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Meta paper finds proactive assistants fail mainly on when to interrupt

AIMeta researchers introduce ProPerMem, a benchmark for memory-driven proactive assistants built from egocentric video and year-long personal memories averaging 618K tokens. Every baseline scored at most 15%, while GPT-5.3-Chat reached 96% with oracle evidence but only 38% when choosing its own timing.
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New Meta paper shows that deciding on its own when to interrupt, not just what to say, is where proactive assistants fail.

Proactive assistants, like AI glasses, get no query. They must decide when to speak and what to say by finding the justifying fact in a year of personal memory, not handed to them.

They built ProPerMem: egocentric video paired with a distractor-rich, year-long memory averaging 618K tokens, with human-verified trigger windows. A clip counts only if the assistant speaks just when due inside the window with grounded content.

Every baseline scored at most 15%, even frontier models with retrieval-augmented memory. With oracle evidence in the window GPT-5.3-Chat reached 96%, but choosing its own timing dropped it to 38%, and using retrieval dropped it to 15%.

For proactive assistants, get both the content and the timing right and fire sparingly, which lifted accuracy from below 10% to 42.4%.

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Rohan Paul · x.comPublished · added here

Original title: New Meta paper shows that deciding on its own when to interrupt, not just what to say, is where proactive assistants fail.