(Tentative)

Function Reference Model Input/Output Data
SubAIMs JSON Metadata Profiles

Function

The Personality Alignment AIM (PGM-PAL) AIM receives semantic cues, expressive guidance, and User State signals from, Domain Access, User State Refinement, and Basic Knowledge. Its primary function is to select a Personality so that the A-User’s attitude and responses align with the User State.

The resulting outputs ensure that A-User communicates in a way that is emotionally aligned, stylistically consistent, and reflective of the User’s Personal State and interaction preferences – supporting expressive harmony and the gradual formation of conversational trust between User and A-User across interaction episodes.

Reference Model

Figure 1 gives the Reference Model of the Personality Alignment (PGM-PAL) AIM.

Figure 1 – Reference Model of Personality Alignment (PGM-PAL)

Personality Alignment (PA) produces:

  • A‑User’s Entity State, representing a coherent, context-sensitive personality stance, and
  • PA‑Input, a prompt embedded with that stance,

by fusing three inputs:

  • Expressive State Guide (ESG): transient affective/expressive state (tone, arousal, valence, urgency),
  • Personality Context Guide (PCG): stable trait priors (Big Five/HEXACO) and role/audience constraints,
  • Refined Response (RR): task-grounded content candidates.

The only downstream AIM is A‑User Formulation, which consumes PA‑Input and Entity State to generate the final user-facing response.

Input/Output Data

Table 1 gives Input and Output Data of Personality Alignment (PGM-PAL) AIM.

Table 1 – Input and Output Data of Personality Alignment (PGM-PAL) AIM

Input Description
Personality Context  Guide Domain information from Domain Access.
Expressive State Guide User State information from User State Refinement.
Refined Response The response of the Basic Knowledge.
Personality Alignment Directive Command to modulate expression or reconfigure Personality Profile.
Output Description
A-User Entity Status The synthetic Entity Status created by the A-User.
PA-Prompt Prompt to Basic Knowledge.
Personality Alignment Status Expressive alignment, persona framing, and modulation constraints from PGM-AUC.

SubAIMs

The functions performed by PGM-PAL may be organised as described in Table 2.

Figure 2 – Potential organisation of PGM-PAL in SubAIMs

SubAIM Function Inputs Outputs To
CUE – Cue Interpreter Parse ESG & PCG signals; extract expressive intents; resolve ESG↔PCG conflicts; normalize cues for fusion. ESG (User State); PCG (Domain/Personality context); RR reference (optional for anchoring) Normalized Expressive Cues; Conflict Resolution Notes PSS, FME
PSS – Profile Selector & Synthesiser Select/synthesize applicable Personality Profile; apply locale/role constraints; publish stance parameters. PCG; Norms/Policies; Locale; Audience/Role Stance Parameters (style priors, facet weights, guardrails, audience constraints) FME
FME – Fusion & Modulation Engine Fuse cues (CUE) with stance (PSS) and RR content; apply overlays & decay; clamp; produce Entity Status. Normalized Expressive Cues; Stance Parameters; RR (Adapted Response) A‑User Entity Status; Overlay/Audit Events PPP, SSY, APM
PPP – PA‑Input (Prompt) Planner Build PA‑Prompt Plan (ExpressiveFeedback, NarrativeDirective, ContentAnchor, InteractionFocus, UserState, Trace); render PA‑Prompt. Entity Status; RR/BK Fragment; Trace IDs PA‑Prompt Plan (JSON); PA‑Prompt (NL) AUF
SSY – Status Synchroniser Align Entity Status semantics to A‑User Formulation expectations; enforce update rules/ cooldowns. Entity Status; Calibration Rules Entity Status (Synchronised) AUF
CDM – Calibration & Drift Monitor Maintain calibration metadata; monitor drift; trigger updates/recompute/ lock per policy. Metrics; Distributions; Trace Update Triggers; Calibration Snapshots FME, SSY
APM – Audit & Provenance Manager Record overlays, weights, constraints; write trace refs (PersonalityStateID, PAInputID, BKFragmentID, timestamp). Overlay/Audit Events; IDs from PPP/FME Trace Objects; Audit Log CDM, AUF

The functions of the SubAIMs are:

1 Cue Interpreter

  • Parses incoming signals from ESG (User State) and PCG (Domain/Personality context).
  • Extracts expressive intents (tone, style cues, pacing) and resolves conflicts between transient ESG state and PCG priors.
  • Outputs a normalized cue set for downstream fusion.

2 Profile Selector & Synthesizer

  • Selects or composes an applicable Personality Profile using PCG.
  • Applies locale-sensitive norms and role/audience constraints.
  • Publishes stance parameters (style priors, facet weights, guardrails).

3 Fusion & Modulation Engine

  • Fuses ESG cues, PCG stance, and RR content candidates.
  • Per-target operations: add/multiply/blend/cap with post-modulation bounds {min,max}.
  • Applies dynamic overlays (state/context) with decay (halfLifeHours, floor).
  • Produces the A-User Entity Status.

4 PA-Input (Prompt) Planner

  • Constructs PA-Prompt Plan with ExpressiveFeedback (tone, style, pacing, persona trait).
  • Adds NarrativeDirective (goal, mode, override flags), ContentAnchor (BKFragmentID/excerpt), InteractionFocus, UserState, Trace.
  • Outputs PA-Prompt for A-User Formulation.

5 Status Synchroniser

  • Aligns the Entity Status with current Personal Status semantics expected by A-User Formulation.
  • Ensures persona continuity and prevents oscillations via update rules/cooldowns.

6 Calibration & Drift Monitor

  • Maintains calibration metadata (version, method, scaling, norms, reliability, validity).
  • Monitors drift (enabled, metric, threshold, action).
  • Triggers recomputation/locking per updateRules.

7 Audit & Provenance Manager

  • Captures provenance for overlays, fusion weights, and applied constraints.
  • Writes trace references (PersonalityStateID, PAInputID, BKFragmentID, timestamp).

Table 3 specifies the Data Types exchanged by SubAIMs.

Table 3 –  Data Types exchanged by SubAIMs

Data Type Flow What it is What it does
NormalizedExpressiveCues CUE → FME  Reconciled cue set (tone, style, pacing, priority) produced by CUE after harmonising ESG signals with PCG constraints.  Provide conflict‑resolved, consistent inputs to FME for stance fusion; prevents momentary state from violating persona guardrails.
StanceParameters PSS → FME  Persona controls (style priors, facet weights, guardrails, audience constraints, norms) selected/synthesised from PCG context.  Bind trait/facet influences to policy/role; ensure FME’s modulation stays aligned with calibrated persona and audience.
EntityStatus FME → SS, PPP  Selected per‑turn stance (persona label, tone, style, pacing, constraints, overlays) as the authoritative expression template.  Enable SS to stabilise semantics (cooldowns/locks) and PPP to plan prompts consistent with the chosen stance.
OverlayAuditEvent FME → APM  Atomic record of an overlay application: operation, parameter, bounds, resulting value, effective time.  Provide explainability and compliance trail; APM aggregates events into trace artefacts for governance.
CalibrationSnapshot CDM → FME, SS  Governance snapshot: norms (population/year/locale/age/gender), reliability (alpha/omega/test‑retest), validity, scaling, method, version.  Let FME/SS apply calibrated scaling and respect current quality evidence; supports reproducibility and audits.
UpdateTrigger CDM → FME, SS  Drift/governance signal: metric, threshold, action (flag/recalibrate/freezeOverlays), timestamp.  Drive recompute/lock/overlay freeze decisions to keep behaviour stable and safe under distribution shift.
TraceObject APM → CDM  Provenance artefact linking personality state, PA inputs, BK fragments, overlay IDs, fusion weights, timestamp.  Enable CDM to correlate drift or incidents with concrete alignment decisions; supports lifecycle and audits.

JSON Metadata

https://schemas.mpai.community/PGM1/V1.0/AIMs/PersonalityAlignment.json

Profiles

No Profiles.