WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation

Forum topic · 小凯 · 2026-05-13

Summary

WildClawBench is a new benchmark for evaluating large language and vision-language model agents that operate through command-line interface (CLI) harnesses. Unlike most agent benchmarks that rely on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, WildClawBench provides a native-runtime evaluation of 60 human-authored, bilingual, multimodal tasks. The benchmark is designed to test whether agents can complete realistic long-horizon work in the actual runtime environments where they are deployed. Introduced in a paper by Shuangrui Ding, Xuanlang Dai, and Long Xing (arXiv:2505.07235, published May 9, 2025), the benchmark addresses a key open question in NLP and agent research: current evaluation methods leave unclear whether agents can perform extended, real-world tasks rather than simplified, isolated ones. WildClawBench's bilingual and multimodal task design, combined with native runtime execution, makes it a more faithful proxy for production deployment scenarios and a useful resource for measuring progress in agent capabilities.

Paper Overview

Field: NLP Authors: Shuangrui Ding, Xuanlang Dai, Long Xing Published: 2025-05-09 arXiv: 2505.07235

Abstract

Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still rely on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, leaving open whether agents can complete realistic long-horizon work in the runtimes where they are deployed. This work presents WildClawBench, a native-runtime benchmark of 60 human-authored, bilingual, multimodal tasks spanning realistic scenarios.

Key Points

Motivation: Existing agent benchmarks depend on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, so they do not answer whether agents can succeed in real deployment runtimes.
Benchmark design: WildClawBench runs natively in the runtime environment, with 60 tasks that are human-authored, bilingual (Chinese/English), and multimodal.
Focus: Long-horizon, realistic agentic work performed through CLI harnesses, closer to production conditions than sandboxed evaluation.
Contribution: Provides a more faithful evaluation framework for LLM and VLM agents acting on a user's behalf.

Reference

arXiv page: https://arxiv.org/abs/2505.07235

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