Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path before committing budget. We formalize minimum-sufficient execution and the Agent Cognitive Redundancy
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- LinkedLinked via arxiv author · 85%Junjie Yin →
“Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution”
- LinkedLinked via arxiv author · 85%Xinyu Feng →
“Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution”