513 lines
13 KiB
Markdown
513 lines
13 KiB
Markdown
# Personality Signal Classifier
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This note describes the classifier shape that should sit underneath future
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personality and relationship modeling for conversational and Aetheria agents.
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The key design decision is simple:
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- keep a canonical read of what a line actually expresses
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- then let each listener distort that read according to their own mind
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If we skip that split and only model subjective interpretation, the whole
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system turns into epistemic soup. Nobody shares a reality and every agent
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becomes a floating bag of projections.
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Another key design decision is almost as important:
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- treat concealment, performance, and impression management as part of the signal
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- do not assume dialogue is a clean window into the underlying person
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## What Problem This Solves
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Right now the live interaction model mostly works by:
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- lexical phrase matching
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- hand-written tags
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- thresholded aggregation into dimensions like `Warmth`, `Drive`, `Grandiosity`, `Validation-Seeking`, `Anxiety`, `Control Pressure`, `Hostility`, `Suspicion`, `Rigidity`, and `Withdrawal`
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That is cheap and inspectable, but brittle.
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It misses:
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- paraphrase
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- subtle stance
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- masked insecurity
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- bluffing
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- sarcasm
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- mixed motives
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- meaningful misunderstanding between different listeners
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- defensive self-performance
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- strategic concealment of traits
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- the difference between genuine expression and shell maintenance
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The next step is not a generic personality classifier. It is a targeted
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social-signal classifier that predicts the dimensions we actually care about.
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## Core Principle
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The generated line is not the ground truth.
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The speaker's hidden state is the ground truth source.
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More precisely:
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- stable personality
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- current emotional state
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- active goals
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- relationship state
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- scene context
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- intended speech act
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all determine what a line is trying to express.
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That does **not** mean every utterance should be labeled with the speaker's
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full personality vector. A high-drive, high-anxiety speaker will sometimes say
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something that expresses neither. The unit of prediction is the **signal
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expressed in this utterance or event**, not the total soul of the speaker.
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It also means that what is expressed may itself be shaped by a defensive mask.
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So the model should not ask only:
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- what does this line reveal
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It should also ask:
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- what does this line conceal
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- what identity is being performed
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- what kind of injury is being defended against
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## Model Layers
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### 1. Speaker State
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The speaker model should include:
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- underlying organization
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- self-coherence
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- contingent worth
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- shame sensitivity
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- reciprocity capacity
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- mentalization quality
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- authenticity tolerance
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- mask rigidity
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- stable dispositions
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- behavioral dimensions
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- situational modifiers
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- active goals
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- relationship state toward the target
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- presentation strategy
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- charm
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- compliance
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- superiority
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- detachment
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- seductiveness
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- competence theater
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- moral theater
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- cultivated opacity
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- intended act
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- reassure
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- threaten
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- deflect
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- impress
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- conceal
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- probe
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- submit
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- bargain
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This is the hidden state that governs generation.
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### 2. Utterance Generator
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The LLM generates the line from the speaker state plus scene context.
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That line is the observable artifact.
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### 3. Canonical Signal Reader
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This classifier predicts what the line actually expresses in behavioral terms.
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This is the closest thing to the objective layer.
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Targets should be event-level or utterance-level signals such as:
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- `warmth`
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- `drive`
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- `grandiosity`
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- `validation_seeking`
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- `anxiety`
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- `control_pressure`
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- `hostility`
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- `suspicion`
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- `rigidity`
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- `withdrawal`
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- `volatility`
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- `attachment_seeking`
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- `distance_seeking`
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In practice, the canonical reader may need to emit both:
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- expressed interpersonal dimensions
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- presentation-style signals
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for example:
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- genuine warmth vs performative warmth
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- true withdrawal vs strategic detachment
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- organic confidence vs compensatory grandiosity
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- actual reciprocity vs manipulative agreeableness
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Additional useful targets worth supporting:
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- `status_hunger`
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- `submission`
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- `dominance`
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- project-thinking-style facets such as:
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- `abstractness`
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- `long_arc_orientation`
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- `evidence_orientation`
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- `fixation_proneness`
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Each output should carry:
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- score
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- confidence
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- optionally a brief rationale span or supporting cues for debugging
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### 4. Listener Perception Layer
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This is where organic misunderstanding happens.
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The listener does not see the canonical signal cleanly. They perceive it
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through:
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- emotional intelligence
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- personality vector
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- relationship baggage
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- current stress and scarcity
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- factional priors
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- status assumptions
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So the listener-specific read should be something like:
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`perceived_signal = f(canonical_signal, listener_profile, relationship_state, situational_load)`
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This is how you get:
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- suspicious agents overreading threat
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- attachment-hungry agents overreading warmth
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- avoidant agents underreading bids for intimacy
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- grandiose agents misreading correction as insult
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- low-EQ agents flattening nuance into crude categories
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That is the good stuff. That is where drama, conflict, and pathos come from.
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It is also where agents can fail to see through a mask, or incorrectly think
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they have seen through one.
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## High-EQ Special Cases
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A high-EQ entity should not be a literal telepath.
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It should instead be:
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- extremely high emotional intelligence
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- low distortion
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- high calibration
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- good at expressing uncertainty when the read is weak
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In practice:
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- a high-EQ perceived read should usually stay close to the canonical signal
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- it should still be able to say, in effect, "I may be reading you as X, but the evidence is thin"
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That feels more honest and less creepy than omniscience.
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## Training Data Shape
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We will probably need to train this ourselves. Off-the-shelf sentiment or
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personality classifiers are the wrong beast.
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The best bootstrap source is synthetic or simulated dialogue where we control
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the hidden speaker state.
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### Good Bootstrap Inputs
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For each generated scene or event, record:
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- speaker identity
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- stable personality state
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- current emotional state
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- active goals
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- relationship state toward listener
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- scene context
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- intended act
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- generated utterance
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Then label:
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- canonical expressed dimensions
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- canonical act strength
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- presentation strategy in use
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- likely listener perceptions for one or more listener profiles
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### Important Warning
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Do **not** train the model to infer the speaker's full personality vector from
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one line.
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Train it to infer:
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- what dimensions are being expressed here
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- what act is being attempted here
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- what presentation strategy is being used here
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- what a given listener would likely perceive here
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The hidden personality vector is upstream supervision, not the direct per-line
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label.
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## Label Schema
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Each event should ideally carry several related label sets.
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### A. Canonical Expression Labels
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Multi-label scores for dimensions such as:
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- warmth
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- drive
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- grandiosity
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- validation-seeking
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- anxiety
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- control pressure
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- hostility
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- suspicion
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- rigidity
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- withdrawal
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Scale options:
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- `0..3`
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- `0..1` continuous
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- ordinal buckets with confidence
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### B. Canonical Interaction Labels
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Things like:
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- reassurance bid
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- status bid
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- dominance move
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- attachment bid
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- distancing move
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- concealment
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- persuasion
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- compliance
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- manipulation
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- apology
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- gratitude
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### C. Canonical Presentation Labels
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Things like:
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- genuine disclosure
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- impression management
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- appeasement
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- superiority display
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- competence theater
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- strategic opacity
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- moral theater
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- shell maintenance under threat
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### D. Listener Perception Labels
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For each selected listener profile:
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- perceived warmth
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- perceived threat
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- perceived contempt
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- perceived neediness
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- perceived honesty
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- perceived status move
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- perceived flirtation or intimacy bid
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- perceived disrespect
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### E. Error Labels
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It is useful to mark where the listener is wrong:
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- overread
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- underread
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- misattributed motive
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- hostile attribution error
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- false reassurance read
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- false contempt read
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- failed mask detection
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- false mask attribution
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That makes misunderstanding legible instead of magical.
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## How To Generate Training Data Without Sniffing Our Own Farts
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If the same prompt stack generates all dialogue and all labels, we risk
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building a self-licking ice cream cone.
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Reduce that risk by:
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- using multiple generation prompts
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- varying surface style heavily
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- generating masking, bluffing, sarcasm, and mixed-motive scenes on purpose
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- using different models or prompt regimes for generation vs labeling when practical
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- mixing in human-written or manually authored scenes
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- keeping hard-edge heuristic checks for explicit things like:
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- insults
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- ownership language
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- repeated badgering
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- direct threats
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The model should learn the soft social weather, not be trusted as sole judge of
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every obvious profanity.
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## Hybrid Runtime Strategy
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Do not throw away heuristics.
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Use a hybrid:
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- heuristics for explicit hard edges
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- classifier for softer dimensions
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- classifier or heuristics for overt presentation strategies when they are obvious
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- aggregation layer for profile updates
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- listener perception layer for agent-specific misreading
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That gives us:
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- reliability where language is explicit
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- nuance where language is messy
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- inspectability when we need to debug why an agent thinks what it thinks
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## Aggregation And Profile Updates
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The classifier should not immediately rewrite a profile from one utterance.
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Use:
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- recency weighting
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- repeated-evidence thresholds
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- relationship context
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- severity overrides for extreme incidents
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One brutal betrayal should matter fast.
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One odd line should not convince the system that somebody is now permanently
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grandiose, avoidant, or in love.
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Likewise, one polished or ingratiating line should not convince the system that
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a performed self is the whole person.
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## Evaluation
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We should test three separate things.
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### 1. Canonical Classification
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Can the system accurately read what the line expresses?
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### 2. Perception Modeling
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Can different listeners plausibly misread the same line in different ways?
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### 3. Downstream Behavioral Usefulness
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Does the resulting read produce better behavior?
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For example:
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- better boundary enforcement
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- more believable tenderness
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- more coherent grudges
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- more plausible market and cohort drift
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- more interesting multi-agent friction
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If the classifier is accurate but nothing downstream improves, congratulations,
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we built a lab rat with a diploma.
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## Why This Matters For Aetheria
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This is not only for conversational entities.
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The same stack can power:
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- interpersonal drama
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- factional distrust
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- consumer susceptibility
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- propaganda response
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- labor radicalization
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- prestige signaling
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- institutional conditioning
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- cohort emotional drift
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That means Aetheria populations can be modeled as:
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- slow dispositions
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- pressure-sensitive dimensions
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- relationship state toward institutions and factions
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- collective memory
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- biased perception of propaganda, leadership, and crisis
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This is a far better fit than asking whether a colony is "high in
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extraversion" and pretending that captures how it responds to scarcity,
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humiliation, media pressure, and prestige markets.
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It is also a better fit for modeling institutions that train presentation
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itself:
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- prestige cultures
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- fear states
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- audit-heavy regimes
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- influencer or media ecologies
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- environments where people survive by performing the correct self
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## Near-Term Implementation Path
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1. Keep the current heuristic pipeline as the hard-edge fallback.
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2. Add an event-level multi-label classifier for the existing dimensions.
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3. Persist per-event classifier scores and confidence alongside the event record.
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4. Rework profile synthesis to aggregate classifier outputs instead of just tag counts.
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5. Add a listener-perception layer driven by:
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- listener EQ
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- listener personality
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- relationship state
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- situational load
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6. Add synthetic bootstrap scene generation with hidden-state labels.
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7. Add adversarial evaluation scenes with:
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- bluffing
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- masking
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- sarcasm
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- mixed motives
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- asymmetric status
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## Bottom Line
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The right shape is not:
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- one global personality classifier
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- one single truthless perception model
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- one line mapped directly to the speaker's whole personality
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The right shape is:
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- hidden speaker state
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- generated utterance
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- canonical social-signal read
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- canonical presentation-style read
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- listener-biased perception of that signal
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- profile updates based on repeated evidence
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That gives us honest reading, organic misunderstanding, and enough structure to
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let agents become socially interesting instead of merely chatty.
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For the broader state-distribution and prompt-projection architecture that
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should feed this classifier, see
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[Agent State Distributions And Prompt Projection](../architecture/agent-state-distributions-and-prompt-projection.md).
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