US EditionSWE-Bench Performance Reaches 50.8% Without Tool Use: A Case for Monolithic State-in-Context Agents
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SWE-Bench Performance Reaches 50.8% Without Tool Use: A Case for Monolithic State-in-Context Agents
Recent advancements in LM agents have shown promising potential for automating intricate real-world tasks. These agents typically operate by proposing and executing actions through APIs, supporting applications such as software engineering, robotics, and scientific experimentation. As these tasks become more complex, LM agent frameworks have evolved to include multiple agents, multi-step retrieval, and tailored scaffolding to optimize performanc…
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