Academic

LiveAgentBench: Comprehensive Benchmarking of Agentic Systems Across 104 Real-World Challenges

arXiv:2603.02586v1 Announce Type: new Abstract: As large language models grow more capable, general AI agents have become increasingly prevalent in practical applications. However, existing benchmarks face significant limitations, failing to represent real-world user tasks accurately. To address this gap, we present LiveAgentBench, a comprehensive benchmark with 104 scenarios that reflect real user requirements. It is constructed from publicly sourced questions on social media and real-world products. Central to our approach is the Social Perception-Driven Data Generation (SPDG) method, a novel process we developed to ensure each question's real-world relevance, task complexity, and result verifiability. We evaluate various models, frameworks, and commercial products using LiveAgentBench, revealing their practical performance and identifying areas for improvement. This release includes 374 tasks, with 125 for validation and 249 for testing. The SPDG process enables continuous updates

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Hao Li, Huan Wang, Jinjie Gu, Wenjie Wang, Chenyi Zhuang, Sikang Bian · · 1 min read · 2 views
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arXiv:2603.02586v1 Announce Type: new Abstract: As large language models grow more capable, general AI agents have become increasingly prevalent in practical applications. However, existing benchmarks face significant limitations, failing to represent real-world user tasks accurately. To address this gap, we present LiveAgentBench, a comprehensive benchmark with 104 scenarios that reflect real user requirements. It is constructed from publicly sourced questions on social media and real-world products. Central to our approach is the Social Perception-Driven Data Generation (SPDG) method, a novel process we developed to ensure each question's real-world relevance, task complexity, and result verifiability. We evaluate various models, frameworks, and commercial products using LiveAgentBench, revealing their practical performance and identifying areas for improvement. This release includes 374 tasks, with 125 for validation and 249 for testing. The SPDG process enables continuous updates with fresh queries from real-world interactions.

Executive Summary

This article presents LiveAgentBench, a novel benchmark designed to assess the performance of general AI agents in real-world scenarios. Developed by the authors, LiveAgentBench consists of 104 scenarios based on publicly sourced questions from social media and real-world products. The Social Perception-Driven Data Generation (SPDG) method is introduced as a key component of the benchmark, ensuring that each question reflects real-world relevance, task complexity, and result verifiability. The authors evaluate various models, frameworks, and commercial products using LiveAgentBench, highlighting their practical performance and areas for improvement. The benchmark's continuous update mechanism ensures that the content remains fresh and relevant. Overall, LiveAgentBench offers a more comprehensive assessment of AI agents' capabilities, closing the gap between existing benchmarks and real-world user tasks.

Key Points

  • ▸ LiveAgentBench is a novel benchmark for assessing general AI agents in real-world scenarios.
  • ▸ The benchmark consists of 104 scenarios based on publicly sourced questions from social media and real-world products.
  • ▸ The Social Perception-Driven Data Generation (SPDG) method ensures each question's real-world relevance, task complexity, and result verifiability.
  • ▸ The authors evaluate various models, frameworks, and commercial products using LiveAgentBench, highlighting their practical performance and areas for improvement.

Merits

Comprehensive Assessment

LiveAgentBench offers a more comprehensive assessment of AI agents' capabilities, addressing the limitations of existing benchmarks.

Real-World Relevance

The SPDG method ensures that each question reflects real-world relevance, task complexity, and result verifiability, making the benchmark more practical.

Continuous Updates

The benchmark's continuous update mechanism ensures that the content remains fresh and relevant, addressing the evolving nature of AI applications.

Demerits

Limited Generalizability

The benchmark's performance may not generalize to all real-world scenarios, as it is based on a specific set of questions and tasks.

Dependence on Data Quality

The quality of the data used to generate the questions and tasks may impact the benchmark's accuracy and reliability.

Expert Commentary

The introduction of LiveAgentBench represents a significant step forward in the development of benchmarks for assessing AI agents' capabilities. However, as with any novel approach, there are potential limitations and areas for improvement. For instance, the benchmark's performance may not generalize to all real-world scenarios, and its dependence on data quality may impact its accuracy and reliability. Nevertheless, the SPDG method and the continuous update mechanism demonstrate a commitment to addressing the evolving nature of AI applications. As the AI landscape continues to evolve, it is essential to develop more comprehensive and practical benchmarks that reflect real-world user tasks and complexities. LiveAgentBench offers a valuable contribution to this effort, and its impact will likely be felt in various domains, including AI research, development, and policy-making.

Recommendations

  • ✓ Future research should focus on developing more comprehensive and practical benchmarks that address the limitations of existing approaches.
  • ✓ The AI research community should prioritize the development of more accurate and reliable AI models, focusing on explainability, transparency, and fairness in AI decision-making processes.

Sources

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