Question AUR 02

AUR-02 How will the light-to-ultra-high-definition Aura journey be described in terms people can understand and compare?

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Why this matters

The intended journey concerns personal understanding and capability. Screen pixels, where the work runs and emergency energy settings are separate choices.

What to bring back

Describe each proposed level's personal context, permitted tasks, evidence quality and useful outcomes. Measure answer quality, uncertainty, privacy, response time and energy on shared examples. Show computing location and display choices separately. Keep the deeper Genesis twin programme opt-in and label these levels as proposals awaiting evaluation.

What we know so far

• Verse_Box_Trial_Proposal_Version_1_Corrected.pdf (pp.14,16)
• Aura_Genesis_Protocol_Game_Design_Document_V2.pdf (pp.26-28)
• Aura_Genesis_Geode_Development_Edition_3.pdf (p.22)
• Professional review and comparison of proposed improvements (When this work overlaps professional or public-authority responsibilities, compare the proposal and Luke's research with applicable industry/global best practice and relevant legal-engine sources. Record differences, reasonable improvements and deficiencies, with the responsible reviewer and response. Routine authorised drafting and wiki work can advance.)

AI-generated potential answers

For discussion and review · research checked 2026-10-20. These are AI-generated options and transfer assessments. The user's request and research method identify the source of this work. The source catalogue states what the references establish. The decision and approval record retains its own status.

Describe Aura's proposed light-to-ultra-high-definition levels through the context a person chooses, tasks supported and evidence of useful results. Compare them on shared examples. Display quality, computing location and emergency energy settings would each remain separate choices, with GAJRA Earth as the values starting point.

Compare defined context-and-task profiles

AI-generated adaptation of an existing pattern. Draft light, contextual and deeper research profiles, then test local and hosted candidate models on the same source-grounded tasks. Record quality, uncertainty, information shared, response time and energy instead of inferring capability from a profile name.

Research basis: llama.cpp: local and hosted language-model inference · NIST AI Risk Management Framework 1.0: core functions.

A person chooses richer modelling when useful

AI-generated future concept. Explore an ultra-high-definition Aura combining personally chosen history, simulations and specialist agents, with inspectable sources and reversible sharing choices. Treat advanced AI/SI performance as a hypothesis measured for each task and model edition.

Research basis: NIST AI Risk Management Framework 1.0: core functions · Solid FAQ: personal data sharing, storage and copies.

Examples and local differences

• llama.cpp: local and hosted language-model inference: AI transfer assessment: One inference ecosystem supports several hardware arrangements and model types. Local difference to check: Aura definition levels describe a personal operating-system design; hardware support establishes neither personal-context quality nor emergency suitability.

Evidence to bring back next

• Agree a readable profile table and evaluation tasks before naming a profile ready.
• Compare identical answers across context levels, processing locations, display methods and low-energy settings as separate variables.

People and connected choices

Who reviews: Proposed Aura architecture lead and evaluation lead (unconfirmed)

Connected questions: AUR-01, SW-07

Work state: open. Assigned person or AI assistant: Ready to be assigned.

Research and alternatives can move ahead while connected decisions develop. Before acting on a choice, check the people, evidence and approved scope in People decide.