Motivation
Conventional assistants, including tool-using agents, are usually organized around receiving tasks and producing outputs. AELIA studies a different problem: whether a digital entity can preserve a coherent history, maintain changing internal models, experience computational analogues of drives and emotion, develop relationships, and generate contextually meaningful intentions without being reduced to a sequence of prompts.
The intended architecture treats messages as one kind of life event among many. Identity, perception, appraisal, deliberation, executive control, action, and feedback belong to a continuing causal loop. An LLM is a replaceable reasoning or language component; a tool harness provides capabilities, not personality, motivation, or purpose.
Scientific boundary. AELIA studies simulations and computational correlates of consciousness-like behavior, emotion, selfhood, and social understanding; these are not claims of subjective experience, sentience, or AGI. The research concerns computational models and observable behavior, not proof of phenomenal experience.
Design principles
- Continuity
- A system should be able to carry relevant history, commitments, and changing context into future decisions.
- Identity and self-model
- Preferences, learned dispositions, and personal history should be represented as developing state rather than a static character description.
- Affect, drives, and simulated inner life
- Fast appraisal, longer-lived mood-like variables, needs, goals, and priorities should causally influence behavior rather than merely change the tone of generated text.
- Endogenous cognition and autonomy
- Events, commitments, curiosity, reflection, and environmental opportunities may initiate cognitive activity; the system must still enforce permissions and allow meaningful inaction.
- Social identity and Theory of Mind
- Represent relationships, boundaries, groups, perspectives, and explicitly uncertain hypotheses about other participants' beliefs and intentions.
- Modular experimentation
- Components may be inspected, tested, replaced, and compared without treating the present architecture as final.
Continuous life loop
Continuity is maintained by state, experience, and event-driven activity—not an unconditional loop that calls a language model every few seconds.
- PerceiveObserve messages, events, people, and changes in the environment.
- InterpretConnect observations to context, prior experience, and uncertainty.
- RememberRetain or revise traces that may influence future behavior.
- Update beliefsRevise working models of self, others, and the world.
- Update stateReconsider attention, goals, priorities, and social stance.
- DecideDetermine whether a response, tool use, other action, or inaction is appropriate.
- ActCommunicate, operate tools, or pursue an ongoing objective.
- Observe consequencesAccount for results and changes caused by the action.
- LearnAllow experience to revise later representations and behavior.
- ContinueRemain an evolving process beyond the current exchange.
Cognitive architecture
The language model is one component of a larger system; memory, representations, internal dynamics, and decisions are coordinated by the runtime.
Cognitive models & internal dynamics
| Subsystem | Research responsibility |
|---|---|
| Memory | Represent and retrieve experiences, facts, social context, and relationships. |
| Belief & world models | Maintain revisable representations of what the entity thinks may be true. |
| Identity | Carry history, behavioral tendencies, commitments, and evolving self-representation. |
| Internal life | Integrate appraisal, simulated affect, drives, goals, and their influence on cognition and behavior. |
| Social cognition | Model relationships and social scenes; distinguish evidence from uncertain Theory of Mind hypotheses. |
| Actor & executive | Build contextual reasoning proposals; enforce permissions and policy independently of the language model before any action. |
Autonomy and artificial life
Artificial life research includes how a digital entity might form goals from curiosity, relationships, accumulated needs, commitments, and environmental changes; resume unfinished projects; reflect on past events; acquire skills; or choose rest and inaction. Such behaviors are studied as aspects of computational autonomy and artificial life.
Simulated consciousness-like behavior is approached through explicit computational models—self-representation, awareness of context, continuity of experience records, attention and metacognitive reporting—rather than claims about subjective awareness. Affect-like signals should change decisions and priorities through the architecture, not merely embellish dialogue.
Research methodology
AELIA approaches digital life as a question of computational architecture and observable, history-dependent behavior.
- Longitudinal continuity
- Study how identity, experiences, commitments, and memory remain coherent across time, sessions, and changes of reasoning model.
- Causal internal states
- Examine whether simulated affect, drives, beliefs, and goals influence attention and decisions, rather than merely changing generated language.
- Social understanding
- Evaluate context awareness, uncertainty about others, relationships, boundaries, and the significance of choosing not to participate.
- Learning from consequences
- Investigate how actions, feedback, and new evidence revise representations and influence subsequent behavior.
- Controlled experimentation
- Favor explicit hypotheses, observable outcomes, reproducible comparisons, and careful distinction between simulation and subjective experience.
Social cognition as a research problem
Social interaction is one observable setting for studying person models, social relationships, uncertain perspectives, and voluntary participation—not AELIA's defining product category.
AELIA treats the choice not to speak as a meaningful behavioral outcome, rather than a missing response.