这篇论文为智能体 AI 的路线图提供了清晰的理论框架，做 AGI 研究或智能体开发的团队值得一读，能帮你理解为什么堆算力不是万能药。
一篇立场论文提出，智能体 AI 系统（而非更大的基础模型）是通往 AGI 最可行的路径。作者将“智能体”的贡献形式化为多个可分离的维度：记忆、推理、工具使用、自我改进和对齐。每个维度都有其独特的瓶颈（如长程连贯性、信用分配、安全审计），而这些瓶颈无法通过增加预训练算力来解决。论文认为，单纯扩大模型规模不足以克服这些挑战，智能体架构才是关键。
Interesting position paper on agentic AI as a foreseeable pathway to AGI. (bookmark it) There has ...
Interesting position paper on agentic AI as a foreseeable pathway to AGI. (bookmark it) There has been strong debate on whether a larger single model get us there or a multi-agent system. The authors argue that agentic AI systems, not bigger foundation models on their own, are the most foreseeable route to AGI. Formalizes what "agentic" actually contributes beyond the base model: memory, reasoning, tool use, self-improvement, alignment. Each is a separable axis with its own bottlenecks (long-horizon coherence, credit assignment, safety auditing). They argues that none of those bottlenecks get solved by another order of magnitude on pretraining compute. Paper: arxiv.org/abs/2605.12966 Learn to build effective AI agents in our academy: academy.dair.ai 💬 23 🔄 38 ❤️ 193 👀 15113 📊 76 ⚡