Long-Horizon Agent Tasks
Overview
This field studies agents that must carry goals through many dependent steps in settings such as web browsing, computer use, software engineering, and embodied environments. Common approaches include hierarchical planning and reusable skills, external or task-aware memory, context compression, tool use, verification, and reinforcement learning with milestone-based credit assignment. Benchmarks increasingly test persistent workflows, changing environments, and partial progress, but current agents still often lose task state or compound small errors as interactions grow longer. A central tension is how to extend useful reasoning and memory while controlling inference cost and ensuring that progress and final outcomes are verifiable.
Latest papers
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