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Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes, and scale-matched training retains gains as mechanism size and horizon grow, indicating the potential for an evolving training substrate.
E-Commerce Bench is introduced, the first open-source benchmark that integrates multi-round counterpart negotiation and dynamic events into a year-long business operation, and it is found that no single model dominates.
This survey treats isolation as a first-class principle for LLM-agent system safety, and organizes the literature with a boundary-centric taxonomy of five boundaries: user-agent, agent-tool, agent-execution, agent-agent, and system-environment.
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