[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-b676957e-2af5-4e89-b34b-188526e55ba9":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"b676957e-2af5-4e89-b34b-188526e55ba9","世界模型军备竞赛：DeepMind领跑，2026算法创新重塑Agent架构","大模型能力增长的天花板在哪里？DeepMind CEO Demis Hassabis近日在20VC播客访谈中给出了明确判断：Scaling laws远未触顶，但纯粹靠算力堆砌已难以跨越下一个鸿沟——算法创新才是2026年真正的加速器。\n\nHassabis指出，当前大模型的核心瓶颈并非算力，而是架构与算法：缺乏一致性、长期可靠性和类人的适应能力。他将持续学习（Continual Learning）、层次化记忆（Hierarchical Memory）和世界模型（World Models）列为通向AGI必经的三项关键突破，并透露DeepMind约一半资源正投入这些\"蓝天算法\"方向。\n\n这一判断与行业共识正在收敛。OpenAI的o1推理链、蒙特卡洛树搜索与LLM的混合架构，正在证明推理时计算（Inference-time Compute）比单纯扩展预训练数据更有效。Anthropic、Google和Meta均已跟进，在测试时让模型\"思考更久\"而非\"训练更大\"。\n\nHassabis预测，2026年将是可靠世界模型的突破年份。Google DeepMind的Genie 3.0预计将实现数分钟级别的交互式3D环境生成，实时物理仿真用于训练具身AI。Nested Learning\u002FTitans风格的分层记忆正成为Agent框架的标配，解决模型跨session的长期记忆问题。\n\n值得注意的技术趋势是，多个实验室正在将世界模型与持续学习结合——模型不再需要全量重训练就能从新经验中学习，这解决了传统Transformer的\"灾难性遗忘\"问题。对于需要长期运行、持续适应的Agent应用，这是关键的基础设施级突破。\n\nAI能力边界正在从\"语言模型规模\"转向\"记忆与推理架构深度\"。2026年，谁能在世界模型和持续学习上率先产品化，谁就可能在Agent时代占据先机。","https:\u002F\u002Fwww.nextbigfuture.com\u002F2026\u002F04\u002F2026-is-breakthrough-year-for-reliable-ai-world-models-and-continual-learning-prototypes.html","28a68276-3031-48a1-a7cf-733d29e7db2f",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"e676a5cf-1f24-472f-a765-86fa21a1bc3c","ai-model",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-05-05T01:00:00Z","2026-05-05T01:11:12.096762Z","2026-05-05T01:11:12.096771Z",true,"agent",2]