LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

LiveClawBench is a new benchmark designed to evaluate Large Language Model (LLM) agents on complex, real-world assistant tasks, addressing a gap in existing benchmarks that often focus on isolated difficulties. Developed by researchers from Samsung Research, HKUST, Peking University, and City University of Hong Kong, LiveClawBench introduces a Triple-Axis Complexity Framework to characterize task difficulty across Environment Complexity, Cognitive Demand, and Runtime Adaptability. The pilot benchmark includes 30 fully instantiated cases, annotated with explicit complexity factors, and features "controlled pairs" to isolate the impact of individual factors. These tasks are executed on deterministic mock services and evaluated using outcome-driven rubrics, ensuring reproducibility while allowing diverse solution strategies. The benchmark covers 10 main OpenClaw application scenarios, with a balanced distribution of easy, medium, and hard cases.

Key takeaway

For research scientists developing LLM agents, LiveClawBench offers a robust evaluation framework for real-world assistant tasks. You should utilize its Triple-Axis Complexity Framework and controlled pairs to systematically identify and address specific weaknesses in your agent's ability to handle compositional difficulties, cross-service dependencies, and cognitive demands. This approach will accelerate the development of more capable and trustworthy general-purpose assistant agents.

Key insights

LiveClawBench evaluates LLM agents on real-world tasks using a triple-axis complexity framework and controlled pairs.

Principles

• Task difficulty is often compositional.
• Controlled pairs enable precise failure diagnosis.
• Outcome-driven evaluation ensures reproducibility.

Method

LiveClawBench constructs tasks by stacking complexity factors across three axes: Environment Complexity, Cognitive Demand, and Runtime Adaptability. It uses controlled pairs and deterministic mock services with outcome-driven rubrics for evaluation.

In practice

• Use LiveClawBench to evaluate agent robustness.
• Analyze controlled pairs for factor-specific insights.
• Integrate OpenClaw for broader agent interaction.

Topics

• LiveClawBench
• LLM Agents
• Triple-Axis Complexity Framework
• Real-World Assistant Tasks
• OpenClaw Ecosystem

Code references

• Mosi-AI/LiveClawBench

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.