西湖大学联合阿里达摩院推出干细胞AI模型“归元”。它构建了大规模组合扰动数据集，破解干细胞重编程长期依赖经验试错、成本高成功率低的难题。该研究涉及25种调控因子、近400万种潜在组合，模型采用双模态编码策略，能精准预测不同组合对细胞的影响，为高效重编程提供了新路径。
深度搜索能力是当前大模型领域的核心竞争点，但传统开发模式依赖资源密集的预训练、微调和强化学习，长期被工业巨头垄断。近日，学术界团队推出OpenSeeker-v2，打破常规，通过高质量方法显著降低资源消耗，展示了高效创新的新路径。
卡内基梅隆大学与斯坦福大学联合研究发现，当前AI智能体发展存在“路径依赖”问题。研究分析主流AI基准中的7.2万个任务，发现测评过度集中于编程领域，却忽视了占美国劳动力92%的非编程职业。这种失衡可能导致AI技术发展偏离实际应用需求。
DeepMind核心人物David Silver离职创业，成立Ineffable Intelligence。他主张AI不应仅依赖人类数据训练大模型，而需探索更自主的智能路径。其离职标志AI顶尖人才正转向更具实验性的新方向。
Iflytek
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Input tokens/M
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Openai
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