机器之心PRO · 会员通讯 Week 34 --- 本周为您解读 ② 个值得细品的 AI & Robotics 业内要事 --- 1. 视频生成 vs 空间表征，世界模型该走哪条路？ 视频预测生成的高质量画面，是否真的意味着模型理解了物理与因果规律？直接在潜在空间建模能否有效避免像素噪声干扰，同时保持决策与规划能力？混合路线是否能成为未来世界模型的 最优路径？随着生成模型和潜在表征技术的发展，AGI 的「思想实验沙盒」能否真正落地应用于物理世界任务？... 2. 抢天才还是拼算力？前 Llama 推理负责人详解 AI 的真实天花板 真正决定 AI 行业天花板的，是天才研究员的灵感，还是指数级增长的算力？如果算力增长放缓，AI 行业会否面临「增长乏力」的拐点？高阶概念想法，如果没有系统实验验证，能否真正推 动模型跃迁？模型泛化的天花板，到底靠升级模型，还是靠设计更高质量的新考题？... 本期完整版通讯含 2 项专题解读 + 30 项本周 AI & Robotics 赛道要事速递，其中技术方面 12 项，国内方面 8 项，国外方面 10 项。 本期通讯总计 20464 字，可免费试读至 9% 消耗 288 微信 ...

Group 1 - The core issue in embodied intelligence is the lack of long-term memory, which limits the ability to process multimodal observational data across time and space [3] - Current visual language models (VLMs) excel in planning and control tasks but struggle with integrating historical experiences in embodied environments [3][5] - Existing video QA benchmarks fail to adequately assess tasks requiring fine-grained reasoning, such as object manipulation and navigation [5] Group 2 - The proposed benchmark includes a task architecture that allows for dynamic environment interaction and memory reasoning validation [4][6] - A total of 60 task categories are designed to cover spatiotemporal semantic memory challenges, including spatial relations, temporal reasoning, attribute memory, and multi-target recall [7] - Key technical innovations include a programmatic expansion of task complexity through increased interaction counts and a strict separation of experience collection from interaction phases [9][6] Group 3 - Experimental results reveal three major bottlenecks in VLM memory capabilities across 60 tasks, including failures in long-sequence reasoning, weak spatial representation, and collapse in multi-target processing [13][14][16] - The performance of native VLMs declines as the number of frames increases, indicating ineffective utilization of long contexts [20] - Supervised fine-tuning models show improved performance by leveraging longer historical data, suggesting a direction for VLM refinement [25] Group 4 - The benchmark represents the first photorealistic embodied memory evaluation framework, covering complex household environments and allowing for scalable assessment [26] - Future directions include memory compression techniques, end-to-end joint training to address the split between high-level reasoning and low-level execution, and the development of long-term video understanding [26]