OpenNovelty.org

Enhancing Agentic Search via Data Synthesis on Hierarchical Constraint Satisfaction

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
data synthesisagentic searchlarge language models
Abstract:

Deep research becomes increasingly important as people seek to solve complex problems that require gathering and synthesizing information from diverse sources. A key capability in this process is agentic search, where an LLM-agent iteratively retrieves relevant information across multiple sources while performing multi-step reasoning. However, developing effective agentic search systems is challenging due to the lack of high-quality training data that reflects the complexity of real-world research tasks. To address this gap, we introduce InfoSeek, a novel data synthesis framework that conceptualizes agentic search as a Hierarchical Constraint Satisfaction Problem (HCSP), where solving a task requires satisfying layered constraints across multiple levels of sub-problems. InfoSeek employs a Diffusion–Retrospection process: in the diffusion phase, the framework expands outward from a seed webpage, generating constraints that connect to neighboring pages and forming an exploration tree; in the retrospection phase, a subtree is sampled and backtracking constraints are introduced, which are then blurred and integrated into an HCSP instance. As a generic framework, InfoSeek can be easily extended to other domains beyond web, facilitating ad-hoc optimization of deep research. To our knowledge, InfoSeek is the first publicly released framework in this area, complete with open-source code and well-curated datasets. Extensive experiments on diverse information-seeking benchmarks show that training on InfoSeek-generated data substantially improves agentic search performance, delivering significantly larger gains than traditional datasets across diverse model backends and training strategies, thereby validating the effectiveness of our approach.

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 InfoSeek, a data synthesis framework that models agentic search as a Hierarchical Constraint Satisfaction Problem (HCSP). It resides in the 'Deep Research and Multi-Source Synthesis' leaf, which contains only three papers total, including this work. This sparse population suggests the specific intersection of hierarchical constraint satisfaction with multi-source information retrieval is relatively underexplored. The framework's diffusion-retrospection process for generating training data represents a novel approach to addressing the scarcity of high-quality agentic search datasets.

The taxonomy reveals neighboring work in 'Enterprise and Web Search Integration' (two papers) and 'Agentic Memory and Knowledge Management' (two papers), indicating that the broader Agentic Search branch remains relatively sparse with seven total papers. The sibling papers DecoupleSearch and Open Data Synthesis emphasize modular query decomposition and heterogeneous source aggregation respectively, whereas this work focuses on constraint propagation across hierarchical search levels. The taxonomy's scope note explicitly includes 'hierarchical constraint satisfaction and multi-step reasoning,' positioning this paper centrally within its designated leaf while distinguishing it from purely retrieval-focused or single-source approaches.

Among the twenty-seven candidates examined, the formalization of agentic search as HCSP shows one refutable candidate out of ten examined, while the InfoSeek framework itself has one refutable candidate among seven examined. The dataset contribution faces two refutable candidates from ten examined, suggesting more substantial prior work in data synthesis for search tasks. These statistics indicate that while the core conceptual framing (HCSP for agentic search) appears relatively novel within the limited search scope, the practical implementation and dataset contributions encounter more overlap with existing methods. The modest number of refutable candidates across contributions suggests incremental rather than transformative novelty.

Based on the top-27 semantic matches examined, the work appears to occupy a sparsely populated research direction at the intersection of constraint satisfaction and multi-source retrieval. The limited taxonomy population and low refutation rates suggest genuine novelty in framing, though the analysis cannot rule out relevant prior work outside the examined candidate set. The contribution-level statistics reveal uneven novelty across components, with the conceptual HCSP formalization showing stronger differentiation than the dataset and framework implementation aspects.

Taxonomy

Core-task Taxonomy Papers
49
3
Claimed Contributions
27
Contribution Candidate Papers Compared
4
Refutable Paper

Research Landscape Overview

Core task: agentic search via hierarchical constraint satisfaction. The field encompasses diverse approaches to organizing autonomous agents that must navigate complex search spaces while respecting layered constraints. The taxonomy reveals six major branches: Agentic Search and Information Retrieval Systems focus on query-driven exploration and multi-source synthesis, often leveraging hierarchical decomposition to manage large-scale retrieval tasks such as those seen in Hiersearch Enterprise[2] and DecoupleSearch Hierarchical[13]. Hierarchical Reinforcement Learning for Agent Planning addresses sequential decision-making with temporal abstraction, exemplified by works like ArCHer Hierarchical RL[3] and Autonomous Parking RL[1]. Constraint-Based Multi-Agent Coordination tackles distributed problem-solving where multiple agents must satisfy shared or overlapping constraints, drawing on classical techniques such as Meeting Scheduling CSP[26] and extending to modern IoT Multi-Agent CSP[37]. Hierarchical Task Planning and Decomposition emphasizes breaking down high-level goals into executable subtasks, with methods ranging from Autonomous HTN Construction[18] to Web-Browsing Planning[19]. LLM-Based Agentic Frameworks and Architectures explore how large language models can orchestrate hierarchical reasoning and tool use, as in LLM Hierarchical Agents[6] and Nex-N1 Agentic[25]. Finally, Domain-Specific Agentic Applications demonstrate specialized deployments in areas like autonomous driving, robotics, and scientific synthesis. Several active lines of work highlight trade-offs between centralized versus distributed control, symbolic versus learned representations, and domain-general versus task-specific architectures. Hierarchical Constraint Satisfaction[0] sits within the Agentic Search and Information Retrieval Systems branch, specifically under Deep Research and Multi-Source Synthesis, where it shares thematic ground with DecoupleSearch Hierarchical[13] and Open Data Synthesis[15]. While DecoupleSearch Hierarchical[13] emphasizes modular decomposition of search queries, Hierarchical Constraint Satisfaction[0] appears to integrate constraint reasoning more tightly into the hierarchical search process itself, potentially offering finer-grained control over how subgoals are validated and refined. Open Data Synthesis[15] focuses on aggregating heterogeneous sources, whereas Hierarchical Constraint Satisfaction[0] likely prioritizes structured constraint propagation across levels. This positioning suggests an emphasis on principled constraint handling within multi-stage retrieval, bridging classical constraint satisfaction with modern agentic search paradigms.

Claimed Contributions

Formalization of agentic search as Hierarchical Constraint Satisfaction Problems

The authors introduce a formal framework that conceptualizes agentic search tasks as HCSPs, where solving a problem requires satisfying layered constraints across multiple levels of interdependent sub-problems. This formulation extends both flat constraint satisfaction problems and sequential multi-hop reasoning into a hierarchical structure.

10 retrieved papers
Can Refute
InfoSeek data synthesis framework

The authors develop InfoSeek, a novel framework employing a Diffusion-Retrospection process to generate complex QA pairs. The diffusion phase expands from a seed webpage to build an exploration tree, while the retrospection phase samples subtrees and introduces backtracking constraints to create HCSP instances with controllable complexity.

7 retrieved papers
Can Refute
Large-scale Deep Research dataset with open-source code

The authors construct and publicly release a dataset containing over 50,000 question-answer pairs and 16,500 reasoning trajectories, along with the complete open-source framework and code. This represents the first publicly available resource of its kind for training agentic search systems on hierarchically complex tasks.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Formalization of agentic search as Hierarchical Constraint Satisfaction Problems

The authors introduce a formal framework that conceptualizes agentic search tasks as HCSPs, where solving a problem requires satisfying layered constraints across multiple levels of interdependent sub-problems. This formulation extends both flat constraint satisfaction problems and sequential multi-hop reasoning into a hierarchical structure.

Contribution

InfoSeek data synthesis framework

The authors develop InfoSeek, a novel framework employing a Diffusion-Retrospection process to generate complex QA pairs. The diffusion phase expands from a seed webpage to build an exploration tree, while the retrospection phase samples subtrees and introduces backtracking constraints to create HCSP instances with controllable complexity.

Contribution

Large-scale Deep Research dataset with open-source code

The authors construct and publicly release a dataset containing over 50,000 question-answer pairs and 16,500 reasoning trajectories, along with the complete open-source framework and code. This represents the first publicly available resource of its kind for training agentic search systems on hierarchically complex tasks.

Table of Contents