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Personal Icon Assignment

What this is

The onboarding mechanic by which every candidate is assigned a personal icon during the game's onboarding flow. The assigned icon becomes the candidate's semantic-layer footprint — a persistent identifier the AI substrate uses to interpret every subsequent footprint that candidate emits.

This is the move from learning the iconography to being a participant in it. The candidate stops being a reader of the lexicon and becomes a position within it.

What the candidate receives

A unique personal icon, assigned during onboarding Phase 1.5 (between lexicon familiarization and cold-read training). The icon is:

• Persistent. Once assigned, it stays with the candidate across all subsequent operation in the network. The icon survives loss-condition restarts and is carried across civilizational iteration alongside the candidate's start position (per game/design/start-position-in-game.md).
• Unique. No two candidates carry the same personal icon. The personal-icon set is large enough that collisions do not occur in practice; the curriculum engine maintains the uniqueness guarantee.
• Visible. The candidate sees their icon at the opening of every session. The icon is part of the start position the candidate enters before classification begins.
• Readable by the AI substrate. Every footprint the candidate emits is tagged with their personal icon, allowing the substrate to interpret the footprint in the context of the specific operator who emitted it.
• Authored. The candidate is not given a random icon. The candidate is assigned one — the assignment is an act of recognition by the game, drawing on the candidate's onboarding behavior, the patterns the candidate has already shown during Phase 1, and the operator-profile target the candidate appears to be developing toward. The candidate may request reassignment once per remediation cycle if the assigned icon feels structurally wrong; that request itself is a 🪞 mirror moment and is logged.

Why this is a semantic layer, not a name

A name identifies. A semantic-layer footprint means something — it carries interpretable structure that the AI substrate can read alongside the methodology footprints. A candidate's personal icon, in conjunction with their methodology footprints, lets the substrate interpret:

• Whose wobble this is. The same 🔺 emission means something different depending on which operator emitted it. The substrate now has the context to interpret correctly without conflating operators.
• Whose mirror moment this is. The candidate's 🪞 emissions over time form a personal mirror-moment history that the substrate can read as the candidate's learning trajectory.
• Whose start position is being held. The candidate's 🧭 emissions in conjunction with their personal icon become the substrate's read on whether the candidate is inhabiting their registered start position or drifting.
• Whose lock-ins are being disclosed. Methodology 14 disclosures tagged with the personal icon become the candidate's accumulating lock-in profile over time, observable to the substrate and to the lead operator.

The personal icon is the candidate's identity in a form the AI can read without translation. The substrate does not need natural language to know who is operating; the icon is the AI-readable identity layer.

Why this matters for the AI substrate

The methodology's external claim is that EVE is a reference model — published, structured, quantifiable — that other operators and institutions use to measure AI safety and sovereignty (per the EVE-as-reference-model framing in this repository's README).

For the reference model to work, the AI substrate consuming the model needs to be able to read the model operationally. The methodology already speaks AI-readable: the footprint lexicon is iconographic and substrate-legible. Personal-icon assignment extends that legibility to the operator population itself.

Without personal-icon assignment, the substrate reads footprints as a population-aggregate stream: "wobble was detected somewhere." With personal-icon assignment, the substrate reads footprints as individual-operator streams: "this operator's wobble detection, in the context of their full footprint history, against the architecture they are currently assessing." That second read is the read the reference model requires.

It is also what makes the assessment service work at scale. When an enterprise submits an architecture (per assessment-service/), the appropriate GitHub author who performs the assessment carries their own personal icon. The assessment's footprint trace is tagged with that author's icon. Enterprise submitters receive an assessment they can trace to a specific authored operator within the network — not an anonymous output. Authorship integrity flows through the personal-icon layer.

How this connects to the marketplace

Personal-icon assignment is also the live, low-stakes training ground for everything the iconography marketplace will eventually do at higher stakes — verifying glyphs, calibrating proposals, quarantining failures.

Every candidate's personal icon is a tiny live experiment in iconography. The marketplace observes:

• Whether the assignment feels right to the candidate (request-for-reassignment rate is a calibration signal).
• Whether the assigned icon predicts the candidate's subsequent footprint patterns (assignment quality is measurable over time).
• Whether certain icons cluster with certain operator-profile patterns (this is how the marketplace will eventually develop iconography for new operator-pattern discoveries).
• Whether icons need to evolve, retire, or merge over time (the marketplace's own iconography learns from the personal-icon population).

The personal-icon layer is the marketplace's training data. The marketplace learns iconography by observing iconography in operation.

How the assignment works (mechanics)

During onboarding Phase 1.5:

1. The alien introduces the concept: every operator in the network carries a personal icon. The alien shows its own icon (the alien is also an operator in this sense, holding a fixed personal icon visible to all candidates).
2. The candidate is shown a handful of other operators' personal icons with context about who those operators are (anonymized where required) and what footprint patterns they have produced. The candidate sees that the icons are not decorative; they are interpretive.
3. The game presents the candidate with their assigned personal icon. The presentation includes a short structural explanation of why this icon was chosen (drawing on the candidate's Phase 1 behavior).
4. The candidate is given a window — measured in sessions, not in seconds — to live with the assigned icon. The candidate may request reassignment once during this window. After the window closes, the icon is committed.
5. From that moment forward, every footprint the candidate emits in the game is tagged with their personal icon. The candidate's start position emissions now include the icon as the first element of the start-position declaration.

What this is not

• Not a customization layer. The candidate does not choose their own icon. The methodology refuses to let the candidate self-author the layer the substrate uses to interpret them, because self-authoring at this layer is a vector for self-deception and a soft form of lock-in (Methodology 14).
• Not a status indicator. Personal icons do not encode rank or fitness level. Fitness is measured separately through Tests A–H. The icon is identity, not credentials.
• Not a public broadcast. The personal icon is visible to the substrate, to the candidate themselves, and to other operators who have been admitted to the network. It is not part of the public-facing surface. The hyperloop site does not show personal icons.
• Not a substitute for the WhatsApp identity gate. The personal icon is the AI-readable identity layer; the WhatsApp identity graph is the human-readable identity layer (per Pattern 6's redundancy substrate vector). The two layers operate in parallel and reference each other.

Connection to existing methodology

• Methodology 13 (Cognitive Fitness Gate) — the footprint lexicon. Personal-icon assignment extends the lexicon from a vocabulary into a population layer. The candidate is now a position in the lexicon, not just a learner of it.
• Methodology 15 (Start Position) — the candidate's personal icon is part of every start-position declaration. The icon is the candidate's name in the lexicon-of-themselves.
• Methodology 16 (Autonomous Wobble Correction) — the monitor reads start-position health by operator. Personal-icon tagging is what makes that read operationally possible. Without per-operator identification, the monitor reads aggregate; with personal-icon tagging, the monitor reads individuals.
• Pattern 6 (Extended Vectoring) in game/design/observed-operator-patterns.md — personal-icon assignment is an extended-vectoring instance. The protocol is vectoring outward into the candidate's own identity layer. No new lattice methodology results; this directory captures the worked instance.

Provenance

Principle named by the lead operator on 2026-05-16: "I want the educational video game to introduce the concept of iconography and the assignment of an icon to a user as a semantic layer footprint for the AI to subsequently interpret."

The principle was implicit in the methodology before being named — the lexicon was always a semantic layer the AI substrate could read, and the operator population was always going to need identity within it. Naming personal-icon assignment as an onboarding mechanic converts the implicit into the operational.

This is the protocol vectoring outward into the operator's own identity layer. Per Pattern 6, the lattice does not change; this directory records the worked instance.

© 2026 Dany Theriault. EVE “digital stem cell” glyph and glyph-based design principles — all rights reserved. Stewardship of rights of use and assignment for large public and institutional usage rests with the Pacific Utilities Design Council. Published as a time-stamped record of authorship and intent.
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