100-Page Survey on Long-Horizon Agents
青稞AI · wechat · 2026-07-18
Taking three months to compile, the 100-page survey "Towards Long-Horizon Agents: A Survey" systematically outlines the development trajectory and key technologies of long-horizon agents.
The paper frames "long-horizon capabilities" as the co-evolution of Harness Engineering and Model Optimization: explicit harness capabilities will gradually be internalized into model policies, and stronger policies will, in turn, drive more powerful harnesses.
The survey explores six main aspects:
- Foundations: Definition and difficulty grading of long-horizon capabilities
- Evolution: From prompt engineering to context engineering, and then to harness engineering
- Harnesses: Loops and workflows, context and memory, tools/MCP and skills, orchestration, hooks, verification
- Optimization: Architecture, data/environment synthesis, pre-training/mid-training, fine-tuning, reinforcement learning, policy distillation, self-evolution
- Applications: Software engineering, information retrieval, Computer Use, multimodality, general agents
- Frontiers: Evolution, effectiveness, efficiency, and trustworthiness
Repository and homepage links are included for further in-depth reading and tracking.
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