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  "text": "Google just gave language models real long-term memory.\n\nA new architecture learns during inference and keeps context across millions of tokens.\n\nIt holds ~70 percent accuracy at 10 million tokens.\n\n𝗧𝗵𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝘀 𝘄𝗵𝗶𝗹𝗲 𝗶𝘁 𝗿𝘂𝗻𝘀\nTitans adds a neural long-term memory that updates during generation.\nNot weights. Not retraining. Live learning.\n\n• A small neural network stores long-range context\n• It updates only when something unexpected appears\n• Routine tokens get ignored to stay fast\n\nThis lets the model remember facts from far earlier text without scanning everything again.\n\n𝗜𝘁 𝗸𝗲𝗲𝗽𝘀 𝘀𝗽𝗲𝗲𝗱 𝘄𝗵𝗶𝗹𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝘅𝘁\nAttention stays local. Memory handles the past.\n\n• Linear inference cost\n• No quadratic attention blowups\n• Stable accuracy past two million tokens\n\n𝗜𝘁 𝗮𝗹𝗹𝗼𝘄𝘀 𝘆𝗼𝘂 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗻𝗲𝘄 𝗸𝗶𝗻𝗱𝘀 𝗼𝗳 𝗮𝗽𝗽𝘀\nYou can process full books, logs, or genomes in one pass.\nYou can keep state across long sessions.\nYou can stop chunking context just to survive limits.",
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