OpenNovelty.org

Empowering Efficiency and Efficacy in WebAgent via Enabling Info-Rich Seeking

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
agentinformation seekingdata synthesisllm
Abstract:

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

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces WebLeaper, a framework for synthesizing entity-intensive information-seeking tasks and generating efficient solution trajectories. It positions itself within the Generative and Adaptive Retrieval leaf of the taxonomy, which contains four papers total (including this work). This leaf sits under Retrieval Methods and Optimization, a branch focused on improving retrieval components rather than end-to-end generation pipelines. The sibling papers in this leaf explore adaptive query generation and generative retrieval paradigms, suggesting a moderately active but not overcrowded research direction where adaptive strategies for retrieval are being actively explored.

The taxonomy reveals that WebLeaper's immediate neighbors include Dense and Hybrid Retrieval (four papers on neural embeddings and dual encoders) and Knowledge Graph-Enhanced Retrieval (four papers leveraging structured graphs). The broader Retrieval-Augmented Generation Architectures branch contains general-purpose and domain-specific RAG systems (fourteen papers combined), indicating substantial activity in combining retrieval with generation. WebLeaper diverges from these by emphasizing task synthesis and efficiency metrics rather than architectural improvements to retrieval or generation modules. Its tree-structured reasoning formulation connects conceptually to knowledge graph methods but operates on web-based entity search rather than structured graphs.

Among twenty-seven candidates examined via semantic search and citation expansion, none were found to clearly refute any of the three core contributions. The WebLeaper framework examined ten candidates with zero refutable overlaps; the ISR and ISE metrics examined ten candidates with zero refutations; and the tree-structured reasoning formulation examined seven candidates with zero refutations. This suggests that within the limited search scope, the specific combination of entity-intensive task synthesis, efficiency-focused metrics, and tree-based reasoning appears relatively distinct. However, the analysis does not claim exhaustive coverage of all prior work in adaptive retrieval or task construction.

Based on the top-27 semantic matches and the taxonomy structure, the work appears to occupy a niche intersection of task synthesis and efficiency-driven retrieval that is less densely populated than general RAG architectures. The limited search scope means we cannot rule out relevant prior work outside the examined candidates, particularly in adjacent areas like query generation or multi-agent systems. The novelty assessment reflects what is visible within this constrained literature snapshot rather than a comprehensive field survey.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: information seeking in web-based question answering. The field organizes around several complementary branches that address different facets of retrieving and generating answers from web sources. Retrieval-Augmented Generation Architectures explore how to combine neural retrieval with generative models, often blending dense retrieval with large language models to produce coherent responses. Retrieval Methods and Optimization focuses on improving the underlying search mechanisms—ranging from adaptive retrieval strategies like Adaptive Information Seeking[5] to generative paradigms such as Generation Augmented Retrieval[3] and Generative Conversational Retrieval[1]—that decide what documents to fetch and when. Conversational and Interactive QA examines multi-turn dialogue settings where follow-up questions and context management become central. Cross-Lingual and Low-Resource QA tackles linguistic diversity and data scarcity, while Answer Generation and Verification emphasizes producing and validating factual outputs. User Behavior and Satisfaction in QA investigates how seekers interact with systems and what drives their satisfaction, and Specialized QA Applications and Surveys covers domain-specific challenges in medicine, law, and other verticals. Within Retrieval Methods and Optimization, a particularly active line of work explores generative and adaptive retrieval techniques that move beyond static indexing. InfoRich WebAgent[0] sits in this cluster, emphasizing rich information extraction during web-based question answering and aligning closely with adaptive strategies seen in Adaptive Information Seeking[5] and the generative retrieval paradigm of Generation Augmented Retrieval[3]. While Adaptive Information Seeking[5] focuses on dynamically adjusting retrieval based on query context, InfoRich WebAgent[0] appears to leverage richer web signals to guide its search process. Meanwhile, Multi Agent Seeking[11] explores collaborative agent architectures for complex queries, highlighting a trend toward orchestrating multiple retrieval and reasoning modules. The central tension across these works involves balancing retrieval efficiency with the depth of information captured: some methods prioritize speed and scalability, while others invest in extracting nuanced contextual cues from web pages to improve answer quality.

Claimed Contributions

WebLeaper framework for entity-intensive information-seeking task synthesis

The authors introduce WebLeaper, a framework that constructs information-seeking tasks with substantially more target entities by modeling IS as a tree-structured reasoning problem. The framework includes three task synthesis variants (Basic, Union, and Reverse-Union) that systematically increase both task complexity and the number of target entities within a constrained context.

10 retrieved papers
Information-Seeking Rate (ISR) and Information-Seeking Efficiency (ISE) metrics

The authors propose two metrics, ISR and ISE, to quantify the completeness and efficiency of information collection in agent trajectories. These metrics are used to filter training data, retaining only trajectories that solve tasks both accurately (high ISR) and efficiently (high ISE), ensuring the model learns optimal search behaviors.

10 retrieved papers
Tree-structured reasoning formulation for information-seeking tasks

The authors formalize information-seeking as a tree-structured reasoning task where nodes represent entities and edges represent relations. This formulation allows compact representation of many target entities within limited context length, addressing the entity sparsity problem in prior work and enabling more efficient training of search behaviors.

7 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

WebLeaper framework for entity-intensive information-seeking task synthesis

The authors introduce WebLeaper, a framework that constructs information-seeking tasks with substantially more target entities by modeling IS as a tree-structured reasoning problem. The framework includes three task synthesis variants (Basic, Union, and Reverse-Union) that systematically increase both task complexity and the number of target entities within a constrained context.

Contribution

Information-Seeking Rate (ISR) and Information-Seeking Efficiency (ISE) metrics

The authors propose two metrics, ISR and ISE, to quantify the completeness and efficiency of information collection in agent trajectories. These metrics are used to filter training data, retaining only trajectories that solve tasks both accurately (high ISR) and efficiently (high ISE), ensuring the model learns optimal search behaviors.

Contribution

Tree-structured reasoning formulation for information-seeking tasks

The authors formalize information-seeking as a tree-structured reasoning task where nodes represent entities and edges represent relations. This formulation allows compact representation of many target entities within limited context length, addressing the entity sparsity problem in prior work and enabling more efficient training of search behaviors.

Table of Contents