OpenNovelty.org

A Benchmark for Deep Information Synthesis

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
BenchmarkDeep Information SynthesisLLM agentsDeep ResearchAI agents
Abstract:

Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis. However, current evaluation benchmarks do not adequately assess their ability to solve real-world tasks that require synthesizing information from multiple sources and inferring insights beyond simple fact retrieval. To address this, we introduce DEEPSYNTH , a novel benchmark designed to evaluate agents on realistic, time-consuming problems that combine information gathering, synthesis, and structured reasoning to produce insights. DEEPSYNTH contains 120 tasks collected across 7 domains and data sources covering 42 countries. DEEPSYNTH is constructed using a multi-stage data collection pipeline that requires annotators to collect official data sources, create hypotheses, perform manual analysis and design tasks with verifiable answers. When evaluated on DEEPSYNTH, 9 state-of-the-art LLMs and deep research agents achieve a maximum F1 score of 8.97. Our analysis reveals that current agents struggle with hallucinations and reasoning over large information spaces, highlighting \ourdata as a crucial benchmark for guiding future research.

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 DEEPSYNTH, a benchmark for evaluating agents on realistic tasks requiring multi-source information gathering, synthesis, and structured reasoning. It resides in the 'Deep Research and Long-Horizon Information Seeking' leaf, which contains only three papers total including this work. This is a relatively sparse research direction within the broader taxonomy of 50 papers across 36 topics, suggesting the specific focus on evaluating deep synthesis capabilities is an emerging rather than crowded area.

The taxonomy reveals that DEEPSYNTH sits within the 'Multi-Step and Multi-Hop Reasoning Approaches' branch, neighboring leaves focused on chain-of-thought reasoning, knowledge graph traversal, and tool-augmented agents. While sibling papers like Deep Research Agents and Agentic Deep Research emphasize system architectures for sustained exploration, DEEPSYNTH diverges by providing an evaluation framework rather than a new agent design. The benchmark also connects to the 'Benchmarking and Evaluation Frameworks' branch, though that branch primarily contains domain-specific or multi-hop QA benchmarks rather than deep synthesis evaluation.

Among 30 candidates examined, the core DEEPSYNTH benchmark contribution shows no clear refutation across 10 candidates reviewed, suggesting novelty in its specific evaluation design for synthesis tasks. However, the multi-stage data collection pipeline with expert annotation faces stronger prior work, with 3 of 10 candidates providing overlapping methodologies. The analysis revealing agent limitations shows no refutation among 10 candidates, though this may reflect the limited search scope rather than definitive novelty. The statistics indicate moderate prior work density for methodology but sparser coverage for the benchmark itself.

Based on the top-30 semantic matches examined, DEEPSYNTH appears to occupy a relatively novel position as an evaluation framework for deep information synthesis, though its data collection methodology builds on established practices. The analysis covers a focused slice of the literature and does not claim exhaustive coverage of all relevant benchmarking or agent evaluation work.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: evaluating agents on multi-source information synthesis and reasoning. The field encompasses a diverse set of approaches organized around how agents retrieve, integrate, and reason over heterogeneous information sources. At the top level, the taxonomy distinguishes between architectural concerns—such as Multi-Source Retrieval and Integration Architectures that handle diverse data streams (e.g., Multimodal RAG Benchmark[2], HydraRAG[15])—and reasoning paradigms like Multi-Step and Multi-Hop Reasoning Approaches, which emphasize iterative query refinement and long-horizon exploration (e.g., Deep Research Agents[4], Meta-Reasoning Chains[5]). Other branches address Multi-Agent Collaboration and Coordination (e.g., Multi-Agent Discussions[13], Routing Multi-Agent Specialists[32]), Benchmarking and Evaluation Frameworks that provide standardized testbeds (e.g., Paperarena[33], LingBench++[19]), Specialized Application Domains spanning manufacturing to clinical simulations (e.g., Manufacturing Knowledge Integration[1], Clinical Interaction Simulations[34]), Information Fusion and Uncertainty Reasoning for handling conflicting or incomplete evidence, and Knowledge Exploration and Dialogue Generation for interactive information seeking (e.g., Multi-Source Dialogue Generation[8], Proactive Information Seeking[38]). Within the Multi-Step and Multi-Hop Reasoning branch, a particularly active line of work focuses on deep research and long-horizon information seeking, where agents must iteratively gather, synthesize, and refine insights over extended search sessions. Deep Information Synthesis[0] sits squarely in this cluster alongside Deep Research Agents[4] and Agentic Deep Research[26], all emphasizing sustained exploration and the integration of findings from multiple retrieval rounds. Compared to shorter-horizon methods like Meta-Reasoning Chains[5], which may prioritize rapid inference over a few hops, these deep-research approaches tackle open-ended queries requiring comprehensive coverage and nuanced synthesis. Trade-offs emerge between the depth of exploration and computational cost, as well as between fully autonomous planning and human-in-the-loop guidance. Deep Information Synthesis[0] contributes to this landscape by addressing evaluation challenges specific to multi-source synthesis, complementing the architectural innovations of neighbors like Deep Research Agents[4] and the agentic orchestration strategies explored in Agentic Deep Research[26].

Claimed Contributions

DEEPSYNTH benchmark for evaluating deep information synthesis

The authors introduce DEEPSYNTH, a benchmark containing 120 tasks across 7 domains and 42 countries that evaluates agents on their ability to synthesize information from multiple sources and perform structured reasoning to generate insights, rather than simple fact retrieval.

10 retrieved papers
Multi-stage data collection pipeline with expert annotation

The authors develop a four-step data collection methodology involving expert annotators who identify data sources, formulate hypotheses, validate them through analysis, and create tasks with verifiable answers and reasoning chains.

10 retrieved papers
Can Refute
Analysis revealing limitations of current agents

The authors conduct an in-depth analysis showing that state-of-the-art agents achieve only 8.97 F1 score maximum, frequently commit navigation and synthesis errors, and perform poorly on under-represented sources, demonstrating key limitations in current agent capabilities.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DEEPSYNTH benchmark for evaluating deep information synthesis

The authors introduce DEEPSYNTH, a benchmark containing 120 tasks across 7 domains and 42 countries that evaluates agents on their ability to synthesize information from multiple sources and perform structured reasoning to generate insights, rather than simple fact retrieval.

Contribution

Multi-stage data collection pipeline with expert annotation

The authors develop a four-step data collection methodology involving expert annotators who identify data sources, formulate hypotheses, validate them through analysis, and create tasks with verifiable answers and reasoning chains.

Contribution

Analysis revealing limitations of current agents

The authors conduct an in-depth analysis showing that state-of-the-art agents achieve only 8.97 F1 score maximum, frequently commit navigation and synthesis errors, and perform poorly on under-represented sources, demonstrating key limitations in current agent capabilities.

Table of Contents

A Benchmark for Deep Information Synthesis | Novelty Validation