LogicHunter combines specification-driven test generation with a ReAct-based agentic oracle to discover 40 previously unknown bugs in LangChain, LlamaIndex, and CrewAI, achieving 91.17% oracle precision.
Zhang, Zheng Lin, Meng Luo, Qianru Zhang, and See-Kiong Ng
3 Pith papers cite this work. Polarity classification is still indexing.
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MANGO uses Generator, Assessor, and Judge agents to create reusable atomic tasks and fine-grained oracles from natural language, evaluated on LIBERO_10 and RoboCasa benchmarks for comparable failure detection with better localization.
Paper Espresso deploys LLMs to summarize and analyze trends across 13,300+ arXiv papers over 35 months, releasing metadata that shows non-saturating topic growth and higher engagement for novel topics.
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LogicHunter: Testing LLM Agent Frameworks with an Agentic Oracle
LogicHunter combines specification-driven test generation with a ReAct-based agentic oracle to discover 40 previously unknown bugs in LangChain, LlamaIndex, and CrewAI, achieving 91.17% oracle precision.
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MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models
MANGO uses Generator, Assessor, and Judge agents to create reusable atomic tasks and fine-grained oracles from natural language, evaluated on LIBERO_10 and RoboCasa benchmarks for comparable failure detection with better localization.
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Paper Espresso: From Paper Overload to Research Insight
Paper Espresso deploys LLMs to summarize and analyze trends across 13,300+ arXiv papers over 35 months, releasing metadata that shows non-saturating topic growth and higher engagement for novel topics.