UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, existing benchmarks struggle to evaluate such agents effectively, as they often rely on sandboxed environments and single-turn evaluation paradigms. Moreover, their scenario-based task taxonomies mix multiple model capabilities within the same task category, making it difficult to identify the root causes of agent failures. To address these limitations, we introduce UniC
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- Linked via arxiv authorZhekai Chen →
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
- Linked via arxiv authorChengqi Duan →
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
- Linked via arxiv authorKaiyue Sun →
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
- Linked via arxiv authorBohao Li →
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
- Linked via arxiv authorYuqing Wang →
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
- Linked via arxiv authorManyuan Zhang →
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
- Linked via arxiv authorXihui Liu →
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks