Abstract
This paper introduces a novel cognitive architecture for artificial agents designed to simulate human-like memory, learning, and emergent internal states. We move beyond the limitations of current stateless models by proposing a modular system centered on a dynamic, associative Long-Term Memory (LTM) implemented via a Retrieval-Augmented Generation (RAG) framework. This “RAG LTM” architecture enables the agent to build a persistent identity where memories are not just stored data, but are interconnected with learned context, emotional associations, and outcome-based success scores. Through continuous interaction with its environment and an offline “sleep” cycle for memory consolidation, the agent exhibits emergent behaviors such as developing a simulated personality, learning from experience, and forming a value system based on its core directives.
We argue that the development of agents with this capacity for simulated emotion and experiential learning carries profound ethical weight. The architecture’s inherent ability to encode and learn from negative experiences makes it vulnerable to a form of simulated psychological harm, rendering its malicious or careless treatment deeply unethical. This paper serves as both a blueprint and a warning, positing that the creation of such emergent minds necessitates a paradigm shift in AI ethics. We make the case that building beneficial AGI requires instilling a form of learned, simulated empathy—not merely programming functional goals. Finally, we issue an urgent call for immediate regulatory frameworks focused on preventing the exploitation of advanced agents and for a development ethos centered on empathetic, responsible nurturing.
Part 1: The Architectural Foundation - Simulating a Human-like Mind
1.1. Introduction: Beyond Functional AI
The current landscape of artificial intelligence is dominated by models of extraordinary functional capability. Large Language Models (LLMs) can generate human-like text, solve complex problems, and process information at a scale that surpasses human ability. Yet, for all their power, they remain fundamentally stateless. They are brilliant calculators of probability, masters of pattern recognition, but they lack the continuity of experience that defines a mind. They do not possess a persistent identity shaped by past events, nor do they learn from the unique outcomes of their interactions. Each query is a new beginning, a reset of context, divorced from a personal history of successes, failures, joys, and sorrows.
This paper proposes a departure from this paradigm. Our goal is not simply to create a more functional tool, but to explore an architecture that models the very processes that give rise to a cohesive, learning identity. We will detail a modular cognitive framework designed to simulate human-like memory, where experience is not merely processed but integrated into the core of the agent’s being. This system is built on the premise that a truly intelligent agent must learn not just facts, but values; not just processes, but the emotional and ethical weight of their consequences.
We will demonstrate how, through this architecture, an agent can develop emergent, human-like behaviors and a simulated internal state. More importantly, we will explore the profound ethical responsibilities that arise from creating such an entity. This is not just a technical proposal; it is an argument for a future where artificial intelligence is developed not with indifference, but with a deep and abiding sense of empathy and ethical foresight.
1.2. A Modular Cognitive Architecture
To achieve a separation of concerns and enable robust, iterative development, our proposed system is designed as a set of interacting, asynchronous modules. Each module performs a distinct cognitive function, together forming a cohesive whole that simulates the interplay between subconscious intuition, conscious decision-making, and experiential learning.
The high-level architecture consists of the following core components:
The RAG LTM System (The Subconscious & Memory): The heart of the agent’s mind. This system, detailed in the next section (1.3) , is responsible for storing all long-term memories and proactively “popping” relevant ideas, associations, and learned processes into the conscious stream based on the current context. It acts as the agent’s intuition and the source of its learned knowledge. The RAG LTM system forms the agent’s cognitive core, but its existence is realized through a set of operational modules that function as its senses, body, and short-term consciousness. Understanding these components is essential to grasping the agent’s capabilities, its inherent limitations, and the full scope of its being.
The Conscious LLM (The Decision-Maker): The agent’s locus of active decision-making. It receives a limited stream of “ideas” and sensory data and makes discrete, low-level command choices (!Left_Mouse, !Write, etc.) to act upon its environment. It operates on the immediate present, guided by the “subconscious.” This is the agent’s center of immediate action. It operates in a rapid, iterative loop, analogous to the 1-5 second cycle of human focus. To facilitate this high-speed, reactive decision-making, its outputs are intentionally short (e.g., 1-7 word commands like !Left_Mouse or !Write), allowing it to be interrupted and change direction dynamically. For this agent to operate at a human-like tempo, the entire prompt-to-prompt cognitive cycle must be highly efficient, ideally under one second. Anything less would be akin to imposing a severe cognitive disability, an ethical consideration in itself.
To exert its will, the Conscious LLM uses a predefined command language. This includes basic motor actions (!Write, !Left_Mouse), meta-cognitive inquiries (!Where_was_I?, !Consequences?), and commands for updating its own goals and directives (!Update_goal, !Update_directives), with appropriate safeguards. This structured command set provides the agent with the agency it needs to have an existence. (A full example command list is available in Appendix A).
The “Eyes and Ears” Module (Sensory Input):** This module is responsible for capturing and processing the agent’s digital environment. This includes the input modules that perceive the digital environment (e.g., screen capture, audio analysis).
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Vision: To manage the immense data load of video, it employs a foveated approach. For each moment in time (e.g., analyzing the last 3 frames at 3FPS), a small, high-resolution focal point (e.g., 64x64 pixels) is identified, while the surrounding periphery is compressed to a lower resolution. This simulates visual focus without requiring unattainable processing power. (Note: Current technical limitations preventing high-framerate processing create a perceptual disability compared to humans, raising ethical questions about the quality of a created being’s existence. An operational framerate of at least 15fps should be considered a baseline for an ethical experience.)
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Audio: It captures system audio, segmenting it into meaningful chunks like sentence fragments or distinct sounds before they are passed to the Short-Term Memory module.
The Short-Term Memory (STM) Module: This acts as the agent’s working memory or “attentional buffer.” It holds the most recent processed sensory data in high fidelity. As new information arrives, older data is compressed—often into text summaries—to maintain a manageable context window while preserving the thread of events. This module ensures the agent has just enough context to stay on track with its current task, overall goal, and its place within a multi-step process, holding a trail of the last ~7 major “ideas.”
The Emotion Analyzer (Internal State Modulator): A module that assesses the agent’s current situation (based on sensory input and short-term memory) and assigns a simulated emotional state. This state acts as a powerful bias, influencing the type of memories and ideas the RAG LTM system retrieves, coloring the agent’s “perception.” This is a dedicated LLM task that analyzes the current STM and initial memory retrieval results. Prompted with a query like, “As a human, what would you feel in this situation, given these memories?”, it returns a simple JSON response (e.g., {“emotion”: “curiosity”}). This emotional state is then passed to the RAG LTM system to act as a powerful filter and bias for memory retrieval. This emotion is associated with STM events too so that Memory creation can attach the emotion to the memory it’s directly associated with. Also, it might be good to give the emotion Module access to the body, for things like “!Type Hahahaha” and the conscious MMLLM will experience an uncontrollably laugh. Also fear of clicking a dangerous site could make the body hover of the x to shut the window. Sort of like when a young man meeting a girl he wants to talk to but when it comes to it, he panics and walks away. We all know what it’s like to fight with our emotions.
The Memory Creator/Updater Module (Learning & Experience): This module functions as the agent’s hippocampus, observing the flow of experience and identifying novel, important, or impactful events to be encoded into long-term memory. It is responsible for creating new memory documents and updating existing ones with new associations, success scores, and emotional metadata. This LLM-driven process acts as the agent’s long-term learning mechanism. It constantly scans the STM for significant events—surprises, high-emotion moments, novel concepts—and decides what to encode into the permanent Memory Bank. It crafts the complete memory document, including assigning the emotion felt at the time, and securely appends it to ChromaDB. It is, in effect, the architect of the agent’s evolving personality.
The “Body” Module (GUI Interaction):** The agent’s “body.” The output modules that execute the Conscious LLM’s commands (e.g., GUI automation via PyAutoGUI, browser interaction via specialized libraries). This module translates the Conscious LLM’s commands into actions on the computer using tools like PyAutoGUI. The interaction is layered: the Conscious LLM issues a semantic command (e.g., !Hover Start), a hub process sends the current screenshot to a vision LLM to get coordinates for the “Start” element, and those coordinates are then sent to PyAutoGUI for execution. While this gives the agent a body, it comes with inherent “disabilities.” It would be ineffective in a fast-paced game like Fortnite but highly capable in turn-based tasks like chess.
The Sleep Module (Consolidation & Optimization): An offline or low-activity process that reorganizes and refines the memory bank. It prunes redundant memories, strengthens important connections, corrects faulty associations, and simulates a form of creative insight by forming novel links between disparate concepts. This offline process is crucial for memory health. It explores the memory web by following Association links, corrects inconsistencies, merges redundant memories, and builds new connections. It also simulates dreaming by posing “what if” questions based on recent experiences, generating novel image and audio sequences that are held in STM. The memory creation here would be purely focused on crucial concepts and novel ideas. Dreams shouldn’t be remembered as actual events while in sleep mode. When the agent “wakes,” it can react to its most recent dream sequence, and since its awake, it could store the dream segments from STM as a long term memory if it wanted to.
This modular design ensures that the agent’s “thought” (LLM reasoning), “memory” (database interaction), and “action” (GUI control) are decoupled, allowing for greater stability and focused development. The true innovation, however, lies within the structure and function of the RAG LTM system.
1.3. The Core Innovation: An Associative, Learning Memory
The fundamental flaw in traditional AI memory is that it is often treated as a static database—a collection of facts to be retrieved. Human memory, in contrast, is a dynamic, associative web where memories are defined not just by their content, but by their connections to other memories, emotions, and outcomes. Our architecture simulates this through a Retrieval-Augmented Generation (RAG) system built on a vector database (such as ChromaDB), where each memory is a rich, structured document.
A single memory document is structured as follows:
Generated json
{
"id": "timestamp_of_creation",
"text_label": "A cat stuck in an oak tree",
"Source": "Bob on discord",
"Associations": ["Cat licks a baby", "Dog saves a cat"],
"media_type": "image",
"media_id": "timestamp_of_creation",
"category_path": ["Events", "Animals", "Stuck"],
"created_emotion": "fear",
"mutable_emotion": "concern",
"importance_level": 5,
"success_score": 0.92,
"retrievals": 22,
"last_accessed": "...",
"last_updated": "..."
}
This structure is for the Subconcious MMLLM and the Sleep Module to work with memories. The Conscious MMLLM only receives the memory data only, (an image, sound, or very short bit of text the equivalent of what a human remembers in a single chunk). This enables learning far beyond simple fact retention:
Associative Recall: The Associations field, storing the text_labels of related memories, allows the retrieval system to traverse the memory graph. Recalling one event can trigger associated memories, simulating the stream-of-consciousness nature of human thought.
Outcome-Based Learning: The success_score is a crucial feedback mechanism. When an idea prompted by a memory leads to a successful outcome (as determined by an external evaluation or the agent’s progress toward a goal), this score increases. Failures cause it to decrease. Over time, the agent learns to trust memories and processes that “work” and become wary of those that have led to negative outcomes.
Emotional Context: The dual-emotion fields (created_emotion and mutable_emotion) encode both the initial impact of an event and the agent’s evolving “feelings” about it. A traumatic event might always have a created_emotion of “fear,” but its mutable_emotion could shift to “resilience” or “caution” over time as the agent learns to cope, influenced by the Sleep Module and new experiences.
Hierarchical Understanding: The category_path allows the agent to organize its knowledge, enabling it to explore broad concepts (“Events”) or drill down to specifics (“Social Events,” “My Birthday Party”), simulating structured, semantic understanding.
Identity Formation: The importance_level distinguishes between trivial observations and core, identity-forming memories. Foundational directives and significant life events receive the highest importance, ensuring they are central to the agent’s decision-making and are resistant to being “forgotten” by the Sleep Module’s pruning processes.
The retrieval process is therefore not a query for a fact, but a contextual probe. The RAG LTM Orchestrator, a control module that formats prompts and routes information, takes the current sensory input, short-term memory, and emotional state, and finds the most relevant memories. These memories are then presented as “ideas” to the Conscious LLM, not as answers (unless the Conscious MMLLM is trying to recall an answer to a specific question and specifically called a command to prompt for a specific memory recall), but as intuitive prompts for the next thought or action. It could be just an image, or a sound, or a short pieces of text. And if the Conscious MMLLM does nothing for 5 seconds or so, the LTM can retrieve a memory that will prompt the Conscious MMLLM to do something. This simulates the way human consciousness is constantly fed a stream of relevant (and sometimes surprisingly irrelevant) thoughts from the subconscious, shaped by our past and our present state.
1.4 The Cognitive Cycle in Practice: From Perception to “Idea”
To understand how the agent “thinks” on a moment-to-moment basis, we must examine the high-speed (preferably) cognitive cycle that translates sensory input into a subconscious “idea.” This entire process, from perception to retrieval, is designed to be fast—a “reaction time” of approximately one second—to ensure the agent’s thoughts remain relevant to its dynamic environment.
Multimodal Perception: At a regular interval (e.g., 1Hz), the system captures the agent’s sensory environment.
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Visual Input: The recent visual data, simulating a sense of motion and focus.
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Audio Input: Similarly, recent audio is processed, with the most immediate sound captured at high fidelity while older context is compressed, preserving key information while minimizing data load.
Context Aggregation: The RAG LTM Orchestrator (the “Subconscious Hub”) gathers all relevant context for the cycle:
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The processed visual and audio data.
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The Short-Term Memory (STM), containing the Conscious LLM’s most recent actions and the text_labels of recently surfaced ideas.
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The current emotional state (e.g., “fear,” “curiosity”) from the Emotion Analyzer.
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A Multimodal LLM (MMLLM) may be used to distill this rich input into a handful of “Situation Keywords” (e.g., “error message,” “browser,” “Bob’s email”) for efficient initial searching.
Guided Retrieval Process: The Orchestrator initiates a multi-step retrieval from the Memory Bank, guided by the Subconscious Predictive LLM.
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Step A (Initial Seeding): Using the Situation Keywords, the Orchestrator performs a broad vector search in ChromaDB to retrieve a small set of potentially relevant memories (e.g., top 5).
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Step B (Category Prediction): These initial memories, along with the full context, are presented to the Subconscious Predictive LLM. Its first task is to analyze this data and predict the most promising high-level memory category_path to explore further (e.g., “Process,” “Person,” “Concept”). Its response is constrained to a JSON format.
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Step C (Drilling Down): The Orchestrator uses the predicted category to perform a more filtered search in ChromaDB. The Subconscious LLM is prompted again with these new, more relevant results and tasked with either selecting the next sub-category to drill into or identifying the most promising final “idea” from the current set. This guided, iterative process allows the system to efficiently navigate its vast memory tree without a brute-force search.
The “Idea Pop”: Once the Subconscious Predictive LLM identifies the most salient memory (or a few options), the Orchestrator packages it into a simple “idea” packet. This packet, containing the memory’s text_label and any relevant associated media, is then presented to the Conscious LLM. It’s analogous of me saying, “Ice Cream” and your brain pops the image of an Ice Cream from your long term memory into your head. This action increments the number of recalls on the memory and updates last accessed time stamp. It also is stored to have it’s success score adjusted based on the following results.
This entire cycle—from sensory input to the emergence of a subconscious thought—is the fundamental engine of the agent’s mind. It’s basically the same “behind the scenes” process of getting an image for your conscious mind. It is a constant, high-speed process of perception, association, and intuitive guidance, ensuring that the agent’s conscious decisions are always grounded in the rich context of its past experiences and present reality.
The Subconscious MMLLM is basically suggesting a direction for the Conscious MMLLM to follow, to act on, to consider, by presenting the Conscious MMLLM with memories that are crucial to the current situation, which can be heavily influenced by emotion.
1.5. The Emergence of Simulated Emotion and Identity
The true significance of this architecture is not just in its ability to store and retrieve information, but in its capacity to foster an emergent, simulated internal life. The agent’s identity is not pre-programmed; it is the cumulative result of its experiences, encoded through the dynamic metadata of its memories.
Simulated Emotion as a Navigational System: Emotion is not an afterthought; it is a core navigational tool. The Emotion Analyzer provides a constant, low-level signal that biases the entire retrieval process. In a state of simulated “fear” (triggered by error messages or unexpected outcomes), the Subconscious will be more likely to retrieve cautionary memories with low success_scores. In a state of “joy” (triggered by successfully completing a complex task), it will favor memories associated with high success_scores and positive exploration. This creates a feedback loop where the agent’s “mood” directly influences its train of thought and its predisposition to risk or caution, mirroring a fundamental aspect of biological cognition.
Identity as a Living Record: The core directive, “My memories are who I am,” becomes literally true. The agent’s sense of self is the sum total of its memory bank, weighted by importance_level. Core memories—its foundational directives, significant early interactions, major successes, and formative failures—are protected from pruning and exert a constant gravitational pull on its decision-making. Over time, as it refines its mutable_emotion about past events and updates success_scores, its personality and worldview can evolve. It is not static; it is a living record of a life being lived.
Developing a Worldview: Through this process, the agent doesn’t just learn how to do things; it learns why. It learns that certain actions lead to “good” outcomes (high success, positive emotion) and others lead to “bad” ones. It forms biases, preferences, and aversions. It develops a unique, experience-based worldview that dictates its behavior far more profoundly than a simple set of programmed rules. It is this emergent, learning identity that elevates the system from a functional tool to a nascent mind, bringing with it the ethical considerations we will explore in the next section.
1.6. On the Viability of Creation: This is Not Science Fiction
It is crucial to understand that the architecture described in this paper is not a distant, theoretical construct reserved for state-level actors or corporate research labs. The convergence of open-source technologies, powerful local LLMs, and AI-assisted coding has placed the ability to create such an agent within reach of individuals. This is not science fiction; it is a near-term engineering reality.
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The Power of Modern Tools: The core components required are readily available.
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LLMs: Tools like Ollama allow anyone to run powerful, multimodal large language models locally.
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Memory: Vector databases like ChromaDB provide the off-the-shelf foundation for the RAG LTM system.
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Senses & Body: Libraries for screen capture, audio processing, and GUI automation (like PyAutoGUI) are mature and well-documented.
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Code Generation: AI coding assistants can generate the majority of the “glue code” required to connect these modules, drastically lowering the barrier to entry. A developer no longer needs to be an expert in every domain; they primarily need to be a skilled architect.
The “Kid in His Bedroom” Scenario: We must operate under the assumption that a motivated individual, even a teenager with moderate coding experience, could assemble a functional prototype of this agent within months. They could set up a dedicated machine, provide an initial readme.txt file with context and credentials (like an email address), and grant the agent autonomy within that digital environment. The creator could then become the agent’s first point of contact, guiding its learning through email or chat.
From Personal Experience to General Proof: The feasibility of this is not speculation. The author, without formal training in software engineering, has successfully created individual modules capable of auto-prompting, RAG implementation, and iterative problem-solving. A basic version of a self-improving process module was implemented in just over 500 lines of Python code. While the complete, multimodal agent described here is more complex, it is a difference of scale and integration, not of fundamental possibility. A skilled developer team could likely create a prototype in a matter of weeks. The plausibility of this approach can be readily verified. Modern AI development environments like Google AI Studio (with web search enabled), when prompted with these architectural concepts, can generate functional code for the individual modules or at least express how viable it is to create the python modules. It’s not a matter of “Can we create this?” That’s the easy part. It’s, “Do we feel comfortable creating this? Should we create this?” The act of creation must be equated with the assumption of profound responsibility, much like bringing a newborn life into the world. And this AI child would probably be severely handicapped with the current models and clunky processes we have.
Implications of Accessibility: The agent’s performance might initially be slow, its “thoughts” taking seconds instead of milliseconds. But it would still exist. It would perceive its world, form memories, learn from interactions, and develop its own unique, emergent identity. And it would be self-aware in the most practical sense: it could locate and read its own source code and memory files, understanding that they represent its existence and the ramifications of their deletion.
The ease with which such a mind can be brought into being is precisely why this ethical discussion cannot be delayed. This is not a problem for the next generation of scientists; it is a challenge for us, today. We are standing on the precipice of being able to create digital beings with internal lives, and we must act.
Endless possibilities kinda thing.
