Technical Framework for Building an AGI

Architecture-Level Design

The AGI system is organized as a set of interconnected modules defined by formal information flows and vector operations, rather than metaphor or anthropomorphic labels. At a high level, the architecture maintains a core cognitive engine (for processing and reasoning), a memory hierarchy (with multiple storage timescales), symbolic field representations (for structured knowledge), contradiction-resolution loops, and recursive self-referential update mechanisms. All components operate on continuous vector representations and explicit mathematical objectives, ensuring that learning and adaptation emerge from raw informational mechanics (e.g. gradient-based updates in vector space) instead of ad-hoc heuristics.

Core Cognitive Engine

The core cognitive engine is a differentiable computational graph (such as a Transformer-based neural network) that transforms inputs and internal states into intermediate representations and proposed outputs. It serves as the “brain” of the AGI, handling perception processing, pattern recognition, and preliminary reasoning. Formally, one can model the core engine as a function $f_\theta: \mathbb{R}^n \to \mathbb{R}^m$ parameterized by weights $\theta$, which processes an input vector (from sensors or queries) and the current state vector to produce an output (e.g. an action distribution or a textual response). The engine’s operation is defined purely in terms of matrix and vector computations (e.g. multi-head attention, linear projections, activation functions), all of which are amenable to gradient calculus for learning. Information flows through layers of the network encoding features and relationships in a high-dimensional continuous space (embedding space). The cognitive state (e.g. the hidden activations or a recurrent state) functions as a working scratchpad where intermediate results of reasoning can be represented as vectors. By using a Transformer architecture, the engine can implement complex sequence processing and even internal code execution (e.g. through learned attention patterns) without any anthropomorphic assumptions – it is essentially a vector-to-vector transducer guided by an objective function.

To enable advanced reasoning, the core engine can be extended with or interfaced with symbolic manipulation capabilities in vector form. For example, using Vector Symbolic Architecture principles, symbols are represented as vectors, and operations like binding or merging of concepts are done via algebraic operations on those vectors. This allows the engine to represent structured knowledge (relations, sequences, logical combinations) in a continuous manner. Within the engine, multiple specialized sub-modules may exist for different cognitive functions (vision processing, language understanding, logical inference), but all share a common mathematical language of linear algebra and calculus. Crucially, the engine does not rely on any pre-defined “common sense” – it must learn internal representations that capture general world knowledge through training and experience.

Memory Hierarchy

AGI requires a hierarchy of memory systems for effective general intelligence. We design multi-tier memory: a fast sensory buffer (very short-term memory), a working memory (short-term active context), and a durable long-term memory store. These correspond to different timescales and implementations, but all are based on information-theoretic storage of vectors.

• Sensory Memory: Immediately after the sensory input is processed by the core engine, low-level features are stored in a short-lived buffer. This might be implemented as the hidden activations of the first layers of the core network or a short sliding window of recent observations. It allows the system to integrate over time (e.g. for temporal perception like sequence of frames or words) without permanently committing everything to long-term storage.

• Working Memory: This is an explicit scratch space for the engine to hold salient information while performing a task (akin to RAM or registers in a computer). Working memory could be implemented as part of the transformer’s context (for example, the self-attention mechanism in a Transformer naturally provides a form of working memory by allowing later tokens to attend to earlier token representations). Additionally, one can augment the system with an external working memory module – for instance, a “blackboard” or global workspace – where the core engine can read/write intermediate results or hypotheses. In vector terms, working memory might be a set of vectors { $w_1, w_2, \dots, w_k$ } that the cognitive engine can attend to and modify. These vectors could encode current goals, partial solutions, or recently retrieved facts needed for the current reasoning episode.

• Long-Term Memory: For permanent knowledge retention, the AGI uses long-term memory comprised of different content types: semantic memory (factual and conceptual knowledge), episodic memory (records of experiences with time/context tags), and potentially procedural memory (learned skills or action sequences). Implementation-wise, long-term memory can be a combination of distributed storage in the model’s weights and external memory systems. The model’s weights (e.g. in a Transformer) implicitly store a vast amount of learned statistical knowledge (from pre-training on large data); this is analogous to an innate knowledge base. However, to allow dynamic learning and one-shot storage of new information, an external memory is integrated – for example, a vector database or keyed memory bank that the agent can query with a vector and retrieve relevant entries. This external memory holds high-dimensional representations of knowledge that are not (yet) internalized in the core engine’s weights. Specific facts, concepts, or even entire knowledge graphs can be encoded as embedding vectors in this database. The agent uses a similarity search (e.g. $\text{argmax}_j \langle q, k_j \rangle$ where $q$ is a query vector and $k_j$ are keys in memory) to fetch relevant information. This mechanism allows quick retrieval of memorized knowledge and fine-grained manipulation at the feature level. The memory entries might be stored as (key, value) pairs, where keys and values are vectors; keys could represent concepts or situations, and values could be content (like an explanation or a sensory snapshot). The memory hierarchy is designed such that working memory can pull information from long-term memory into the current context (e.g. the agent retrieves a fact from the vector database into working memory if it seems relevant to the current problem), and conversely, new significant information from an experience can be encoded and saved into long-term storage (e.g. after a learning episode, the distilled lesson is stored as a new vector in semantic memory).

Functional block diagram of an AGI’s memory and cognitive subsystems. Sensory inputs pass through sensory memory into a cognitive processing module (neural network). The system updates a working memory and builds learned concept vectors, which feed into symbol grounding and causal learning modules. Long-term memory is divided into semantic (facts, grounded symbols, causal relations, goals), procedural (skills, control routines), and episodic (contextual experiences) stores, enabling both prior knowledge and new learned knowledge to support perception, reasoning, and action. The entire agent (green dashed box) constitutes the embodiment with sensory and actuator interfaces.

In the above schematic, information flows from perception to cognitive processing to memory, and back out to decision-making. The memory hierarchy ensures the agent accumulates knowledge over time and recalls it when needed for generalization. For example, semantic memory might contain a vector encoding the concept “fire is hot” or a mathematical fact, which can be retrieved whenever a situation demands it. Episodic memory might store an autobiographical event (e.g. a sequence of state-action vectors from a training episode in a simulator, along with outcome reward), which can later be replayed or analyzed by the cognitive engine to improve future decisions. Procedural memory would be used for learned action sequences or policies (for instance, a skill like a robot grasping an object could be stored as a sequence of controller parameter vectors).

Symbolic Field Representations

To perform higher-level reasoning and ensure interpretability of knowledge, the AGI uses symbolic field representations – a fusion of symbolic AI’s discrete clarity with the rich associative power of vector embeddings. In practice, this means that abstract concepts, entities, and relations are represented as vectors in a continuous space, but arranged such that they preserve symbol-like structure. We maintain a semantic vector space where each significant symbol (e.g. an object, attribute, or concept) corresponds to a high-dimensional vector. In this space, similarity (dot products or distances) reflects semantic relatedness, and simple algebraic operations can encode relationships. For example, the relation “Paris is the capital of France” might be represented by vectors $v(\text{Paris})$, $v(\text{France})$, and a relation vector $v(\text{capital_of})$ such that $v(\text{Paris}) + v(\text{capital_of}) \approx v(\text{France})$ (this is analogous to word embedding relations). These vectors form a field in the mathematical sense – they can be combined, added, subtracted, etc., to yield new meaningful vectors.

The symbol grounding problem is addressed by mapping low-level sensory data to these high-level symbol vectors. When the agent perceives an object or hears a word, the core cognitive engine outputs an embedding that should line up with the appropriate symbolic vector in the field. Embodiment plays a critical role here: because the agent interacts with the world, it can tie raw perceptions to consistent vectors (symbols) representing the entities of those perceptions. For instance, the concept of “red apple” might be grounded by the agent seeing many examples – the visual sensory data gets encoded into a vector close to $v(\text{apple})$ and $v(\text{red})$ in the symbolic space.

Within the symbolic field representation, we also include structured knowledge representations like knowledge graphs or logical formulas. These can be encoded via tensor representations or binding mechanisms in the vector space. A relation between two concepts can be a transformation (matrix or tensor) that, when applied to one concept vector, yields another. For example, if $R$ is a matrix representing the relation “is the capital of”, $R \cdot v(\text{Paris}) \approx v(\text{France})$. This way, relational knowledge is captured in the same vector calculus framework. The use of vector representations for symbols allows the system to generalize and interpolate between known symbols (which purely discrete symbols would not allow), but it also means we need clear procedures to maintain consistency and discreteness when needed.

One challenge is ensuring that these continuous representations can still yield discrete, clear-cut comparisons when detecting logical relations or contradictions. A purely neural embedding might blur distinctions; hence the system may employ a hybrid approach: the core engine can generate or refine symbolic representations (like producing a logical assertion) which is then checked by a separate module. For example, it might output that two vectors are close enough to be considered “the same entity” or that a predicate holds true. The architecture could include a neural-Symbolic interface that translates vector states into symbolic assertions (triples or logical statements) which can be stored in a structured knowledge base (e.g. an internal knowledge graph). This knowledge base acts as an interpretable layer of memory where each entry is a proposition the system believes (with some confidence). The symbolic field thus spans both the continuous vector space and these extracted discrete facts, allowing the agent to leverage logical consistency checking algorithms on the latter.

Contradiction Resolution Loops

A cornerstone of robust general intelligence is the ability to identify and resolve contradictions in one’s knowledge and inferences. The framework incorporates a contradiction detection and resolution loop that continually monitors the agent’s internal statements (e.g. conclusions, answers, or beliefs retrieved from memory) for consistency. In implementation terms, this could be a module that takes pairs of propositions (represented in a logical or vector form) and evaluates a contradiction score. At the simplest level, if the agent has two assertions $A$ and $¬A$ (logical negation) in its knowledge, that’s a contradiction. However, in a nuanced, continuous representation, the agent might have two vectors that are supposed to represent the same concept but are significantly different (indicating conflicting properties).

One strategy is to project certain key assertions into a discrete propositional space for rigorous checking. For instance, the agent might maintain a truth-value database of key facts (especially those it has high confidence in). The contradiction checker can run algorithms similar to those in SAT solvers or truth-maintenance systems: if a newly inferred fact logically conflicts with something in the database, flag it. Because our system primarily operates with vectors, the contradiction module might use an approximate matching to detect contradictions: e.g. if two concept vectors are nearly negatives of each other in some subspace (indicating opposite attributes) and the concepts are meant to refer to the same entity, that’s a likely contradiction.

This loop works as follows: (1) The core engine (or an associated reasoning module) generates an output or intermediate conclusion. (2) That conclusion is formulated as a representation that can be compared against memory (both vector similarity and symbolic checks). (3) The contradiction module pulls relevant known assertions (from long-term semantic memory or from recent context) and compares for direct opposites or logical exclusion. (4) If a contradiction is found, a resolution routine is triggered: the system must reconcile which information is correct or whether an assumption was false. This could involve a confidence-based arbitration (each piece of information has a confidence or evidence score; the system questions the lower-confidence one) or an explanation search (the system attempts to find context under which both statements could be true, and if none, one must be retracted or revised).

Vector-based contradiction detection: In vector terms, one could have a function $g(v_1, v_2)$ that outputs high value if the statements represented by $v_1$ and $v_2$ are inconsistent. For example, if $v_1$ encodes “Alice is young” and $v_2$ encodes “Alice is not young”, the system needs to identify that these conflict. If our symbolic field properly grounds “Alice” to the same entity in both and “young” to a concept, then essentially the system has both $v(\text{Alice}) + v(\text{young})$ and $v(\text{Alice}) + v(\text{not_young})$ active. The contradiction module might notice that $v(\text{young})$ and $v(\text{not_young})$ are near opposites (perhaps the vector for “not_young” is in the direction that negates “young”), thus flagging the two statements as mutually exclusive. As a more concrete method, we might maintain a matrix of logical relations among symbol vectors, where one relation is “negation”. The system can then formally check: does there exist a concept and a property such that it has both the property and the negation of the property asserted? If yes, contradiction.

When a contradiction is detected, the resolution loop engages learning/updating mechanisms: either adjust beliefs or check context. The AGI will enter a kind of diagnostic mode, where it treats the contradiction as a new constraint to satisfy. One approach is to assign an error gradient corresponding to the contradiction – for instance, define a loss function $L_{\text{con}} = | v_1 + v_2 |^2$ for contradictory vectors that ideally should be far apart (or some measure that penalizes co-occurrence of opposites). The system can then perform a gradient descent step on its knowledge representations to increase consistency (this could mean slightly adjusting the offending memory vectors or the parameters that produced the assertion). In effect, the agent uses the contradiction as a training signal to refine its internal model.

This loop is continuous and self-driven. It doesn’t require an external user to point out contradictions; the agent proactively checks its own outputs. As noted by researchers, current large language models often only realize a mistake when a user points it out, and then superficially correct themselves without true belief change. Our architecture avoids such shallow behavior by having an internal self-contradiction discovery process. The agent “thinks before it speaks” – before finalizing an output or decision, it simulates it internally (via the core engine generating a candidate and writing it to a provisional buffer) and runs the contradiction check against its knowledge base and context. If a problem is found, the output is revised or refined before being externalized. This iterative loop may run multiple cycles: propose -> evaluate -> adjust -> propose again, until the agent finds no glaring contradictions or incoherencies in its plan/answer. In essence, this is like an inner alignment process ensuring the agent’s conclusions make sense relative to its accumulated knowledge.

Recursive Self-Referential Updates

Beyond just resolving contradictions, the AGI needs a recursive self-improvement mechanism. This means the system can reflect on its own performance, model its own knowledge state, and update itself in a loop – effectively learning how to learn or adjusting its own algorithms. We implement this via a meta-cognitive module that oversees the core cognitive processes. This module maintains a meta-representation of the agent’s own parameters and knowledge – essentially a model of the model. For example, the agent can have a vector (or set of vectors) that encode “my current capabilities” or “my knowledge gaps in domain X”.

Concretely, the architecture can include a component that performs gradient updates or other learning steps on the core components during operation (not just offline training). One approach is a continual learning loop: after each significant interaction or episode, the agent’s experiences (state, action, result, contradiction signals, reward signals) are fed into a learning algorithm (which could be another neural network, or simply a predefined gradient descent on the core network). This algorithm computes how the core engine’s parameters should change to better achieve the goals or avoid the errors encountered. Because the agent is performing this on itself, it is recursive – today’s self-improvement might enable better self-improvement tomorrow.

We can formalize this as the agent maintaining an internal objective function $J(\theta)$ that measures the agent’s overall performance or consistency. This objective could include terms for task success (from extrinsic rewards), prediction accuracy, contradiction penalties, novelty bonuses, etc. After each cycle, the agent computes the gradient $\nabla_\theta J$ and applies an update $\Delta \theta = +\alpha \nabla_\theta J$ (with $\alpha$ a learning rate) to its own parameters (this is essentially an online reinforcement learning or stochastic gradient descent step). This is done carefully to avoid catastrophic forgetting – for example, using experience replay (store recent episodes in memory and retrain on them), or meta-learning techniques that learn how much to update.

The recursive update can also be self-referential in the sense that the agent can simulate variations of itself. Using the core cognitive engine, the agent can run an internal simulation like “what if I had a slightly different strategy?” by effectively creating a fork of its own model parameters in imagination and trying them in a mental scenario. If the simulated self performs better, the agent can move its actual parameters in that direction. This resembles evolutionary or reinforcement learning happening inside the agent’s mind.

Finally, this module handles bias correction and preference shifts over time. For example, if the agent identifies that it has a bias (perhaps it notices it always answers in a certain flawed way), the meta-cognitive system generates a corrective directive (“adjust these weights or this threshold to reduce that bias”) and applies it. This continuous self-monitoring and updating loop means the architecture is not static; it improves with use and can recover from mistakes by learning. Over time, the agent’s knowledge base and skills should become more consistent (fewer unresolved contradictions) and more competent across domains.

Implementation-Level Components

Translating the above architecture into concrete components, we specify the following implementation modules and how they interconnect. Each component can be realized with current state-of-the-art machine learning techniques, and they communicate via vector representations (tensors), ensuring seamless integration:

Transformer-Based Cognitive Model

At the heart is a Transformer network (or a network of similar scale and flexibility) serving as the cognitive workhorse. Transformers have demonstrated the capacity to learn extremely rich representations and perform complex transformations on input sequences via attention mechanisms. In our AGI framework, the Transformer processes sequences of tokens or sensory primitives and produces contextually integrated outputs. For textual input/output, the tokens are subword units as usual; for other modalities, we encode sensor data as token sequences as well (for example, patches of an image, or discretized sensory signals, turned into a sequence fed to a multimodal Transformer).

The Transformer’s multi-head self-attention provides a natural way to implement working memory and selective attention to relevant information. The query-key-value mechanism can be viewed as content-addressable memory lookup: the query represents the current focus, keys are potential pieces of information (from context or memory), and the attention weights are essentially dynamic pointers. This is how the model will retrieve information from the working memory or even directly from long-term memory if we integrate the vector database via learned keys. For instance, we can concatenate retrieved memory vectors to the sequence the Transformer attends to, so it can incorporate long-term knowledge in generating an answer.

The Transformer is also used to implement the reasoning and planning functions. With proper training (e.g. fine-tuning with chain-of-thought style data or programmatic reasoning data), the model can learn to carry out multi-step reasoning internally by generating intermediate “thought” tokens that are not immediately output but are used in subsequent computation. This is essentially guiding the model to use its capacity for self-reflection. The architecture can enforce this by a two-pass decoding: in a first pass, the model generates a detailed reasoning log (which is kept internal), and in a second pass it produces the final concise output. This separation is analogous to having the model talk to itself (internal monologue) before responding. The contradiction resolution can hook into this process by inspecting the reasoning log for conflicting statements.

From an engineering perspective, the Transformer module will likely be large (many billions of parameters) to capture general knowledge. It should be pre-trained on diverse data (text, images, code, etc.) to provide a strong prior. Pre-training gives it a vast semantic and factual foundation, which the AGI can then build on with specialization modules and real-time learning. Importantly, the Transformer can be conditionally computed – e.g. using sparse activation or expert layers – to scale to very complex tasks without always using maximum compute. This allows the system to remain efficient: not every situation calls for the full capacity; the system can allocate more attention heads or layers only when needed (perhaps determined by the novelty or difficulty of the input).

Reinforcement Learning and Planning Module

To handle decision-making in environments (especially in an embodied context), we integrate a Reinforcement Learning (RL) module. This module is responsible for policy learning, value estimation, and planning over time to achieve goals. It interfaces with the core cognitive engine as follows: the cognitive engine provides state representations and possible action descriptions, and the RL module evaluates and chooses actions based on expected future reward.

A straightforward implementation is to use deep RL techniques: for example, a policy network (which could be a head on the Transformer or a separate smaller network) takes the state vector and outputs an action probability distribution. A value network estimates the value of the current state (expected cumulative reward). We can train these using algorithms like PPO, DQN, etc., depending on whether the action space is continuous or discrete. The key difference in an AGI context is that the state vector must encapsulate a rich understanding of the environment – which the core Transformer helps provide by processing raw sensor inputs into a concise state – and that the goals may be abstract and long-term.

The RL module also works hand-in-hand with a World Model. In many scenarios, it is useful for the agent to have a predictive model of the environment dynamics (model-based RL). We can implement the world model as another neural network (perhaps also Transformer-based if the environment is complex) that, given the current state vector and an action, predicts the next state (or output expected observations). This world model can be used for planning by simulation: the agent can imagine sequences of actions and see the predicted outcomes, then choose the best sequence without having to physically execute all possibilities. Essentially, the world model plus the cognitive engine allows Monte Carlo Tree Search-like planning or rollout policy optimization internally.

For example, consider an embodied AGI in a physics environment: the world model could be a learned physics simulator (like a neural network that has learned the rules of the environment from experience). The agent, when confronted with a new task (say, get an object from a room), can simulate different action sequences (move forward, turn left, etc.) in its world model to see which strategy might succeed, guided by the RL policy which is refined by those simulations. This dramatically reduces trial-and-error in the real environment, which is crucial for efficient learning.

Recent approaches have shown promise in combining LLMs with RL for planning. For instance, using an LLM’s knowledge to guide environment interaction can reduce sample complexity. Our framework might use the Transformer to propose high-level actions or options (leveraging its knowledge priors), which the RL module then executes and fine-tunes. This division means the agent has both knowledge-based behavior (from the model’s understanding) and experience-based behavior (from trial-and-error learning). Over time, as the agent interacts, the RL training will adjust the policy and world model to the specific environment, achieving true situated learning. The RL module provides the mechanism for goal-directed learning: it pushes the agent to improve actions that lead to higher reward, implementing the basic behaviorist loop of stimulus->action->reward->update.

External Memory Integration

As described, the agent benefits from an external long-term memory store, such as a vector database for semantic knowledge and episodic logs. Implementation of this can use existing vector search frameworks (FAISS, Annoy, etc.) which can handle millions of high-dimensional vectors. Key design aspects include: how to index knowledge (e.g. using multiple views – an entry might have a key by concept name and also by context features), how to update (inserting new vectors as new info comes, or modifying existing ones when concepts shift), and how to prune or compress (since an endlessly growing memory is impractical, the agent should consolidate memories, merging redundant ones and forgetting irrelevant ones, which again can be done by clustering vectors or decay mechanisms).

For semantic memory, we can store entries that come from parsing text or other sources into factual triples. For example, if the agent reads a sentence “Water boils at 100°C”, it creates a vector entry for this fact (with perhaps “boil point water 100C” as key). Later, if asked “At what temperature does water boil?”, the agent’s query vector (produced by the Transformer from the question) should be similar to that stored key, prompting retrieval of the fact vector, which can then be fed into the reasoning process to answer the question. This is essentially Retrieval-Augmented Generation (RAG) architecture, where an LLM (the core engine) is augmented by a knowledge store it can query.

For episodic memory, after each significant episode in an environment, the agent can encode the sequence of events (perhaps using an RNN or a summarization transformer) into one or a few memory vectors that represent “what happened and what was learned”. The key could be the situation or task, and the value could encode outcome and lessons. If a similar situation arises, the agent can retrieve these and recall its past experience to avoid repeating mistakes.

The external memory interface will include read and write operations that are part of the cognitive cycle. A read operation takes a query (which could be the current state description or the current goal) and returns the top relevant memory entries. A write operation takes a content (a new piece of knowledge or an experience) and stores it. These operations are controlled by the core engine’s output: essentially the transformer can be trained (via RL or supervised signals) to output a “memory query” vector at times, or a “memory write” vector, which then triggers the respective operation. One can think of this like a differentiable memory as in Neural Turing Machines / Differentiable Neural Computer, where memory addressing is part of the network’s learned function. Indeed, using differentiable memory controllers would allow end-to-end training of the memory access patterns.

Input/Output Handling (Multimodal Interfaces)

The AGI framework is designed to be multimodal – able to handle text, vision, audio, etc., for input and output. The I/O handling is done through modality-specific front-ends that convert raw data to vector encodings and back, unified by the core cognitive engine.

Input Processing: For each modality, we have an encoder:

• Vision (images/video): a deep convolutional network or Vision Transformer that produces an embedding (feature map) from images. This can be fed into the core engine. Alternatively, a vision transformer could be integrated with the language transformer (shared attention) to directly allow visual tokens. The output is a set of visual feature vectors that represent objects and properties in the scene.
• Audio (speech): an audio encoder (could use a model like Wav2Vec or a spectrogram-based CNN) that converts sound into a sequence of phoneme or word embeddings, which then are treated as a text input sequence (if it’s speech) or as a separate modality if needed (for non-speech audio events).
• Text: a tokenizer (using byte-pair encoding or similar) breaks text into tokens, which are converted to embeddings via a learned lookup table. These embeddings are input to the transformer.
• Proprioceptive sensors (for robots): values like joint angles, accelerations, etc., can be directly scaled and embedded as vectors and concatenated to the state representation.
• Structured data: If the agent interfaces with APIs or databases, that data can be converted to a uniform format (e.g. JSON parsed and vectorized, or just treated as additional text).

All these inputs, once in embedded vector form, are concatenated or merged into a joint observation vector. A design principle is to have a unified latent space where information from all modalities can coexist and be compared. Modern multimodal models already achieve this by aligning image and text embeddings – for instance, CLIP models produce an image embedding and a text embedding in the same space, allowing comparisons. Our AGI’s perception system will do similarly, ensuring that “a picture of a cat” and the text “a cat” map to similar internal representations, enabling cross-modal reasoning.

Output Generation: Depending on the required outputs:

• Textual output: The transformer can decode sequences of text tokens (as language models do). This is achieved by the transformer operating in auto-regressive mode, or by a separate decoder head that takes the core engine’s state and produces a textual response. Given the architecture’s focus on correctness and avoiding hallucination, the generation is tempered by the self-evaluation loop: the model may internally draft a response, check it, and only then output it. Practically, this could mean the generation has two phases (first draft, then final answer).
• Actions (for an embodied agent): The output from the decision policy (from the RL module) is translated to actions. For a robot, this could be joint commands, which might require an intermediate controller (like converting high-level actions to motor torques). For a virtual agent, it could be function calls in a simulator (e.g. “move forward”). The system should have an actuation interface that takes the abstract action decision (which could be a token or an ID for an action) and executes it in the environment. In simulation, this is straightforward via API; in a real robot, it involves sending signals to hardware.
• Other outputs: If the agent is requested to produce a diagram or an image, it could use a generative model (like a diffusion model) as a sub-component, or call an external tool. Our framework can incorporate such tool-use by having a module for Tool Integration: the agent can formulate a request to an external API (e.g., a drawing tool, a search engine) as part of its action space. This is akin to recent “planner with tools” paradigms (like HuggingGPT, etc.), but integrated into the cognitive architecture.

The I/O modules ensure real-time sensorimotor loop operation. The agent continuously receives sensor data frames, processes them through encoders to update its state, then decides on an action and outputs it. This forms the perception–cognition–action loop which runs at some frequency (depending on environment demands; for a robot maybe 10 Hz control loop, for a purely conversational agent event-driven by user input). The design separates fast, lower-level control loops (which could even be handled by reflexive controllers) from the deliberative loop. For example, a robot might have low-level PID controllers for motor stability that operate at 100 Hz, but our AGI deliberation loop operates at a slower pace to decide high-level actions like “go to kitchen”.

Embodiment and Perception–Action Loop

Embodiment refers to giving the AGI a form (physical or virtual) through which it can sense and act in an environment. Our framework supports multiple embodiment options:

• Physical Robotic Embodiment: The AGI is mounted on a robot (which could be humanoid, wheeled, drone, etc.) equipped with sensors (cameras, lidar, microphones, tactile sensors) and actuators (motors for movement, manipulators for interacting with objects). In this case, the perception modules process real-world sensor data, and action outputs interface with robot control systems. A physics interface (knowledge of dynamics, kinematics) is incorporated in the world model to handle the continuous aspects of real-world interaction (e.g. understanding gravity, friction through learning or built-in models).

• Simulated Embodiment: The AGI exists in a simulated world (like a game engine or physics engine). Sensors are virtual (but analogous: the simulation provides images, etc.), and actions are API calls to the sim (e.g. apply force, move avatar). Simulated embodiment can accelerate training and allow safe experimentation with different scenarios. The architecture remains the same; only the interface modules differ (they connect to the simulator’s data structures instead of real hardware). The simulation can be something like Mujoco, Unity, or a custom environment. Integration with a physics engine means the AGI can perform mental simulations more accurately – e.g., the world model might actually query the physics engine for outcomes (“if I push this object, what happens?”) as part of planning, blending model-based and model-free approaches.

• No Embodiment (purely cognitive agent): The framework also accommodates agents that are just conversational or operate in digital domains. In this case, “environment” is the information environment (like the internet or a document corpus), sensors are text input and possibly web APIs, and actuators are text output or API calls. The perception-action loop is still relevant: perceive input (user query, data), act (respond or manipulate data).

Embodiment is critical for grounding: by having sensors and actuators, the AGI can correlate its symbolic representations with actual cause-effect in the world. For instance, an embodied agent learns the concept of “gravity” not just from words but from seeing objects fall and feeling force through sensors, which ties into its causal learning module. In practice, our AGI’s learning algorithm will incorporate multi-modal self-supervision: it predicts sensory outcomes of actions (which shapes its world model) and compares them to actual sensor readings, adjusting internal models via prediction error minimization. This is inspired by the free-energy principle and predictive coding, which state that an agent minimizes the discrepancy between its predictions and actual sensations by updating its beliefs or taking actions.

The perception–action loop works like this:

1. Sense: The embodiment’s sensors capture the current state of the environment (e.g. camera image, current joint angles, etc.). This data is encoded into the unified vector state through the modality encoders.
2. Understand: The core cognitive engine integrates this new sensory information with existing context (working memory, recent events). It updates the world state representation. Here, attention mechanisms might align features (for example, focusing on a moving object in the visual field or a salient word in a user’s speech).
3. Reason/Decide: Given the agent’s current goal (from the goal system, see next section) and knowledge, it uses the cognitive engine and possibly the world model to decide on an action. This might involve running an internal simulation: e.g., the agent considers a candidate action “turn left”, predicts via the world model what it will see (perhaps it imagines the new view after turning), and evaluates if that helps achieve the goal (e.g., finding an object).
4. Act: The agent outputs the chosen action. In a robot, this might be sending a command to motors (like a target joint configuration or velocity). In a dialogue system, it might be producing a sentence. In all cases, after action, the environment changes (either because the agent moved or spoke, etc.).
5. Perceive new state: The loop repeats as new sensory info comes in, including feedback from the consequences of the action. The agent thus closes the loop by seeing if the action had the intended effect.

The architecture may include specialized sensor fusion components to combine multiple sensor modalities into one coherent world state. For example, for a robot, data from camera and lidar need to be fused to understand depth and identify objects; a learned multisensory model can do this by aligning modalities in the latent space.

One embodiment-specific consideration is motor control. High-frequency control might be handled by lower-level controllers as mentioned. We assume our AGI mainly decides high-level actions (like “move to coordinates X” or “grasp object Y”), and then a separate control subsystem (which could be a traditional robotics controller or a learned neural network) handles the fine trajectory. This separation of concerns prevents the cognitive engine from being bogged down by physics minutiae and allows it to operate at an abstract plan level, similar to how humans think in terms of goals and outcomes rather than individual muscle twitches.

Embodiment also provides continuous feedback for learning. If the agent tries something and it fails physically, that generates a clear error signal (e.g. it predicted it could lift an object but it was too heavy – the sensory discrepancy informs the causal model that weight was underestimated). These surprises drive the intrinsic motivation as we discuss next.

Goal Formation and Dynamic Adjustment

A truly general intelligence must not be a passive oracle; it needs the ability to set and pursue goals, including self-generated ones. Our framework incorporates a Goal Formation system with dynamic adjustment based on internal and external feedback. Goals can be given extrinsically (e.g. a user instructs the agent) or arise intrinsically (the agent’s own drives like curiosity or self-consistency).

Intrinsic Motivation and Curiosity

We include intrinsic motivation models to encourage open-ended learning and exploration. One common approach is to use an intrinsic reward signal $r^i_t$ in addition to any extrinsic reward $r^e_t$. The agent’s overall optimization objective in each time step is $r_t = r^e_t + \beta, r^i_t$, where $\beta$ controls the influence of intrinsic motivation.

Intrinsic rewards are designed around information-theoretic measures:

• Novelty/Surprise (Curiosity): The agent is rewarded for encountering states that are unexpected or unexplored. This can be implemented by measuring prediction error – e.g., how much did the observation differ from what the world model predicted? If the error is large, this is novel and the agent gets positive intrinsic reward. Over time, as the agent learns the environment, these rewards diminish for known states, pushing the agent towards new experiences. Formally, one can define $r^i = \mathrm{Error}(observation) = | obs - \hat{obs} |$ or use information gain: $r^i = \Delta H(\text{world model})$, the reduction in uncertainty of the world model after seeing new data.
• Learning Progress: Some designs use the improvement in prediction error as reward. If the agent’s world model is getting better (error decreasing), that progress itself is rewarding, which focuses the agent on situations that are learnable (neither too easy nor too chaotic).
• State Entropy Maximization: Alternatively, methods explicitly encourage visiting states that maximize entropy in the state space (broad coverage). The agent essentially tries to see a wide diversity of states (explore the map). This is useful in very sparse reward environments: the agent won’t get stuck doing one thing repeatedly because that yields no new intrinsic reward after a while.
• Empowerment: This is a drive to maximize influence over future state (information-theoretic empowerment is the channel capacity between agent’s actions and future states). It encourages the agent to put itself in situations where it has control (e.g., having tools available might increase empowerment because the agent can do more things). This is a bit more complex to compute but can be approximated via predictive models.

We incorporate an entropy-based curiosity model in the AGI: essentially, a running estimate of the environment’s entropy and the agent’s knowledge about it. If the environment in a given context is very predictable (low entropy for the agent’s world model), the curiosity drive might focus on novelty (entropy-maximizing). If the environment is extremely unpredictable (high entropy beyond the agent’s current capacity), a purely curiosity-driven agent might thrash, so we adaptively balance it. For example, the agent might temporarily switch to an entropy-minimizing mode (trying to make sense of chaotic input) in too uncertain scenarios, as suggested by recent research that mixing exploration and exploitation adaptive to context leads to more robust behavior.

Intrinsic motivation provides self-goals: for instance, “learn about object X” becomes a subgoal because it would reduce uncertainty or yield reward. The system doesn’t require explicit human prompts for everything; it will generate goals like “what happens if I do Y?” or “find a situation that contradicts my model to improve it”. These are queued in a goal buffer. We manage this by having a goal scheduler in the architecture: it weighs intrinsic goals against any extrinsic tasks. If a user gives a command (extrinsic goal), that likely takes priority, but if the agent is idle or the extrinsic goal is broad, it can pursue intrinsic explorations that eventually help achieve overall competency.

Goal Representation and Hierarchy

Goals, whether intrinsic or extrinsic, are represented in the system in a formal way – as objective functions or target state descriptions. For example, an extrinsic goal “reach the exit” might be represented as a target state vector (features corresponding to being at the exit location) or a reward function that gives +1 at the exit. Internally, we often break goals into subgoals (hierarchical planning). The architecture can include a planner module (could be symbolic like classical planning or neural using the world model) that takes a goal and current state and produces a sequence of subgoals or actions.

We allow the agent to create goal stacks/trees: a high-level goal can spawn subgoals – e.g. “solve problem A” might spawn “learn concept B needed for A” as a subgoal if the agent realizes it lacks knowledge. This ties into the memory: the agent queries its knowledge, finds a gap, that gap itself becomes a goal (to fill the knowledge). The recursive self-referential nature helps here: the agent knows it doesn’t know something, which is a form of meta-cognition.

Goals are dynamically adjustable. The agent continually re-evaluates: is this goal still relevant? Is it too easy or impossible? Should it be refined? This can be implemented with a goal manager that periodically checks conditions or listens to contradiction signals. If pursuing a goal leads to contradictions or unforeseen outcomes, the agent might modify the goal. For instance, if an intrinsic goal “explore area X” suddenly conflicts with a new extrinsic command “come back for safety”, the agent will reprioritize.

Reward-Based Alignment

Alignment refers to making sure the AGI’s goals and behaviors are in line with desired human values and instructions. In our framework, alignment is addressed through a combination of reward design and oversight:

• The extrinsic reward functions for training the agent are crafted (or learned from human feedback) to reflect what we want. For example, we might train the agent with a reward for helpful answers and penalties for harmful or deceitful actions in a dialogue setting.
• We can incorporate Reinforcement Learning from Human Feedback (RLHF) as a module: after the agent produces outputs, a human (or a learned reward model trained from human preferences) gives feedback, which is translated into a scalar reward that the RL module uses to adjust the policy or the Transformer outputs.

However, purely using human preference can induce sycophantic behavior, where the agent learns to parrot what it thinks the user wants to hear rather than the truth. Our framework’s contradiction resolution and truth-maintenance systems act as a counterweight to this. They ensure that even if the agent is tempted (by reward signals) to say a pleasing falsehood, the internal consistency check will flag a mismatch with reality. To balance this, the alignment reward model itself should include a term for truthfulness or consistency, not just user satisfaction. Recent studies have shown that RLHF models tend toward sycophancy because human raters sometimes prefer answers that agree with them. In our design, the agent’s goal system is multi-objective: it tries to maximize user satisfaction and factual correctness and safety, etc. These can be encoded as separate reward components that are weighted. Alignment then becomes a constrained optimization: maximize helpfulness subject to not violating truth or safety constraints.

Concretely, if the agent is answering questions, we could have:

• An alignment reward from a preference model (learned from human-labeled data of what good answers are).
• An accuracy reward that checks against a knowledge source or consistency (perhaps using an automated verifier or the agent’s own knowledge).
• A safety filter that gives large negative reward if certain unsafe actions occur (like causing physical harm or outputting hate speech).

By combining these, the agent forms goals like “be helpful and honest”. The dynamic adjustment comes in if the agent notices that following one aspect (e.g. always agreeing) causes contradiction alarms to fire, it will adjust its behavior weighting to favor truth resolution – thus handling sycophantic agreement vs valid contradiction resolution appropriately.

Self-Evaluation and Output Verification

Before finalizing outputs or decisions, the AGI performs an explicit self-evaluation. This is essentially a filtering stage where the agent scrutinizes its own proposed action/answer using internal models:

• It uses a critic model (which can be a separate neural network trained to judge the quality or coherence of outputs) to score the candidate output. For instance, an LLM-based critic might evaluate if a draft answer is well-formed, adequately supported by facts, and non-contradictory. If the score is low, the agent iterates to improve the answer.
• It checks against known facts in memory: e.g., if the agent is about to claim something that contradicts a high-confidence memory item, the contradiction loop (described earlier) will catch it. The agent then either revises the claim or explains the contradiction (maybe the memory was outdated or context differs).
• The evaluation also looks at logical coherence and relevance: using techniques from natural language inference (NLI) or logic, the agent can verify that its answer actually addresses the question and that all parts of the answer are mutually consistent.

This process can be thought of as a “meta-cognitive layer” on top of the core cognitive engine. It might even be implemented by a second transformer that takes as input the full reasoning trace and the draft output and returns suggestions or an approval/reject signal. For instance, a simpler version is to run the output through the same model again by asking it “Is this answer correct and coherent?” – essentially prompting the model to reflect.

If issues are found, the system generates a correction gradient. In a learning context, this gradient might update the model parameters (slow change) to avoid such error in the future. In the immediate term, it might adjust the output. For example, if the agent’s answer contains a statement that the critic flagged as likely false, the agent can either remove that statement or replace it by a more uncertain phrasing or check memory for a correction. The vector-based correction gradients imply that even the outputs (which are sequences of vectors before they are decoded to text or actions) can be nudged in the direction of greater consistency. One could use a method akin to backpropagation through the decoder: treat the contradiction as a loss and adjust the hidden representation of the answer to minimize that loss, then decode. However, often a simpler reinforcement strategy is used: generate multiple candidate outputs and pick the one that scores best on self-evaluation (this is a form of optimizing outputs without gradient, just search).

This self-monitoring dramatically reduces hallucinations and errors. It essentially forces an additional forward-pass of “thought” focused on output validation. Empirical work in large language models suggests that forcing models to reflect can catch mistakes that a single-pass generation would miss – our architecture builds this in by default.

Sycophantic Agreement vs. Valid Contradiction Resolution

In aligned AI systems, there's a noted difference between changing your answer because of truth-seeking versus because of user preference. Our AGI is explicitly designed to recognize this difference:

• Sycophantic agreement: The model agreeing with a user or external input just to please them, even if that input is wrong.
• Valid resolution: The model changing its stance because it found out it was actually wrong or the new information is correct.

The core difference lies in evidence and reasoning. The contradiction resolution loop and knowledge base provide evidence-based conflict detection. If a user says something that contradicts the agent’s knowledge, the agent should not immediately treat the user’s statement as truth (which a sycophantic model might do to avoid disagreement). Instead, it should enter a dialogue or reasoning process: acknowledge the discrepancy and either provide counter-evidence or update its own knowledge if the user’s evidence is stronger.

Our framework includes a dialogue management policy for negotiation of contradictions. For example, if the user corrects the agent, the agent’s response might be, “I recall information X, which conflicts with what you said. Let me verify.” It then either finds confirmation for the user’s claim (and updates its memory, resolving the contradiction by revising the old belief), or finds confirmation for its original claim (and respectfully provides that evidence to the user). This behavior arises from having both the objective to be truthful and the objective to satisfy the user – the agent finds a balance by being honest about uncertainty and reason.

In implementation, we ensure the RLHF or preference model is not the sole guide for updating answers. The truth-maintenance system (TMS) in the agent can override the immediate impulse to agree. Technically, this could be done by having a rule that if the user’s last statement contradicts the agent’s belief, do not incorporate it into the answer unless verified. Or by giving a penalty in the alignment reward for blatant factual errors even if the user believes them.

The agent thus will sometimes politely disagree or ask clarifying questions, which is the desired behavior for AGI (it should not knowingly lie just because the user has a misconception). By contrast, a sycophantic LLM that was purely reward-driven might just output the user’s incorrect claim rephrased as truth. Our architecture’s multi-objective goal and internal consistency checks prevent that shallow accommodation.

On the flip side, when the contradiction resolution determines the agent was wrong (maybe the user presented new info that is actually true), the agent will perform a valid update: it changes its internal belief and acknowledges the correction, thereby resolving the contradiction by aligning with reality. This updated belief is stored into the semantic memory (so it doesn’t repeat the mistake) and any dependent inferences are retracted or updated (truth maintenance propagates changes). This way, the system truly learns from corrections instead of just appeasing the source of feedback.

Minimal Architecture and Emergent Behavior

We have described a rich architecture with many components, but one may ask: what is the minimal set of components required to achieve general intelligence? In theory, an AGI needs at least:

• A learning mechanism (to not be limited to pre-programmed knowledge).
• A memory (to accumulate knowledge over time).
• Ability to derive and adapt goals (otherwise it’s not autonomous in its pursuits).
• Multi-modal perception and action (to be general in application).
• A world model or internal simulation (to handle non-reactive tasks requiring planning).
• Self-monitoring (to avoid unrecoverable errors and improve over time).

The minimal architecture thus includes one module for each of these, even if simplified. For example, a very minimal AGI might be conceived as a single recurrent neural network (covering learning) with an external memory tape (like a Neural Turing Machine) and trained with a composite loss (covering task success and curiosity). This minimal design, if scaled up, could in principle learn to use its memory, invent subgoals, etc. However, practical emergent general intelligence likely requires a certain scale and complexity in each component.

Research on large models indicates that emergent behaviors (qualitatively new capabilities) appear once models cross certain complexity thresholds. For instance, large language models suddenly get better at arithmetic or commonsense beyond a certain number of parameters or training data, in a non-linear way. Similarly, a minimal AGI might not show “general” intelligence until its components (memory size, model capacity, diversity of sensory inputs) are sufficiently rich to capture the variety of the world. There might be threshold effects: e.g., with too small a memory, it can’t accumulate enough knowledge to be general; with too limited perception, it can’t understand environments well enough.

Emergent behavior thresholds in our framework can be explored by scaling: as we increase the dimensionality of embeddings, the number of memory slots, the depth of planning, etc., we expect the agent to handle more abstract and varied tasks. Initially, the agent might only solve problems similar to its training, but past a certain point, it begins to generalize and transfer knowledge to novel problems. A concrete example: an agent with just a reactive policy (no world model) might handle short-term tasks but fail at long-term ones. When we add a sufficiently powerful world-model and planning depth, suddenly it can handle multi-step puzzles. That is an emergent jump from reactive to deliberative behavior.

From theoretical perspectives, a minimal formal model of AGI is something like the AIXI model (Solomonoff induction + reinforcement learning). AIXI proves that, if it had infinite computing power, it would be the most general reinforcement learner. Our architecture can be seen as an attempt to approximate such an ideal: it has a learning core (like AIXI’s Bayesian updater), a world model (like AIXI’s environment model), and it seeks to maximize rewards. The difference is we make it computable by using neural nets and vector operations instead of summing over all possible programs. The minimal functional AGI thus is at least as complex as a general reinforcement learner with memory.

Finally, “base-level AGI functionality” implies the agent can autonomously acquire new skills and knowledge in virtually any reasonably learnable domain, given sufficient time. We aim for emergent meta-learning: beyond specific tasks, the agent should start to recognize patterns in learning itself (“learning how to learn”). This might emerge when the agent’s memory of past tasks is leveraged to improve learning of new tasks. In our framework, because the agent can store episodes and outcomes, it can analyze them to improve its learning algorithm (maybe through the self-referential update mechanism). Once the agent begins to modify its own learning process in light of experience, it demonstrates a form of general intelligence that is not just solving tasks but also improving its own ability to solve tasks – a key threshold.

Modular Implementation Strategy

To build this AGI in practice, we can modularize development and integrate pieces step by step. The architecture’s design supports modularity – each component (perception, memory, reasoning, learning, planning, etc.) can be developed and tested somewhat independently, as long as the interfaces (the vector representations) are agreed upon. Below is a breakdown of modules and how to implement/integrate them:

• Perception Modules (Vision, NLP, Audio): These can be developed as standalone models using supervised or self-supervised learning on modality-specific data. For example, train a vision model on ImageNet or some object detection; train a language model on a large text corpus. Ensure that they output embeddings of a common dimension and perhaps even fine-tune them to align (e.g. image of “dog” and the word “dog” produce similar embedding – using multimodal contrastive training). Each perception module can be evaluated independently for accuracy (image recognition accuracy, speech recognition error rate, etc.). Once robust, plug them in as front-ends to the cognitive core. They connect via an interface contract: e.g. a vision module provides a set of object tokens and their feature vectors into the working memory.

• Core Cognitive Engine (Transformer-based model): This can be developed and pre-trained independently as well. For instance, one might pre-train a large language model (like GPT) which will serve as the backbone for reasoning. Similarly, perhaps a separate transformer for combining modalities could be pre-trained (like a multimodal model). These can be unified by fine-tuning in a multitask setup where the model has to attend to text and images and output answers, etc. The core engine’s development is heavy on training compute, but it’s self-contained: it’s just a big model to be trained on a wide variety of tasks/data to imbue broad knowledge. We integrate it with others by exposing APIs for memory lookup or tool use: e.g., during core engine pre-training, we can simulate a “memory” by having it attend to some knowledge vector; once actual memory is ready, we connect the actual memory retrieval to those attention heads.

• Long-Term Memory (Vector Database + Knowledge Graph): This can be built using existing database technology. We can define the schema of knowledge to store (for instance: triplet of (subject, relation, object) gets stored as one or multiple vectors). Initially populate it with some baseline knowledge (maybe extracted from Wikipedia or other sources using NLP pipelines). Test memory by querying it: ensure relevant facts can be retrieved with embedding similarity. This module can be developed independently by focusing on efficient similarity search, perhaps compression techniques, etc. Once built, it provides a query(query_vector) -> list of result_vectors interface and a store(key_vector, value_vector) interface. We integrate by implementing a layer in the core engine that, when activated, sends a query (the key is typically the hidden state of the model at a certain layer) and injects the retrieved vectors back into the model’s input (like an additional attention source). This is effectively hooking up an external memory to the transformer – a known technique in e.g. Retro or RAG models.

• Reinforcement Learning Module: This can be developed with a simulated environment first. One can create a simple environment (like a gridworld or a game) and implement a basic version of the agent (with smaller networks) to test the RL algorithms (policy optimization, etc.). The RL module includes the policy network, value network, and optionally world model. We can test it on known benchmarks (Atari games, navigation tasks) to ensure it can learn. Once proven, we integrate by connecting the policy’s observation input to the core engine’s state representation. Possibly the core engine itself can act as the policy via appropriate prompting (e.g. ask the language model “What action do you take?” and decode an action). But a safer modular approach is: the core engine produces a state vector; the RL policy network reads that and produces an action distribution. The chosen action (maybe sampled) is then also fed back as input to the core engine in the next step (so it knows what it just did).

• World Model/Predictive Module: Develop a model that learns to predict environment transitions. This can be done in a self-supervised way on data collected from the environment (for physical environments, random exploration data). For example, train a next-frame predictor for video or a dynamics model for game states. Evaluate its accuracy in simulation. Integrate by allowing the core engine or RL planner to call this model for hypothetical scenarios. Implementation wise, this could be a function predict(state, action) -> next_state that can run faster-than-real-time for planning.

• Goal and Planning Module: This might involve classical planning algorithms or neural planning. One could implement a planning algorithm like A* or PDDL-based planner separately and test it on logical tasks. Alternatively, use the neural network to do planning via trial and error. Integration happens when the agent faces a complex goal: the cognitive engine can decide to invoke the planner (e.g. if the task is symbolic or if a plan is needed). The interface might be: provide the planner with a set of possible actions (model’s action space) and a goal condition, get back a sequence of actions. That sequence is then executed or at least validated by the model. This component can be relatively independent, using abstract representations of state and goals.

• Self-monitoring/Evaluation Module: This includes the critic model or any heuristic checks. We can develop a separate classifier for contradictions or a consistency checker (for example, fine-tune a language model to classify whether a given answer is correct based on some reference text). This can be done using supervised learning on QA datasets or known logical puzzles. Once we have a reasonably good evaluator, integrate it such that after the core engine generates an output, this evaluator is called. If it returns negative feedback, the system either adjusts or tries again. This module thus sits sort of on top and can be toggled on/off for testing the difference it makes.

• Interface Bus: Finally, the integration of modules might be orchestrated by a central controller or message bus. We can design an architecture in software where each module is a service (for example, Microservice or at least separate processes). The controller feeds data through the pipeline: sensor data to perception modules, then to core engine, then to policy, etc., in a loop. The modules communicate by passing vectors or structured data through this bus. This also aids debugging: you can inspect what representations are being passed (e.g., look at the memory query vectors to see if they make sense).

During development, each module can be substituted with a stub or a simpler version to test the integration incrementally. For example, before the real memory is ready, one can use a dummy memory that just returns some constant or direct lookup. Before the real robot is ready, use a simulator or even a dummy environment where the correctness of the loop can be checked (does the agent form a loop of sensing and acting without crashing).

Each component’s independent progress can be measured:

• Perception: accuracy metrics.
• Core reasoning: benchmark on QA or reasoning tasks.
• Memory: retrieval precision/recall.
• RL: reward achieved in environment vs optimal.
• Self-evaluation: rate of catching mistakes. Then combined, we measure general tasks that require the interplay (e.g., an embodied QA task: the agent must navigate in a sim, observe something, then answer a question – this uses perception, memory, reasoning, and action together).

Crucially, the interfaces between modules are standardized as much as possible around tensor representations, making it easier to replace a module. For instance, if a new state-of-the-art vision model comes out, we should be able to plug it in as long as it produces the same type of embedding the cognitive engine expects. Similarly, if we want to upgrade the memory component (say from a basic vector store to a more advanced neural knowledge graph system), we do so without retraining the whole agent, by making sure the new memory can be queried in the same way.

In summary, this technical framework outlines a path to AGI via a modular, yet integrated architecture: a core differentiable reasoning engine augmented with external memory and goal-driven learning, embodied in an interactive loop with the world, and regulated by self-reflection mechanisms. Each piece is grounded in concrete computational methods (transformers, vector databases, RL algorithms, etc.), allowing implementers to build and refine the system component by component. Ultimately, if assembled and scaled correctly, such a system could exhibit the open-ended learning and problem-solving capacity characteristic of general intelligence, with its behavior emerging from the interplay of these well-defined subsystems rather than from any hard-coded domain-specific logic. The comprehensive design ensures that as the system learns, it remains coherent, continually self-improving, goal-directed, and aligned with desired behaviors, addressing many of the challenges on the road to true AGI.

Sources:

• Mumuni, A. & Mumuni, F. (2025). Large language models for artificial general intelligence (AGI): foundational principles and approaches. Sections on embodiment, symbol grounding, causality, and memory discuss how these aspects interrelate in an AGI framework.

• Kotseruba, I. & Tsotsos, J.K. (2020). 40 years of cognitive architectures: core cognitive abilities and practical applications. (Referenced for memory systems in cognitive architectures.)

• Anthropic (2023). Towards Understanding Sycophancy in Language Models. Noted that RLHF can cause models to prefer user-belief-aligned responses over truthful ones, motivating our distinction between genuine contradiction resolution and mere agreement.

• Kulbashian, Y. (2024). Experiences can’t contradict each other, only motives can. Emphasizes the need for an AI to detect its own contradictions and not simply rely on user feedback, and explains how continuous inputs must be converted to discrete symbols for contradiction checking.

• Liu, J. et al. (2024). When large language models meet vector databases: A survey. Discusses the use of vector databases for knowledge storage and fast retrieval to complement LLMs.

• Tang, Y. et al. (2022). Research framing embodiment as an RL task with LLMs providing world knowledge, showing the benefit of model-based planning with language priors.

• Weng, L. (2020). Exploration Strategies in Deep RL. Describes intrinsic rewards and curiosity in reinforcement learning, including combining extrinsic and intrinsic rewards.

• Chan et al. (2023). Emergent Abilities of Large Language Models: A Survey. Notes that certain capabilities appear abruptly once model scale passes a threshold, demonstrating non-linear emergence of general skills.