iAgentBench: Benchmarking Sensemaking Capabilities of Information-Seeking Agents on High-Traffic Topics
arXiv:2603.04656v1 Announce Type: new Abstract: With the emergence of search-enabled generative QA systems, users are increasingly turning to tools that browse, aggregate, and reconcile evidence across multiple sources on their behalf. Yet many widely used QA benchmarks remain answerable by retrieving a single relevant passage, making them poorly suited for measuring cross-source sensemaking, such as integrating evidence, tracking causal links, and resolving dependencies across facets of a topic. We present iAgentBench, a dynamic ODQA benchmark that targets these higher-level information needs while keeping questions natural and grounded in realistic information-seeking behavior. iAgentBench draws seed topics from real-world attention signals and uses common user intent patterns to construct user-like questions whose answers require combining evidence from multiple sources, not just extracting a single snippet. Each instance is released with traceable evidence and auditable intermedia
arXiv:2603.04656v1 Announce Type: new Abstract: With the emergence of search-enabled generative QA systems, users are increasingly turning to tools that browse, aggregate, and reconcile evidence across multiple sources on their behalf. Yet many widely used QA benchmarks remain answerable by retrieving a single relevant passage, making them poorly suited for measuring cross-source sensemaking, such as integrating evidence, tracking causal links, and resolving dependencies across facets of a topic. We present iAgentBench, a dynamic ODQA benchmark that targets these higher-level information needs while keeping questions natural and grounded in realistic information-seeking behavior. iAgentBench draws seed topics from real-world attention signals and uses common user intent patterns to construct user-like questions whose answers require combining evidence from multiple sources, not just extracting a single snippet. Each instance is released with traceable evidence and auditable intermediate artifacts that support contamination checks and enable fine-grained diagnosis of failures in retrieval versus synthesis. Experiments across multiple LLMs show that retrieval improves accuracy, but retrieval alone does not reliably resolve these questions, underscoring the need to evaluate evidence use, not just evidence access.
Executive Summary
This article introduces iAgentBench, a dynamic Open-Domain Question Answering (ODQA) benchmark designed to evaluate the sensemaking capabilities of information-seeking agents on high-traffic topics. Unlike existing QA benchmarks, iAgentBench assesses the ability to integrate evidence from multiple sources, track causal links, and resolve dependencies. The benchmark draws seed topics from real-world attention signals and constructs user-like questions that require combining evidence. Experiments show that retrieval improves accuracy, but retrieval alone is insufficient to resolve complex questions, highlighting the importance of evaluating evidence use. This work contributes to the development of more comprehensive ODQA benchmarks and sheds light on the limitations of existing evaluation methods.
Key Points
- ▸ iAgentBench is a dynamic ODQA benchmark that evaluates cross-source sensemaking capabilities.
- ▸ The benchmark draws seed topics from real-world attention signals and constructs user-like questions.
- ▸ Experiments show that retrieval improves accuracy, but retrieval alone is insufficient to resolve complex questions.
Merits
Comprehensive Assessment of ODQA
iAgentBench provides a more comprehensive evaluation of ODQA systems, going beyond simple passage retrieval to assess integration of evidence, tracking of causal links, and resolution of dependencies.
Real-World Relevance
The benchmark draws seed topics from real-world attention signals, making it more relevant and applicable to real-world information-seeking scenarios.
Demerits
Technical Complexity
iAgentBench requires significant technical expertise to develop and maintain, which may limit its adoption and accessibility.
Scalability Challenges
The benchmark's dynamic nature and requirement for multiple sources of evidence may pose scalability challenges, particularly for large-scale evaluation.
Expert Commentary
iAgentBench is a significant contribution to the field of ODQA, providing a more comprehensive evaluation of information-seeking agents. The benchmark's dynamic nature and reliance on real-world attention signals make it more relevant and applicable to real-world scenarios. However, the technical complexity and scalability challenges associated with iAgentBench may limit its adoption and accessibility. As the field continues to evolve, it is essential to develop more comprehensive evaluation methods that account for the complexities of information retrieval and synthesis. iAgentBench serves as a valuable starting point for this effort, and its implications will be felt across the broader NLP community.
Recommendations
- ✓ Develop and refine iAgentBench to address technical complexity and scalability challenges, ensuring its widespread adoption and accessibility.
- ✓ Extend the benchmark to evaluate additional aspects of ODQA systems, such as contextual understanding and common sense reasoning.