Does urgency, and a chime-plus-glance mode, change how an always-on assistant should deliver a message?

What it found

A stylised expected-utility model adds three urgency classes (deadlines of 120, 20 and 6 s) and a chime-plus-glance mode to the speech, glance and silence choice. Chime-plus-glance is the best mode in 62% of normal and high-urgency contexts and raises mean utility per message from 0.351 to 0.417 (partly by construction, since high-urgency messages carry twice the weight); a Monte Carlo check agrees with the closed form to 0.002. The look and reaction rates drive the twin gap (up to 0.018 utility per message). Every parameter is a round figure of mine: nothing is fitted or measured.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Ambient AI assistant

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• Can a sentence-completeness cue let an always-on assistant stop waiting so long before it replies?Can a sentence-completeness cue let an always-on assistant stop waiting so long before it replies: a stylised two-hangover endpointer cuts the mean wait from 913 to 574 ms at a 10% cut-off budget if no pause sits at a complete point, but only to 837 ms if half do; cue and pauses assumed.
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• How many audited messages does an always-on assistant need before it can trust a noisy urgency label?How many audited messages an assistant needs to estimate a noisy urgency label's confusion matrix and prior well enough to act on it: about 50 at moderate accuracy, with smoothing helping at tiny audit sets (stylised)
• What does a noisy auditor do to an assistant that learns its urgency label’s reliability from audits?What a noisy human auditor does to an assistant that learns its urgency label's reliability from audits: taking audit labels as truth biases the estimate and loses 8% to 69% of the label's gain at a 0.9 to 0.7 auditor, and correcting for a known auditor accuracy recovers it (stylised)

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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.