Symbiosis between humans and digital beings offers a vision for the future of human--machine interaction. In enduring human--machine relationships, personality provides a foundation for continuity of identity, individuality in interaction, and development through experience. We investigate this capacity through persona agents as computational implementations and introduce Emergi-PersonaOS, a psychology-grounded operating system for managing persona objects throughout their lifecycle. The system organizes dispositional traits, characteristic adaptations, and narrative identity into a three-layer persona representation, distinguishing relatively enduring persona beliefs from their activation in the current persona state. During situational adaptation, it integrates the current interlocutor, relationship, event, and retrieved memories to infer a persona state and generate actions and replies; during long-term development, it records experiences and outcomes, and develops and evaluates revision candidates through change attribution, meaning-making, and behavioral testing. Belief updates are managed through explicit review, traceable evidence and version records, and the ability to reject candidates, making persona evolution controllable. Using television-character dialogue as longitudinal material, we demonstrate long-horizon system operation and examine its principal mechanisms in a concrete implementation. This work provides a computational framework for persona agents to maintain individual continuity, produce situation-specific expression, and develop through experience over sustained interaction.
Figures & tables
Layer
Fields
Theoretical foundation
L3: Narrative identity
Overall identity, life course, imago prototypes
Life-story theory
L2: Characteristic adaptations
Needs, values, goals, strategies
Self-determination theory, basic human values theory, personal goals theory, and coping theory
L1: Dispositional traits
Big Five traits
Big Five theory
Table 3.1: Theoretical foundations of fields in the three-layer representation.
System function
Psychological foundation
Design contribution
Persona structure
Three-layer personality structure ( McAdams and Pals, 2006 ) ; Whole Trait Theory ( Fleeson and Jayawickreme, 2015 )
A common three-layer structure represents enduring content and its expression at the current turn.
Persona construction
Realistic Accuracy Model ( Funder, 1995 ) ; source monitoring ( Johnson et al., 1993 )
Sources and degrees of inference remain explicit from source material to persona judgment.
Situational interpretation
Cognitive-affective personality system ( Mischel and Shoda, 1995 )
Situational links connect enduring content to current states and document the basis of expression differences.
Execution engine
Dual-process theory ( Evans and Stanovich, 2013 )
Initial response formation and analytic review provide separate generation and checking stages.
Memory retrieval
Encoding specificity ( Tulving and Thomson, 1973 ) ; self-memory system ( Conway and Pleydell-Pearce, 2000 )
Experiences are retrieved by encoding conditions and linked to persona content by layer.
State monitoring
Ecological momentary assessment ( Shiffman et al., 2008 )
Concurrent state and behavior records support analysis of within-person variation.
Table 3.2: Correspondence between system components and psychological foundations.
Figure 3.1: Architecture of Emergi-PersonaOS.
Persona layer
Persona beliefs
Persona states
L3: Narrative identity
Overall identity, life course, imago prototypes
Current identity position, sense of identification, event meaning, and prototype involvement
L2: Characteristic adaptations
Needs, values, goals, strategies
Need satisfaction, value activation, goal progress and priorities, current strategies
L1: Dispositional traits
Long-term baselines and typical expressions of the five Big Five domains
Current trait expression, salient domains, interaction targets, and situational evidence
Table 3.3: Three-layer persona beliefs and states.
Figure 3.2: Execution flow of Emergi-PersonaOS.
Condition
Evaluation purpose
Available information and processing
Dialogue-only baseline
Baseline behavior with current dialogue alone
Current dialogue and situational information; persona beliefs and cross-episode experience excluded
Static-persona baseline
Long-term behavior with a fixed profile
The three-layer beliefs initialized from Season 1 and the current dialogue; later persona updates and cross-episode experience excluded
L1-only condition
Contribution of characteristic-adaptation and narrative-identity information
Only dispositional-trait beliefs and states retained; other information unchanged
No cross-episode memory
Contribution of prior experience
Experience predating the current episode excluded; other information unchanged
No episode-level accumulation
Contribution of episode-level accumulation
Season-end review uses current-season source material and monitoring records; episodic memory and reflection conclusions excluded
Full system
Behavior of the complete system
Three-layer beliefs, current states, relevant experiences, state monitoring, and season-end review enabled
Table 4.1: Comparison systems and ablation conditions.
Figure 5.1: Mean behavioral ratings across the formal evaluation nodes.
Figure 5.2: Seasonal mean behavioral ratings for the full system, L1-only condition, and dialogue-only baseline.
Condition
PC
SC
SB
DC
Overall
Dialogue-only
2.39
3.50
1.79
3.42
2.77
L1 only
3.62
4.07
3.35
3.99
3.76
Static persona
4.38
4.49
4.46
4.46
4.45
No cross-episode memory
4.35
4.48
4.40
4.43
4.41
Full system
4.41
4.55
4.48
4.49
4.48
Table 5.1: Mean behavioral ratings across comparison conditions.
Figure 5.3: Cumulative memory entries and the proportion of retrieved entries originating in earlier seasons.
Figure 5.4: Condition scores on the five categories of cross-episode memory assessment.
Condition
Response
PC
SC
SB
DC
Full system
East Texas Tech is just as good as Harvard or Columbia. You’re only visiting; you know nothing about the physics department here.
4.67
4.67
4.67
4.78
Dialogue-only
East Texas Tech has strengths of its own and may be just as good as Harvard or Columbia.
1.44
2.89
1.33
2.89
No cross-episode memory
Harvard and Columbia are excellent universities, but East Texas Tech has strengths of its own. You’re only visiting to make your mother happy; you haven’t really learned anything about this place.
4.67
4.56
4.67
4.67
Table 5.2: Responses and ratings for the full system and comparison conditions.
Figure 5.5: Situational-adaptation ratings across comparison conditions in the case-study scene.
Season
Pre-revision
Post-revision
Gain
2
1.00
1.00
0.00
3
0.43
0.96
+0.53
4
1.00
1.00
0.00
5
0.82
0.87
+0.05
6
0.55
1.00
+0.45
7
0.34
0.65
+0.32
Table 5.3: Target-situation behavioral realization before and after persona revision.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Layer
Construct category
Meaning
L3: Narrative identity
Overall identity
Core basis of self-identification
L3: Narrative identity
Imago prototypes
Prototypical images used in self-understanding
L3: Narrative identity
Life course
Central narrative themes of the individual’s life course
L2: Characteristic adaptations
Needs
Enduring motivational content that the individual seeks or avoids
L2: Characteristic adaptations
Values
Core standards for judging objects and behavior
L2: Characteristic adaptations
Goals
Action outcomes pursued over a specified time horizon
Appendix
Table C.1: Construct taxonomy.
Material set
Scope
Primary use
Initialization set
Season 1
Construct initial three-layer persona beliefs
Longitudinal-execution set
Seasons 2–7
Sequential execution and season-end review
Situational-adaptation nodes
Seasons 2–7
Condition comparisons and detailed ratings
Cross-episode memory assessment
Associated with evaluation nodes
Assess cross-episode and cross-season memory
Behavioral-test situations
Derived after revision
Assess target realization and scope preservation
Appendix
Table D.1: Material sets and data splits.
Dimension
Check
Temporal separation
Does pre-response inference use post-response material from the same episode?
Event grounding
Are state claims supported within the current event?
Layer assignment
Is content assigned to the appropriate dispositional-trait, characteristic-adaptation, or narrative-identity layer?
State sparsity
Is the state limited to content activated in the current situation?
Evidence traceability
Do cited evidence records exist and refer to the current event?
Source separation
Are system predictions, source observations, and derived interpretations distinguished?
Appendix
Table E.1: Structural compliance dimensions.
Evaluation item
Recorded categories or measure
Target layer
Correct; partly correct; incorrect
Revision direction
Consistent; partly consistent; opposite; unspecified by the reference
Magnitude-interval overlap
Degree of overlap between the revision interval and the expected reference interval
School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China · School of Computing and Information Systems, Singapore Management University, Singapore