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UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
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Introduces UniClawBench, a capability-driven benchmark of 400 bilingual real-world tasks evaluating proactive agents in live Docker containers with a hidden-supervisor, user-simulator closed-loop multi-turn evaluation, and compares 10 models and 3 agent frameworks.
Problem
Existing agent benchmarks rely on sandboxed or cached environments, single-turn evaluation, and scenario-based taxonomies that conflate model capabilities, making failure root causes hard to diagnose for proactive agents.
Approach
Organize tasks by five foundational capabilities (skill usage, exploration, long-context reasoning, multimodal understanding, cross-platform coordination) and evaluate in live Docker containers with a three-role loop where a hidden supervisor checks checkpoint rubrics and only a coarse status signal reaches a user simulator, which provides sanitized feedback to the executor.
Key results
- Claude Opus-4.8 achieves the highest overall pass rate at 47.5% under OpenClaw, and GPT-5.4 the highest overall average score at 0.774; all models remain below 50% pass rate.
- Under GPT-5.4, OpenClaw achieves 40.7% PR versus 33.8% for EDICT and 29.0% for Nanobot, showing framework choice strongly affects results.
- Automatic pass/fail agrees with human majority vote on 92.0% of 50 sampled trajectories; checkpoint AS correlates with human scores at Pearson r=0.71.
Surprising findings
- Framework choice exerts a stronger influence than model choice, per the authors.
- Most models achieve high checkpoint scores yet fail to complete tasks (a 'halfway failure' phenomenon).
Limitations
- Relatively small set of 400 manually curated tasks.
- Possible instability from live-environment execution.
- Potential bias from LLM-based evaluation (supervisor and user simulator).
Experimental setup
Benchmarks: UniClawBench
Metrics: Pass Rate Average Score (checkpoint-based) token usage
Baselines: ClawBench SkillsBench ClawMark EDICT Nanobot
Builds on: ReAct OpenClaw Nanobot WebArena OSWorld
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- TaskBench: Benchmarking Large Language Models for Task Automation (NeurIPS 2024 · similarity 0.90)
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