综述 | 长程智能体研究全景：基础、演化、框架、优化与前沿

TL;DR - A 149-page survey frames long-horizon agents as systems that sustain goals, state, tool use, recovery, and adaptation across extended task trajectories. It argues that reliable long-horizon behavior requires both runtime engineering and model-level optimization.

• Defines three task levels: within-context interaction, cross-context execution, and open-ended task streams requiring accumulated experience.
• Organizes runtime systems around workflows, memory, tools and protocols, orchestration, middleware, and continuous verification.
• Covers model-side techniques including long-context architectures, trajectory synthesis, fine-tuning, agent reinforcement learning, policy distillation, and self-evolution.
• Identifies effectiveness, efficiency, continual evolution, and trustworthiness as key open challenges.