decomposs the problem, identifying key pivots, executing precise searches, and performing cross- - mirrors the sophisticated research methodologies employed by human experts.
Case Trajectory in BrowseComp-en
Question: A publicly traded company reported that, in single fiscal year between January 1, 2004, and December 31, 2006, three customers contributed between 27% and 74% of its revenue. In different fiscal year, between January 2004, and December 31, 2006, four customers accounted for between 55% and 89% of its revenue. In a month that falls between January 1, 2011 1, and December 3.1 2019, a former employee of the same company filed a class action lawsuit. The court later certified the plaintiff's s class. The settlement was somewhere between 1.2 and 1.9 million dollars. According to a source published in 2008, the founder of the company changed roles within the company, with this change set to take effect at the beginning of the fiscal third quarter, which falls between January 2007, and December 31, 2008. The change mentioned above also instates new person CEO. The company was formed in the jurisdiction of Delaware. The company was founded sometime between January 1, 1988, and December 31, 1995. What is the name of the publicly traded company?
Step 1
<think>
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Published as a conference paper at ICLR 2026
We need find the of a publicly traded company that matches these clues. The clues:
It's publicly traded company incorporated in Delaware, founded between 1988 and 1995. 2. In some fiscal year between 2004 and 2006 (inclusive), three customers contributed between 27% and 74% of its revenue. In another fiscal year between those years, four customers accounted for between 55% and 89% of its revenue.
former employee filed a class action lawsuit between Jan 2011, 1, and Dec 31, 2019. The court later certified the plaintiff's class. The settlement was somewhere between $1.2M and $1.9M.
source published in 2008, the founder changed roles within the company, with the change set to take effect at the beginning of the fiscal third quarter, which falls between Jan 1, 2007, and Dec 31, 2008. The change also installed Thus we need to identify company where a founder stepped down from CEO or changed role to something else, and new CEO was appointed, effective early Q3 of fiscal year 2007-2008. The company must have been incorporated in Delaware, founded 1988-1995. Also, financial data: three customers contributed 27% 74% of revenue in one fiscal year (2004-2006) and four customers contributed 55% to 89% of revenue in another year. That suggests the company perhaps a supplier few major customers; maybe a semiconductor company, defense contractor, etc.
4. According
person CEO.
The class action: a former employee filed a class action and settlement between $1.2M-$1.9M. Could be about wage and hour claims, ERISA, securities fraud? Might be an ESG claim?
Let's think.
Potential candidates: companies like "Burlington Resources", "Ciena", "Viatris"? No. Let's parse timeline: The founding date range is 1988-1998 Many tech firms were founded earlier (1970s, 1980s). Possibly small-to-mid cap.
The founder changed roles in 2008, effective Q3 of fiscal year 2007-2008. So the new CEO took over after that. Founder might have been originally CEO, but stepping down become chairman or something.
One known case: "American Tower" founder? No. "Amdocs"? Founded 1982.
Perhaps "Covance"? Not sure.
Better to search for "founder stepped down as CEO effective third quarter 2007 2008" "Delaware corporation". Also include "class action former employee settlement $1.5 million".
Let's do web search.
</think>
<tool_call>
("name": "search". "arguments": ("query": ["former employee class action settlement $1.5 million 2015". "founder stepped down CEO effective Q3 2007 new CEO 2008 Delaware corporation", "three customers contributed 27% of revenue 2005 Form 10-K", "four customers accounted for 55% of revenue 2006 Form 10-K"]]]
</tool call>
<tool response>
Search Results are omitted...
</tool_ resposeessss
Step 2
<think>
The search results are generie; we need to refine. Better approach: Identify a company where founder stepped down as CEO effective Q3 of fiscal year 2007-2008 Could be technology biotech firm that went public earlier (maybe 1990s). Look up "Q3 2007 founder becomes chairman new CEO". Search for "founder will step down as chief executive officer and become chairman of the board beginning in the third quarter of fiscal 2007".
</think>
<tool <tool_call>>>>>>
("name": "search", "arguments": ("query": ('"beginning of the third quarter" 2007 founder CEO stepped down". ""effective" "third quarter" 2007 "new CEO"", ""founder" "stepped down" "CEO" "July 2007" "Delaware""]]
18

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Published as a conference paper at ICLR 2026
</tool call>
<ttoo_ecal>>>>>>>__e___ resporssssss>
Search Results are omitted...
Step 3
<think>
We got a result: "sv1" (SEC filing) that might be relevant. Let's open that.
</think>
<tool <tool_call>
("name": "visit", "arguments": f"url": https///wwww.sccgg//////v/////// 1555//99/0//2599//0000
089102008000014/00 14000044/2895orrv 1.tm"), "goal": "Find information about founder and CEO change 2007, effective beginning of third quarter")
</tool_calll call>
<tool res_orrsssss>>>>
Visit results are omitted...
Step 4
<think>
The page may not have relevant info. Let's search more specifically: "founder will become executive chairman effective third quarter 2007"
</think>
<tool <all>>al>>>>slllllll
{ ("name": "search", "arguments": ""query": ["""fuuder" "effective" "third quarter" "2007" "CEO"" ""founder" "will" "become" "Chairman" "third quarter" 2007"". ""founder" "transition" "CEO" "third quarter" 2007""]])
</tool_call
etooo_responss
Search Results are omitted...
</tool response>
Step 5
<think>
Result #1: "National Interstate Corporation Announces Third Quarter 2007 Dividend and
CEO Promotion." That mentions founder. Let's open.
</think>
<tool call>
""name": "visit". "arguments": ("url": http::/wwww..glbbbeewwwww....oos Inttriintt--nteettttee-e----
hwa.riaammmmmeetetoooooooooooono Corporation Third- C
html"), "goal": "Find details about founder,
CBO change, effective Q3 ]
</tool call>
<tool response>
Visit results omitted...
<tooo_response-
Step 6
<think>
This appears to match founder change: founder Alan R. Spachman (founder of National Interstate Corporation) stepped down as CEO effective Jan 1, 2008, which is the beginning of fiscal Q3? Wait, what's 's National Interstate's fiscal year? It may be a calendar year or something else. The date effective Jan 1, 2008 is effectively start of fiscal year 2008; Q3 might be July 2008? But they said "beginning of the fiscal third quarter", which falls between Jan 2007 and Dec 31, 2008. Possibly the effective date the start of Q3 in fiscal year 2008 (e.g., July 1, 2008). However, the article says effective Jan 1, 2008. That is the start of the fiscal year for calendar-year company, but necessarily Q3.
But perhaps the phrase "beginning of the fiscal third quarter" means the start of Q3 of the fiscal year that begins in April (or earlier). For many insurance companies, the fiscal year
ends December 31 (so Q3 starts July 1). If the change takes effect at the beginning of Q3, it
19

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Published as a conference paper at ICLR 2026
would be July 2008 maybe.
Nevertheless, we have founder stepping down and new CEO taking over, which matches part of the clues. Also, National Interstate Corp (NATL) publicly traded, incorporated in Delaware? Let's check.
Search "National Interstate corporation incorporated in Delaware".
</think>
<tool_call>
("name": "search", "arguments": ("query": [ (""National Interstate Corporation" incorporated in Delaware"]))
</tool call>
<tool_ reolrresssss>>>
Search results are omitted
</tool_response>
Step 7
<think>
Thus maybe it's not National Interstate Corp. Let's keep searching.
Also consider the founder change: The founder changed role effective beginning of fiscal Q3
2007-2008. That may refer to a different company.
Another candidate: "Roper Technologies"? Founder? Not
Mayhe a small biotech like "Cerus Corporation"? Or "Bio-Rad"? Or "Illumina"? No.
Let's think about companies founded between 1988 and 1995.
List of publicly traded companies founded within that period:
Netflix (founded 1997, out) Salesforce (1999)- Intuit (1983) Dell (1984) - HP (1939)-
Apple (1976)- - IBM (1911) AMD (1969)- Qualcomm (1985) - Juniper Networks (1996)- Akamai (1998) Red Hat (1993) but now part of IBM Goodyear? No. MicroStrategy (1989) but maybe. VeriSign (1995). Workday (2005). Splunk (2003). Etsy (1999).- PayPal (1998). - eBay (1996). Google (1998). Many others.
But need founder stepping down as CEO effective Q3 2007-2008 and new CEO appointed. Also customers contributions to revenue percentages are relatively high (27% to 74% from three customers; and 55% 89% from four customers). That suggests maybe B2B or service provider with few large clients, possibly a contract manufacturer, semiconductor foundry, defense contractor.
Maybe the company is "Qualcomm"? But Qualcomm's 's customers are many. Alternatively, "contract research organization" "clinical trials" firm.
Could be "Perrigo"? Not sure.
Maybe the company "Voya Financial"? "? Not likely.
Another possibility: " "Avid Technology" (AVDL). Founded 1987.
Or "VeriSign". Founded 1995. Founder is Jim Bidzos? Actually the founder is Dr. Roger D. Dierking? Not sure. Not aware of CEO change at that time.
We might need to search for "class action former employee settlement $1.5 million 2015"
plus "NATL"? But NATL had class action? Not sure.
Better to systematically examine each clue and search for relevant combos.
Clue 1: "In single fiscal year between Jan 1, 2004 and Dec 31, 2006, three customers contributed between 27% and 74% of its revenue. In a different fiscal year, between Jan 1, 2004 and Dec 31, 2006, four customers accounted for between 55% and 89% of its revenue." Interpretation: In one year there were three major customers that contributed some portion of revenue between 27% and 74%. This a range because maybe we don't 't have exact numbers. In another year there were four customers that contributed between 55% and 89% of revenue. So these percentages could be for the same company across years.
Thus the company had heavy reliance on few customers.
This is typical for companies that sell to government agencies (e.g., defense contractors), or to big telecoms (e.g., chip manufacturers selling to phone makers), Could be an aerospace parts supplier (c.g.. "Spirit AcroSystems").
Clue 2: "In month that falls between Jan 2011, and Dec 31, 2019, former employee of the same company filed a class action lawsuit. The court later certified the plaintift's s class. The settlement somewhere between 1.2 and 1.9 million dollars." " So moderate-she class action, perhaps wage/hours or discrimination, not huge. Settlement $1.2-$1.9M.
20

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Published as a conference paper at ICLR 2026
Clue "According source published in 2008, the founder of the company changed roles within the company, with this change set to take effect at the beginning of the fiscal third quarter, which falls between Jan 1, 2007, and Dec 31, 2008. The change also instates new person as CEO."
Clue Company formed in Delaware.
Clue 5: Company founded between 1988 and 1995.
Thus we need Delaware-incorporatee public company founded 1990ish, with founder stepping down around mid-2008.
Now let's search for "founder steps down as CEO effective July 1 2008".
</think>
<tool_ cool_cal>>
("name": "search", "arguments": ("query": [""effective July 2008" "CEO" "founder""])
</tool_call>
<tool_ @ool_response>
Search results are omitted
</tool response>
Step 8
<think>
No obvious hits.
Search for "founder will become chairman the board effective third quarter 2008"
</think>
<tool_call>
("name": "search", "arguments": ("query": ["""ounder" "will become" "Chairman"
"effective" "third quarter" "200
</tool <all>
<tool_rest res_oresps
Search results are omitted
</tool_ responses
Step 9
<think>
Great! This appears promising: "FormFactor Board Appoints Mario Ruscev FormFactor
CEO", Let's open.
</think>
<tool_call>>
("name": "visit", "arguments": f"url":
fertailsfformaaato--bbrrtt aapponts-mmario caario-ruscev--ee www-rruuuceeeeieeeeeeeeee soweeee-foomfactoocccooooo "goal": "Find appointment")) </tool_call> call>
details about founder role change effective beginning of fiscal third quarter 2008, new CEO
<tool response>
Visit results are omitted
</tool response>
Step 10
<think>
Great! This matches the clue: founder changed roles, effective at beginning of fiscal third quarter 2008. Founder becomes executive chairman; new CEO appointed.
Now we need verify other clues for FormFactor.
FormFactor is publicly traded company on NASDAQ: ticker FORM. It was incorporated in Delaware? Let's check.
</think>
<tool_call>
I"name": "search", "arguments": ("query": [""ForFFactor" "incorporated in Delaware"
</tool call>
<tool response>
Search results are omitted
21

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Published as a conference paper at ICLR 2026
</tool response>
Step 11
<think>
That matches the incorporation and founding date range: incorporated in Delaware in 1993, founded between 1988-1995.
Now need verify the revenue concentration data for fiscal years 2004-2006.
Search FormFactor 2005 10-K "customers accounted for" etc.
</think>
<tool <tool_call>
{ I"name": "search", "arguments": ("query": ["""ForFaatoor" "three customers" "revenue"
2005", ""FormFactor" "customers accounted for" 2005", ""FormFactor" "Revenue" "customer" 2006"]]]
</tool <all>
<tool response>
Search results are omitted
Step 12
<think>
Let's open the 2005 annual report PDF for FormFactor.
</think>
<tool call>
{" I'name": "visit", "arguments": ("url": ["httss//www.amuulrrpp www.aauwwrrpoot..ooooooot/tt///a com/HosteeDDDttt//muulR
portArchive//NAASAA FORM FORM_2005.ppoe 2005.pdf'), "goal": "Find revenue concentration: number
of customers and % of revenue in fiscal 2005")]
</tool _aall>
<tool <tool_resppl ree_orssssss>>
Visit results are omitted
</tool response>
Step 13
<think>
Let's try another source: maybe the 10-K filing for 2005. Use SEC filing.
</think>
<tool call>
("name": "visit", "arguments": ["url": ("tttss/wwww..cc.oo//rrrdd www.sscc..o./rcchhi///aaaaaaaaa ottpg//rrr//aaaa/0039///////0 1039399/000 fiscal 2005 and 2004")]
"goal": "Find revenue concentration information (customers) for
</tool call>
stool_response
Visit results are omitted
</tool responses
Step 14
<think>
The SEC site might require proper path. Let's locate the 10-K for FormFactor for fiscal year ended March 31? Actually FormFactor's fiseal year ends on December 28? Or something like that. But we can search for "FormFacto 2005 10-K".
</think>
<tool_call
("name": "search", "arguments": ("query": [""ForFFattor" "10-K" "2005""]]
<tool_response
Search results are omitted
</tool response>
Step 15
<think>
Let's open the SEC filing for 2005 (ID 1).
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Published as a conference paper at ICLR 2026
</think>
<tool_call
("name": "visit", "arguments": {"url": ("hhtps:/investtrs.fooo wow.:/ocrooooommcoooooooooooooo com/sec-- com/scc-illnngsssee htmp://0-k0000955134405000000000 2005 and 2004"))
"goal": "Extract revenue concentration data for fiscal
</tool_call>>
<tool_ree rool_response>
Visit results omitted
</tool reoporeessos>>>
Step 16
<think>
may require navigation to view the HTML content. The link likely points to a page with viewer that loads PDF separate HTML file.
Let's click the "View HTML" maybe.
Search for the HTML file with Jttm after the hash. The earlier result #4 from search result had an external link to open that.
.html". Let's
</think>
<tool_call>
("https//ccottentcddar htw.://0w0....auhhooaaaaaaaaaaaa httpocccccb.3eo.aaeaa/////eeeeee "Find revenue concentration: customers accounted for X% of revenues")
"name":
"visit".
"arguments". urrll:
online.
al Icdd9866be4dddl
10w48_ (0w4__htm"), "goal":
</tool_cail _all>>
<tool_response:
Visit results are omitted
</tool response>
Step 17
<think>
The API may have restrictions for SEC content. Let's maybe try to view the PDF file directly (annual report PDF). The PDF may contain revenue concentration. Open the PDF via annualreports.com. Maybe we can extract text.
</think>
<tool_call
("name": "visit", "arguments": ("url": htttpp//www..auuuulrpprrtoooot/s
Repprttrchive///AAADADFFOOO 200MMM_000ppd"'", "goal": "Extract revenue concentration details"))
</tool_cail _alll>
<tool response>
Visit results are omitted
</tool @tooo_response
Step 18
<think>
Great! This gives the revenue concentration.
Now we need to verify the other year: maybe 2004 had three customers contributed between 27% and 74%? Wait the clue: "In single fiscal year between Jan 2004, and Dec 31, 2006, three customers contributed between 27% and 74% of its revenue." Actually the phrase: "three customers contributed between 27% and 74% of its revenue" it's ambiguous: maybe that in some fiscal year three customers contributed 27% and 74% (range?). Or maybe there single fiscal year where the three customers' combined contribution fell somewhere between 27% and 74%. The range suggests that the figure could be anywhere within that range, but not exact. For FormFactor, we see four customers contributed 72.8% in 2005, which falls within the range 55-89% for four customers; also four customers contributed 64.8% 2004, within 55-89%; and 2003 they contributed 66.2% as well. However, the clue mentions one year with three customers, not four. But maybe there is year with three customers? Let's check the 2006 or 2003? The PDF shows 2005 and 2004 have four
23

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Published as a conference paper at ICLR 2026
customers. Could there be year earlier like 2003 where only three customers accounted for some percentage? Let's open the 2004 annual report (maybe the 2003 10-K). Alternatively check 2006 10-K.
</think>
<tool call>
"nname": "visit", "arguments": ("url": ("ttps:/wwwamuulleeprt...omm http://wwwwaaaartoooooo/oo/o//aa FFtpii////AAADAAAAFFLLLLA
"goal": "Find revenue concentration for
fiscal 2004"))
</tool call>
<tool response>
Visit results are omitted
</tool_restee resposses
Step 19
<think>
Thus, 2004 has four customers contributing 64.8% of revenue.
Now the clue says a single fiscal year between January 1, 2004, and December 31, 2006, three customers contributed between 27% and 74% of its revenue." This could refer to year where three customers accounted for something within that range. Possibly 2005 had three customers? Actually 2005 had four customers; but perhaps there another year where only three customers contributed? Let's check 2006 10-K. Search "FormFactor 2006 10-K revenue concentration".
</think>
<tool_call>
{"
I"name": "search", "arguments": ("query": (""ForrFactor" "2006" "four customers"
"revenue"". ""FormFactor" "2006" "revenue" "customers"""]
</tool_cal _all_
<tool_ree rroo__respose>>>
Search results are omitted
</tool responses
Step 20
<think>
Let's open the PDF for 2011 but it mentions fiscal 2006. Probably the 201 annual report includes 2006 data. Open link #1. </think>
<tool_call>
I"name": "visit", "arguments": { ("url": htw.aaamwuuerpppttooo//o/sotdttd CepoortArchive///AAAAAO FORMS220000 1.pdf"", "goal": "Find revenue concentration for fiscal 2006"))
</tool_cal <all>
<tool response>
Visit results are omitted
</tool response>
Step 21
<think>
The 2011 annual report doesn't have the data for 2006. Let's find separate 2006 annual
report.
Search "FormFactor 2006 annual report PDF".
</think>
<tool_call>
<tool call>
("name": "search", "arguments": ("query": F""ForrFactor" "2006" "annual report" "pdf""])
</tool call>
<tool_ responsss
Search results are omitted
</tool resoonsess
Step 22
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Published as a conference paper at ICLR 2026
<think>
The 'AnnualReports.com"" page may list multiple years. Let's open it and see if we can view the 2006 report.
</think>
<tool call>
("name": "visit", "arguments": ("url": ("htts://www.amnulrrerr http://ppwttssoooooooooooooooooa
"goal": "Find link FormFactor 2006 anmual report (10-K) or PDF"))
</tool call>
<tvol reeprreessss
Visit results omitted
</tool_ reso_sesspnse>>
Step 23
<think>
Thus the PDF for 2006 may not be easily accessible. But we have revenue concentration numbers for 2004 and 2005 (four customers) and maybe for 2003 (four). The clue mentions both a year with three customers between 27-74% and different year with four customers between 55-89%. In our data, 2005: four customers accounted for 72.8% (within 55-89% Also 2004: four customers accounted for 64.8% (also within 55-89%). So both are customers. But the clue says "In a single fiscal year between January 1, 2004, and December 31, 2006, three customers contributed between 27% and 74% of its revenue." Could there be a year where three customers contributed, maybe 2006? Let's search again for 2006 revenue concentration details. Use the FormFactor 10-K for fiscal year ended December 31, 2006 (maybe filed in March 2007). Let's 's try to open the 10-K filing from 2006. Search "FormFactor 2006 10-K"
</think>
<tool call>
{" I"name": "search", "arguments": ("query": (""FormFattor" "10-K" "2006" "FormFactor"")
</tool_call: _cal_
<tool res__rresonse>>
Search results are omitted
<ttoolresponse>
Step 24
<think>
Let's open the PDF for 2006.
</think>
<tool_call <all>
("name": "visit", "arguments": ("url": ("httss//www.ammulreporrr http://pppsstoooooooss/////////e
RepotAArchive///NAAAA FORM 2006.pdf"), "goal": "Extract revenue concentration data for fiscal 2006"))
</tool eall>
<tool_ response>
Visit results are omitted
</tool_response
Step 25
<think>
Great! This matches the clue: In single fiscal year between 2004-2006, three customers contributed 47.3% of revenue (which lies within 27-74%). In another fiscal year, four customers accounted for 72.8% or 64.8% etc.
Thus this clue is satisfied.
Now we need to verify the class action lawsuit filed by former employee between 2011 1 and 2019, settlement $1.2-$1.9M. Let's search for FormFactor class action settlement.
</think>
<tool call
I"name": "search", "arguments": ("query": [""ForFaatoor" "class action" "settlement"
"million"", ""FormFactor" "lawsuit" "former employee"
25

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Published as a conference paper at ICLR 2026
</tool eall>
<ttool_call>
resporesss
Search results are omitted
Step 26
<think>
Result #1: "Class Action Settlement and filed General The stipulation provides for
payment of $1.5 million." 'That likely is the class action settlement need.
Let's open result #1.
</think>
<tool_call>
("name": "visit", "arguments": ("url":
www.:esttosssooooattooooooomommm "goal": "Find details about class
flle//03345ce-3fel 4cal 1acca--5dd-Odbbal 455dd-0bb263bbccdddd. action settlement amount $1.5 million")
[ttol_ccal>>
<tool_ reoo_rrsssss>>>>
Visit results are omitted
<ttool_respons>>
Step 27
<think>
Maybe the PDF is accessible via a different link. Let's open the 2016 annual report PDF to see if it mentions class action settlement.
</think>
<tool_call
("name": "visit", "arguments": ("url": httpss//www.amuuurrpptttssoo///s
"goal": "Find class action settlement details
in 2016 annual report")
</tool eall>
<tool response>
Visit results are omitted
etool_response'
Step 28
<think>
Great! This matches the class action clue: former employee filed class action in August 2013 (which is within Jan 1, 2011 Dec 31, 2019), court certified class Jan 4, 2016, settlement
$1.5M.
Thus all clues match FormFactor, Inc.
Now can answer: The name of the publicly traded company is FormFactor, Inc. But we should verify that FormFactor was founded between 1988-1995 (it was incorporated in Delaware April 15, 1993, according to SEC filings). Let's confirm. Search for incorporation date. </think>
stool_calls
("name": "search", "arguments": ("query": [""ForFFactor" "incorporated" "April 15,
1993"']}}
</tool_calls _aal>
responssssss>>
Search results are omitted
</tool_response
Step 29
<think>
Thus the company was incorporated in Delaware on April 15, 1993.
Now have all clues matched.
Thus answer: FormFactor, Inc.
We should provide a suceinet answer with supporting references. Use citations from the sources we visited.
26

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Published as a conference paper at ICLR 2026
We'll cite:
Founder change and new CEO effective Q3 2008 (source from press release) lines about
Mario Ruscev becoming CEO and Igor Khandros becoming executive chairman beginning of fiscal third quarter 2008.
Incorporation and founding date (source from prospectus) showing incorporation date April 15, 1993.
Revenue concentration: three customers contributed 47.3% revenue fiscal 2006 (source PDF). And four customers accounted for 72.8% in 2005 and 64.8% in 2004 (source PDF).
We have that information from 2006 PDF.
Class action settlement $1.5 million (source 2016 annual report PDF).
Thus answer.
Now produce final answer.
</think>
<answer>
The answer is very long report with comprehensive analysis, so we provide it in the next pages.
</answer>
27

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Published as a conference paper at ICLR 2026
The publicly traded company described in your question is FormFactor, Inc. Below is comprehensive explanation as to part your description matches FormFactor's history disclosures.
Company Overview and Incorporation
FormFactor, Inc. is semiconductor equipment manufacturer specializing in probe cards used to test integrated circuits during manufacturing, The company was incorporated the state of Delaware on April 15, 1993, which falls within the 1988-1995 window specified. is headquartered Livermore, California, and its shares are traded NASDAQ under the ticker symbol "FORM."
Founding and Leadership Transition
FormFactor was founded 1993 by Igor Khandros. In 2008, there was significant leadership transition. According publicly released statements and board resolutions, Igor Khandros stepped down as chief executive officer and assumed the role Executive Chaimman. This transition became effective at the beginning of FormF actor's fiscal third quarter of 2008. Simultaneously, Mario Ruscev, previously President, was appointed as the new Chief Executive Officer. This leadership shift fulfills criteria regarding the founder changing roles with new CEO taking over, set within timeframe of January 1, 2007 to December 31, 2008.
Revenue Concentration Among Customers
A key element in your query concerns customer velianoe-specifical the proportion of the company's revenue coming from its customers
fiscal years between 2004 and 2006.
Detailed Breakdown of Customer Revenue Shares
The data below summarizes the contributions of the company's s largest customers according its annual reports and filings:
Fiscal Year
Number of Customers
Total Revanue Contribution
Notable Customers
2003
4
66.2
Intel, Spirox, Elpida, Infineon
2004
4
64.8
Intel, Spirox, Elpida, Infineon
2005
4
72.8
Intel, Spirox, Elpida, Samsung
2006
Elpida, Powerchip
In fiscal 2006: Three major customers together accounted for 47.3% of total revenue, which fits your requirement of "three customers contributed between 27% and 74% its revenue."
In other fiscal years (2004 and 2005): Four customers accounted for 64.8% and 72.8% of revenue, respectively.
These figures fall within your stated range of 55%-89%, matching the second customer concentration criterion.
This shows that the company's business relled heavily on small number of large clients during the mid-2000s-
common characteristic among certain B2B technology firms serving niche markets.
Figure 8: The answer of the case
28

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Published as a conference paper at ICLR 2026
Class Action Lawsuit Settlement
Your question references class action lawsuit filed former employee within specific period, with court-certified class status and a settlement amount ranging from 1.2 million.
Timeline and Settlement Details
employee FormFactor flled of California, alleging violations of California wage and hour laws and other claims.
Lawsuit Filing:: August
lawsult Superior Court
Court Certification: January 2016,
certified the plaintiff class this lawsuit.
Settlement Terms: By March 3, 2017, the partles signed stipulation agreeing to settle the suit, requiring
FormFactor pay $1.5 million damages resolve the matter.
The timeline, scope, and settlement figure ($1.5 million) all match your requirements perfectly, confirming that this legal place after 2011
2020, involved substantial enormous settiement typical for employmentt-ellted disputes.
Summary Table: Key Facts Matching Your Clues
Requirement
FormFactor, Evidence
Formed Delaware, founded 1988-1995
Incorporated in Delaware on April 1993
Founder changed roles; new CEO installed at start of Q3 2007-2008
Igor Khandros became Executive Chairman, Mario Ruscev became CEO effective Q3 2008
Revenve dependence: customers, 27%-74% in one year
2006: three major customers contributed 47.3%
Revenue dependence: customers, 55%-89%
2004: 64.8%; 2005: 72.8% (four major customers)
another year
Former employes filed class action, certified class, 1.2-1.9M
Class action filed in 2013, certified in 2016, settled for $1 $1.5M in 2017
Publicly traded, NASDAQ: FORM
Yes, trades on NASDAQ since IPO
Conclusion
All elements in your question align directly with FormFactor, Inc.'s documented corporate and financial history. The company's foundation incorporaioon date, timing leadership transitions, high degree customer comcentration reflected in its public financial reports, and the details of the class action lawsuit-all match the parameters given your query.
Therefore, the answer is FormFactor, Inc.
Figure 9: The answer of the case
29

---

Published as a conference paper at ICLR 2026
Additional Context
FormFactor has grown the mid-2000s leading supplier precision wafer probe cards and metrology
solutions for the global semiconducter industry. Its business model historically involved intense customer concentration due the highly specialized nature of the market, where handful companies purchase high-end testing equipment.
Leadership continuity transitions, especially founder moving executive chaimanship while promoting long-term operating executive to CEO, are notable features highlighted in their govemance documentation. The class action lawsuit, while relatively modest in compared to corporate setllements, demonstrates FormFactor's exposure to labor law risk and its commitment to settling disputes rather than prolonged litigation.
If you require further details specific document excerpts for any aspect above, please me know.
Figure 10: The answer of the case
30


Reviews:
[{"review_id": "d43qchN0tQ", "reviewer": "Reviewer_rnrd", "summary": "The paper presents WebSailor-V2, an open-source web agent trained through a multi-stage post-training pipeline. The approach combines a dataset generated from a knowledge-graph-based process intended to cover diverse forms of uncertainty, supervised fine-tuning on synthetic demonstrations, and reinforcement learning within a dual-environment setup, consisting of a Wikipedia-based simulated environment and a managed real-world environment with tool-execution interfaces. The model is based on Qwen3 and uses 128k-token contexts. The RL stage employs a GRPO-style algorithm with token-level updates, leave-one-out advantages, on-policy sampling, and filtering of negative trajectories. The paper reports that this combination improves performance relative to SFT-only baselines and narrows the gap between open-source and proprietary web agents.", "questions": "- What are the quantitative properties of SailorFog (size, graph statistics, uncertainty-type counts)?\n- How is simulator realism assessed? Are there any metrics comparing simulated and real tool outputs?\n- Which parts of the performance gain come from SFT data versus RL fine-tuning?\n- Were all benchmark runs conducted under identical context lengths and tool budgets?\n- How consistent is the LLM-as-judge evaluation, has human validation been performed?\n- Will the dataset, simulator, and training code be publicly released?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear description of a complete training pipeline.\n- Reported benchmark gains on BrowseComp and HLE relative to earlier open models.\n- RL procedure and stability considerations are explicitly stated.\n- Diagnostic plots provide visibility into training behavior.", "weaknesses": "- The paper provides no quantitative validation that the simulated environment approximates real-world web interactions.\n-  The paper does not report SailorFog statistics such as number of QA pairs, node/edge counts, or category distributions.\n- The effect of individual components (dataset, SFT, RL) and RL design choices is not shown beyond one aggregated comparison.\n- Reported comparisons to proprietary systems use previously published numbers without matching evaluation conditions (context length, sampling, tool limits).\n- Accuracy is measured with an LLM-based judge and the paper does not provide calibration or human-agreement checks.\n- It is not explicitly stated whether code, data, and simulator will be released concurrently with publication.", "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": "3wayNhgLU7", "reviewer": "Reviewer_Usyc", "summary": "The paper introduces Sailor-Fog-QA-V2, a refreshed data generator that builds denser, cyclic knowledge graphs and samples non-isomorphic subgraphs via random-walks, plus a dual-environment RL setup (offline Wikipedia simulator + robust real-web tools stack). With SFT on synthetic trajectories from V2 and a GRPO-style RL phase, a Qwen3-30B-A3B agent delivers SOTA among open-source and competitive vs proprietary systems on BrowseComp-EN/ZH, xbench-DeepSearch, GAIA, HLE, and DeepResearch Bench (e.g., 35.3 on BrowseComp-EN, 44.1 on BrowseComp-ZH, 30.6 on HLE).", "questions": "1. Please list and define the uncertainty categories added beyond obfuscation, with 1–2 concrete generation templates per type. Where in the pipeline are they injected? \n2. Are RL rollouts taken in both simulator and real web? If yes, what mix over training (e.g., % rollouts or steps per phase), and do you adapt the mix based on validation reward or environment-specific success rates? \n3.  You drop some negative trajectories to avoid format collapse. Which categories are excluded beyond “no final answer due to length,” and how sensitive is performance to this filter?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Sailor-Fog-QA-V2 deliberately creates cyclic, densely connected graphs (vs tree-like) and preserves procedural metadata (queries, URLs, stats) that supports richer QA targeting across orbit nodes. Subgraphs are random-walk sampled and WL-checked for non-isomorphism. \n* A Wikipedia-based simulator enables fast, low-cost iteration, whereas the real environment adds a unified tool interface with QPS controls, caching, retry/timeout, and fallback switching, which directly addresses brittleness in web-tool rollouts. \n* The 30B-A3B MoE agent surpasses prior open-source, narrows the gap with leading proprietary systems, and even beats DeepSeek-V3.1 (671B) on HLE, showing that data and stability matter as much as model scale.", "weaknesses": "* The paper states it goes “beyond obfuscation” but never enumerates the additional uncertainty types or how they’re realized during QA generation (only a general description of obfuscation appears). This makes it hard to attribute gains to the proposed “wider variety.” \n* The dual-environment design is described, but how rollouts are scheduled/allocated between simulator and real web (by phase, by task type, by curriculum, or adaptively from reward signals) is not specified. Figure 2 shows the loop but not the selection strategy.  \n* Training uses Sailor-Fog-QA (v1), V2, and IterBench, yet there’s no quantitative ablation showing the marginal benefit of each component on the benchmarks.", "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": "ekol7E4Kp6", "reviewer": "Reviewer_YWs5", "summary": "This paper presents WebSailor-V2, a complete post-training pipeline (SFT + RL) designed to bridge the performance gap between open-source and proprietary web agents.", "questions": "See weakness.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Motivated Data Construction: The paper identifies a clear weakness in existing agent datasets (acyclic, tree-like structures and over-reliance on simple obfuscation). The proposed SailorFog-QA-V2, with its densely interconnected, cyclic graph and \"wider array of uncertainties,\" is a strong and novel contribution that better reflects the complexity of real-world information.\n\n2. The dual-environment RL approach is a very practical and well-executed solution to a major bottleneck in agent training. The high-fidelity simulator (using an offline Wikipedia dump) allows for rapid, low-cost algorithmic iteration, while the robust real-world environment, featuring a unified tool interface with fault tolerance and retry mechanisms, insulates the final training from the latency and failures of live APIs. This is a significant engineering contribution that enables stable RL.\n\n3. The results are impressive and clearly demonstrate the efficacy of the proposed pipeline. The fact that the 30B WebSailor-V2 significantly outperforms all other open-source agents and even much larger models like the 671B DeepSeek-V3.1 validates the hypothesis that superior data and training strategies can overcome raw model scale.", "weaknesses": "Could the authors elaborate on the specific types of \"uncertainties\" (beyond obfuscation) that were introduced in the SailorFog-QA-V2 dataset? Providing a few examples would greatly clarify this contribution.", "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": "Luqfw7t2qF", "reviewer": "Reviewer_uNmb", "summary": "The paper presents WebSailor-V2, a post-training framework for pre-trained LLMs designed to develop agentic capabilities by integrating reasoning and tool use.\nThe pipeline comprises two stages: supervised fine-tuning and reinforcement learning.\nTraining data are derived from a densely constructed knowledge graph, from which a novel QA dataset is sampled.\nFor RL training, the authors develop a simulation environment that enables efficient experimentation by approximating real-world interactions, which are inherently slow and fragile.\nThe final training stage is executed in real-world conditions using a custom tool-calling API.\nEmpirical results show that WebSailor-V2 surpasses both state-of-the-art open-source and most proprietary models on benchmarks assessing agentic reasoning.\nHowever, the paper provides insufficient methodological details, limiting reproducibility and interpretability of the proposed techniques.\nFurthermore, many claims are presented without supporting evaluations, reducing the evidential strength of the conclusions.\nOverall, the work is better characterized as a technical report rather than a scientific paper, as critical details are unclear and unsupported claims abound, making replication highly challenging.", "questions": "* Will you publish your code for the complete pipeline, incl. the building of the knowledge graph, sampling from it, the simulated training environment, the reward model, the tool-calling API, the training loop itself?\n* How do you ensure that the simulated environment closely resembles the real world?\n* Will you make the SailorFog-QA-2 dataset publicly available?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "Next to the actual weights (if made publicly available, not mentioned in the paper), the main contributions of this paper consist of the vaguely described data-set creation and agentic research simulation environment. The paper itself is clearly written and figures are neat and polished. \nIf correct and not biased, the results are very impressive. While this experimental report has nothing to do at a **scientific conference**, the effort of the authors to improve open source SOTA is of great value for any non-scientific user.", "weaknesses": "The Core Issue: The paper presents itself as an engineering or experimental report (i.e., \"we built a system that achieves X performance\") rather than a scientific paper that answers a fundamental research question. It lacks a clearly defined scientific problem (e.g., \"To what degree does data structure, versus data scale, impact an agent's reasoning?\"). This focus on the final artifact, rather than the underlying principles, makes it difficult for the scientific community to extract deep understanding, generalizable intuitions, or understand why this method is better than baselines. To address this main drawback, I advise the authors to:\n* Reframe the Contribution: The paper should be reframed around a central scientific question, not just a final performance score.\n\n* Provide Rigorous Ablation Studies: The paper proposes several new components at once (Agentic CPT, graph-based synthetic data, scalable RL). It is impossible to know which part contributed most to the performance. The paper must include ablations to isolate the impact of each component on the improvement.\n\nAnother core weakness of this paper is the vague descriptions of its key contributions. Overall, the clarity could be improved tremendously by: elaborating more detailed how the knowledge graph is built, how the data is sampled from the graph, how exactly the simulated environment works, how the reward is computed, etc.\n\nAdditionally, there are no attempts made at making the method reproducible.\n\nLastly, throughout the text is a number of claims that were not shown empirically, e.g\n* Page 2, uncertainty in QA: “We also expand the diversity of our QA generation by incorporating a wider variety of uncertainty definitions beyond obfuscation, directly targeting the need for more sophisticated reasoning”\nWhere are those uncertainty definitions?\n* Page 2, considering the simulated environment: “Through meticulous design, it achieves high fidelity, ensuring that the agent’s interaction dynamics, state transitions, and reward mechanisms closely mirror those of a real-world setting”\nNever shown.\n* Page 4, about the graph subsampling: “Ultimately, this strategy enables us to efficiently gather a sufficient quantity of non-isomorphic connected subgraphs that collectively represent the full spectrum of structural complexities, without the prohibitive cost of a brute-force search.”\nNever shown, (and \"non-isomorphic nodes\" is never defined).\n* Page 4, QA generation: “To this end, we introduce a wider array of defined uncertainties, aiming to elicit a more diverse and comprehensive suite of advanced reasoning abilities from the model” \nIt’s never tested if the reasoning abilities are actually more diverse or comprehensive\n* Page 5, Data Curation: “ The quality of the training data directly establishes the upper bound for the model’s ability to generalize to out-of-distribution scenarios through self-exploration.”\nHas this been shown somewhere? \n\nFinally, while the figures are neat and the text clear. I'd advise the two following points to improve the clarity, but also improve the scientific accuracy of the paper:\nClearly include:\nI. A list of contributions at the end of the introduction (often denoted with i., ii., ...) that stands out visually.\nII. A list of scientific research questions at the beginning of the experimental evaluation section (often denoted RQ1, RQ2, ... etc), that are each answered in different paragraphs. For example\n* (RQ1) Can our training pipeline help open source agents outperform existing research agents?\n* (RQ2) Can the graph creations help external viewers to verify the integrity of the provided results?\n* (RQ3) How important is component A ? (conducting an ablation study)\n* ...\n\nThe figures and tables can thus help the readers understanding the addressed problem, the method, and the experimental evaluation during their first pass over the paper. They should thus be structuring the paper like, e.g.:\n1. Figure 1 (describing the problem, if the problem is not obvious),\n2. Figure 2 (describing the method),\n3. Figures and Tables describing each important results (that answers precise scientific questions mentioned above). \n\nFurther, each caption should make its figure a standalone component of the paper. Thus, each figure should be built in the following way:\n\nThe first sentence should highlight the main message of the Figure/Table (e.g. \"Our method outperforms the existing SOTA methods on the studied problem.\")\n\nThe next sentences then explain what is depicted in the Table/Figure. E.g. Mean test accuracy, on 5 seeded trainings, with std. \n\nFinally, details and references to e.g. appendix can be provided if necessary. E.g. Our method outperforms baseline 1 in 3 out of 4 tasks, ... etc.", "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)