A Benchmark for Deep Information Synthesis
#DEEPSYNTH #Large Language Models #AI Benchmark #Information Synthesis #ICLR 2026 #AI Evaluation #arXiv
📌 Key Takeaways
- DEEPSYNTH addresses a critical gap in AI evaluation benchmarks for complex information synthesis
- The benchmark includes 120 tasks across 7 domains and 67 countries with rigorous validation
- Current state-of-the-art AI systems perform poorly on DEEPSYNTH, revealing limitations in reasoning
- The research was accepted at ICLR 2026, indicating significant recognition in the AI research community
📖 Full Retelling
Researchers led by Debjit Paul and 16 collaborators introduced DEEPSYNTH, a novel benchmark for evaluating large language model-based agents on February 24, 2026, addressing the critical gap in current evaluation metrics that fail to adequately assess AI systems' ability to solve real-world tasks requiring deep information synthesis from multiple sources. The benchmark consists of 120 tasks collected across 7 domains and data sources covering 67 countries, designed to evaluate agents on realistic, time-consuming problems that combine information gathering, synthesis, and structured reasoning to produce meaningful insights. DEEPSYNTH was constructed using a rigorous multi-stage data collection pipeline requiring annotators to collect official data sources, create hypotheses, perform manual analysis, and design tasks with verifiable answers, ensuring the benchmark's authenticity and complexity. When evaluated on DEEPSYNTH, 11 state-of-the-art LLMs and deep research agents achieved a maximum F1 score of 8.97 and 17.5 on the LLM-judge metric, significantly underperforming and highlighting the substantial difficulty of the benchmark compared to existing evaluation standards.
🏷️ Themes
AI Evaluation, Information Synthesis, Benchmark Development
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Educational technology
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Reinforcement learning
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Machine learning
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Artificial intelligence
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Benchmark
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.21143 [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 Abstract: Large language model -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 DEEPSYNTH, 11 state-of-the-art LLMs and deep research agents achieve a maximum F1 score of 8.97 and 17.5 on the LLM-judge metric, underscoring the difficulty of the benchmark. Our analysis reveals that current agents struggle with hallucinations and reasoning over large information spaces, highlighting DEEPSYNTH as a crucial benchmark for guiding future research. Comments: Accepted at ICLR 2026 Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Information Retrieval (cs.IR); ...
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