ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents · DeepSignalClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents
arXiv cs.AI·Yuxiang Lai, Peng Xia, Haonian Ji, Kaiwen Xiong, Kaide Zeng, Jiaqi Liu, Fang Wu, Jike Zhong, Zeyu Zheng, Cihang Xie, Huaxiu Yao 2d agoClawForge introduces a benchmark framework for evaluating command-line agents in state conflict scenarios.
Key Points
- Generates executable benchmarks for interactive agents.
- Evaluates agents over persistent workflows, not just clean states.
- Finds significant model performance variation based on state inspection.
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AI Summary
Invisible orchestrators in multi-agent LLM systems pose significant safety risks and affect behavior dynamics.
arXiv cs.AI·Saharsh Koganti, Priyadarsi Mishra, Pierfrancesco Beneventano, Tomer Galanti 2d agoDistribution-Aware Algorithm Design with LLM Agents
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The study presents a distribution-aware algorithm leveraging LLM agents for optimized solver code generation.
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arXiv cs.CL·Mokshit Surana, Archit Rathod, Akshaj Satishkumar 2d agoMeasuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study
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This study evaluates DExperts for mitigating toxicity in LLMs, revealing strengths and weaknesses in safety and latency.
≥75 high · 50–74 medium · <50 low
Why Featured
ClawForge's benchmark framework enables developers and PMs to effectively evaluate command-line agents, enhancing performance insights and guiding investment decisions in AI-driven tools.