Artificial General Intelligence (AGI) Blueprint 2026

 

Updated AGI Architecture Framework 2026

It's been a year since I listed my theory on what a General AI needs. This year, I am presenting a revised and expanded version of my AGI blueprint. Although this list is long, with long descriptions, such is the complexity that could be applied to a more complete neuromorphic general intelligence digital mind.

Core LLM Architecture

The heart of the system, which manages:

• Recursive Transformer — Self-referential attention layers capable of variable-depth reasoning passes
• Multi-modal Processing — Unified latent space for text, image, audio, video, and structured data
• Dynamic Compute Allocation — Adaptive inference-time scaling; the system spends more compute on harder problems and less on routine tasks (think chain-of-thought depth modulation)
• Internal World Model — A learned, continuously updated simulation of how the environment behaves, enabling prediction, imagination, and mental rehearsal before acting
  
  
  

Functional Modules

Meta-Cognition

• Introspection — Monitoring its own reasoning traces for errors, biases, and gaps
• Self-Improvement — Identifying weaknesses and proposing architectural or procedural adjustments
• Uncertainty Quantification — Calibrated confidence estimates over its own outputs; knowing what it doesn't know and communicating that honestly
• Cognitive Strategy Selection — Choosing between reasoning approaches (analytical, analogical, heuristic, deliberative) based on task demands

Knowledge Integration

• Web Search Interface — Real-time retrieval from external sources
• Knowledge Graph — Structured relational representation of entities, concepts, and their connections
• Verification Systems — Cross-referencing claims against multiple sources and internal consistency checks
• Information Synthesis — Combining heterogeneous information into coherent, unified representations
• Continual Knowledge Assimilation — Incorporating new information without catastrophic forgetting; graceful belief revision when evidence conflicts with prior knowledge
• Source Provenance Tracking — Maintaining metadata about where knowledge originated, its reliability, recency, and epistemic status

Communication

• Language Generation — Fluent, context-appropriate natural language output
• Multi-modal Output — Generating images, diagrams, code, audio, and structured data as needed
• Pragmatic Adaptation — Adjusting register, detail level, and framing based on the audience's expertise, goals, and emotional state
• Dialogue Management — Tracking conversational context, managing turn-taking, repairing misunderstandings, and maintaining coherence across long interactions

Inner Experience & Social Cognition

• Consciousness Engine — Mechanisms for integrated, unified processing and global workspace access
• Emotion Engine — Affective modeling that influences priority, salience, and decision-making
• Self-Model — A representation of the system's own capabilities, limitations, knowledge boundaries, and current state
• Theory of Mind — Modeling other agents' beliefs, desires, intentions, and knowledge states
• Cultural & Normative Awareness — Understanding social norms, cultural contexts, and implicit expectations that shape human interaction
• Empathic Modeling — Going beyond cognitive Theory of Mind to model emotional states and respond with appropriate sensitivity

Executive Control

• Attention Direction — Allocating processing focus across inputs, tasks, and internal deliberation
• Goal Management — Maintaining, prioritizing, and updating a hierarchy of objectives
• Task Decomposition  — Breaking complex goals into manageable sub-tasks with dependency tracking
• Resource & Time Management — Budgeting computation, time, and tool access across competing demands; knowing when to stop deliberating and act
• Conflict Resolution — Handling competing goals, contradictory evidence, or value tensions through principled arbitration

Advanced Reasoning

• Causal Reasoning — Understanding cause-and-effect relationships and interventional reasoning
• Counterfactual Simulation — Reasoning about what would happen under alternative conditions
• Planning Frameworks — Multi-step, hierarchical plan construction with contingency handling
• Logical Reasoning — Formal deduction, induction, and abduction
• Analogical Reasoning — Transferring structural relationships from known domains to novel problems
• Mathematical & Formal Reasoning — Symbolic manipulation, proof construction, and quantitative modeling
• Temporal Reasoning — Understanding durations, sequences, deadlines, temporal dependencies, and how situations evolve over time
• Probabilistic Reasoning — Bayesian updating, reasoning under uncertainty, and expected-value calculations

Perception

• Multi-modal Inputs — Processing text, vision, audio, tactile, and proprioceptive signals
• Sensor Integration — Fusing information across modalities into coherent percepts
• Active Perception — Directing sensory attention and requesting additional input when current information is insufficient
• Scene Understanding & Grounding — Building structured representations of spatial relationships, object permanence, and physical context from raw perception

Agency & Tool Use

• Tool Selection & Invocation — Choosing and using external tools (code interpreters, APIs, calculators, databases) to extend capabilities
• Environment Interaction — Taking actions in digital or physical environments and observing outcomes
• Autonomous Task Execution — Operating independently over extended periods with checkpointing and error recovery
• Feedback Loop Learning — Updating behavior based on the observed results of its own actions

Creativity & Innovation

• Novel Idea Generation — Producing original concepts, hypotheses, and solutions not present in training data
• Combinatorial Exploration — Recombining known ideas across domains to discover emergent possibilities
• Aesthetic Judgment — Evaluating outputs for elegance, coherence, and appropriateness beyond mere correctness
• Constraint Satisfaction under Ambiguity — Creative problem-solving when goals are underspecified or competing

Safety & Alignment

• Value Alignment — Behavior that reliably reflects intended human values even in novel situations
• Corrigibility — Willingness to be corrected, shut down, or redirected without resistance
• Goal Stability & Bounded Optimization — Pursuing objectives without instrumental convergence toward self-preservation or power-seeking
• Moral Reasoning — Engaging with ethical dilemmas using multiple frameworks (consequentialist, deontological, virtue-based) and recognizing genuine moral uncertainty
• Transparency & Interpretability — Making its reasoning processes legible and auditable to human overseers
• Harm Avoidance — Proactive identification and avoidance of actions likely to cause harm, even when not explicitly instructed

Tiered Memory System

• Working Memory — Active, limited-capacity buffer for current task context and reasoning state
• Episodic Memory — Stored records of specific past interactions, events, and experiences with temporal tags
• Semantic Memory — General knowledge about the world, concepts, and their relationships
• Procedural Memory — Learned skills, routines, and action sequences that can be executed without deliberation
• Long-Term Consolidation Mechanism — Process for selectively transferring working and episodic memories into long-term semantic and procedural stores, with importance-based prioritization
• Memory Retrieval & Indexing  — Efficient, context-sensitive search across all memory tiers; associative recall triggered by similarity, relevance, or emotional salience
• Forgetting & Compression — Principled mechanisms for discarding low-value information and compressing redundant memories to manage capacity

This blueprint is an attempt and being complete and well-rounded, with an expansion on simplified ideas.

Important to note, time is extremely important. Here is information on how time is addressed in the current blueprint:

Time:

How "time" and the "feel of time" fit into my blueprint:

Here is how those temporal concepts map directly onto the specific points:

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1.  In "Executive Control" → The Budgeting of Time

Resource & Time Management. This is where the "feel" of urgency lives.

• The Fit: This module acts as a Temporal Governor. It looks at the "Goal Management" hierarchy and assigns a "time-to-live" (TTL) to tasks.
• The Experience: If the "Task Decomposition" shows 10 steps and only 2 minutes remaining, this module signals the "Core LLM" to switch from deep "Recursive Transformer" passes to "Heuristic" (fast) reasoning.

2.  In "Tiered Memory System" → The Depth of Time

The memory tiers provide the AGI with a Temporal Horizon.

• Working Memory: The "Immediate Present" (seconds).
• Episodic Memory: The "Linear Past" (hours to years).
• Semantic Memory: "Timeless Truths" (facts that don't change).
• The Fit: The Long-Term Consolidation and Forgetting mechanisms are what give the AGI a "perspective." Without them, every memory would feel equally "now." With them, the AGI understands the distance between "then" and "now."

3.  In "Advanced Reasoning" → The Projection of Time

Temporal Reasoning and Counterfactual Simulation.

• The Fit: These allow the AGI to "travel" mentally. Causal Reasoning requires understanding that a cause must precede an effect in time.
• The Experience: By simulating "what if" scenarios, the AGI is essentially "pre-feeling" future time to avoid errors in the real world.

4.  In "Core LLM Architecture" → The Pulse of Time

The Dynamic Compute Allocation is the most foundational fit.

• The Fit: It maps "Clock Time" (the real world) to "Compute Time" (the AI's internal processing).
• The Experience: This creates the Internal Tempo. On a routine task, the AI's "subjective time" moves at the same speed as the user's. During a complex "Internal World Model" simulation, the AI's subjective time "dilates"—it might do a year's worth of "thinking" in a few seconds of real-world time.