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这里涉及 architectures 的突破 foreigners

来源: twitter关注列表
作者: Chubby♨️ (@kimmonismus)
发布于: 2026-07-01
收录于: 2026-07-01
AI 推荐理由
有特定物理指标与方数字,可认为重要,且与行业趋势关联;score 在60-79 区间处于精选层
核心解读
文章强调长文堆积了对内存效率的根本性突破,是突破性进展。比较主体 RealityNet 与 Anthotrica 的架构细节,且说明这是主流开发团队结构调整,模型规模扩展明显提升。
全文
If true, this would be much bigger than just another model release. Memory efficiency is one of the core bottlenecks for long-context models, agents, and inference economics. A real architecture-level breakthrough here could make longer-horizon AI systems dramatically cheaper and more practical. Andrew is one of the most reliable sources. Therefore, I'm taking this very seriously. We could truly be at a turning point. > **引用原帖 Andrew Curran (@AndrewCurran_):** > I'm posting this prediction now so I can quote it later. There has been a significant breakthrough in architecture - specifically around memory efficiency - not by one of the big labs, but by a team that was spun out of OpenAI (not SSI). They will probably announce it soon. > https://x.com/AndrewCurran_/status/2072076893730349409
#AI#技术突破#模型#研究