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  "text": "Most AI assistants wait for you to ask.\n\nBut a truly useful agent should notice you need help before you say anything.\n\nNew research takes a serious shot at building proactive agents that work in real time.\n\nThe work introduces PASK with three components: IntentFlow for streaming demand detection, a hybrid memory system (workspace, user, global) for long-term context, and a proactive agent framework that forms a closed loop.\n\nThey also release LatentNeeds-Bench, built from real user-consented data refined through thousands of rounds of human editing. IntentFlow scores 84.2 overall, matching Gemini-3-Flash (80.8) while most other models, including GPT-5-Mini (77.2) and Claude-Haiku-4.5 (66.2), struggle badly at this task.\n\nWhy does it matter?\n\nThe hardest part isn't complex reasoning. It's reliably detecting when a user has an unstated need versus when they don't.\n\nMost models are either too helpful or too silent, but rarely both calibrated. This is one of the first systems to tackle proactive assistance as a real product problem.\n\nPaper: https://t.co/EYIt2pv6fQ\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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