GPT-6 Astra 信号：上下文窗口不足

𝗚𝗣𝗧-𝟲 𝗔𝘀𝘁𝗿𝗮 𝘀𝗲𝗻𝗱𝘀 𝗮 𝘀𝗶𝗴𝗻𝗮𝗹: 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗰𝗼𝗺𝗽𝗮𝗰𝘁𝗶𝗼𝗻 𝗮𝗹𝗼𝗻𝗲 ...

𝗚𝗣𝗧-𝟲 𝗔𝘀𝘁𝗿𝗮 𝘀𝗲𝗻𝗱𝘀 𝗮 𝘀𝗶𝗴𝗻𝗮𝗹: 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗰𝗼𝗺𝗽𝗮𝗰𝘁𝗶𝗼𝗻 𝗮𝗹𝗼𝗻𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝗳𝗼𝗿 𝗹𝗼𝗻𝗴-𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀. The model has a 1.05M-token context window and scores 96.3% on OpenAI’s MRCR v2 8-needle test at 512K–1M context, compared with 73.8% for GPT-5.6 Sol. One change in Codex caught my attention. When context filled up, the usual approach was compaction: summarize the history and continue. With Astra, notes can persist across context windows, while earlier raw context remains searchable. A requirement, test result, or tool output that didn’t make it into the summary can still be retrieved later. For Agent Memory, this creates a different data problem: 𝗸𝗲𝗲𝗽 𝗮 𝗰𝗼𝗺𝗽𝗮𝗰𝘁 𝘀𝘁𝗮𝘁𝗲, 𝗽𝗿𝗲𝘀𝗲𝗿𝘃𝗲 𝘁𝗵𝗲 𝘂𝗻𝗱𝗲𝗿𝗹𝘆𝗶𝗻𝗴 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗵𝗲 𝘁𝘄𝗼. A few changes in Milvus 3.0 fit this pattern. First, memory is not always a collection of flat chunks. A memory or trajectory has structure: summary, messages, observations, tool calls, and their relationships. StructArray is one example of how Milvus 3.0 can keep those elements under the same entity while making the individual parts searchable. Second, searchable history needs more than vector similarity. An agent may need semantic relevance together with keywords, metadata, time, status, ordering, or aggregation. Milvus 3.0 expanded the retrieval engine across dense and sparse retrieval, filtering, ORDER BY, aggregation, and multi-vector search. Third, memory has a storage lifecycle. A long-running agent can accumulate months or years of history. Most of it will be cold, but it still needs to remain retrievable. Milvus 3.0’s lake-native architecture and External Collections allow large histories to stay in object storage while keeping a retrieval layer over them. So the Agent Memory problem starts to look like three problems together: 𝗵𝗼𝘄 𝗺𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗵𝗼𝘄 𝗶𝘁 𝗶𝘀 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲𝗱, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗶𝘁 𝗶𝘀 𝘀𝘁𝗼𝗿𝗲𝗱 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲. That is the part of Astra I’m watching most closely. 💬 0 🔄 0 ❤️ 0 👀 125 ⚡