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[2602.21143] A Benchmark for Deep Information Synthesis
Summary
The paper introduces DEEPSYNTH, a benchmark for evaluating large language models on complex tasks requiring deep information synthesis and reasoning across multiple sources.
Why It Matters
As AI models are increasingly tasked with real-world problem-solving, existing benchmarks fall short in assessing their capabilities. DEEPSYNTH aims to fill this gap by providing a rigorous evaluation framework that highlights the challenges LLMs face, such as hallucinations and reasoning over extensive information, guiding future research directions.
Key Takeaways
- DEEPSYNTH evaluates LLMs on 120 complex tasks across 7 domains.
- Current LLMs struggle with synthesizing information and reasoning.
- The benchmark reveals a maximum F1 score of 8.97, indicating high difficulty.
- DEEPSYNTH is crucial for guiding future AI research and development.
- The benchmark addresses the inadequacies of existing evaluation metrics.
Computer Science > Artificial Intelligence arXiv:2602.21143 (cs) [Submitted on 24 Feb 2026] Title:A Benchmark for Deep Information Synthesis Authors:Debjit Paul, Daniel Murphy, Milan Gritta, Ronald Cardenas, Victor Prokhorov, Lena Sophia Bolliger, Aysim Toker, Roy Miles, Andreea-Maria Oncescu, Jasivan Alex Sivakumar, Philipp Borchert, Ismail Elezi, Meiru Zhang, Ka Yiu Lee, Guchun Zhang, Jun Wang, Gerasimos Lampouras View a PDF of the paper titled A Benchmark for Deep Information Synthesis, by Debjit Paul and 16 other authors View PDF HTML (experimental) 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 67 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 DE...