Abstract Large language models have demonstrated unprecedented capabilities in language generation, reasoning, coding, and knowledge retrieval. However, their intelligence remains constrained by several fundamental limitations: difficulty maintaining persistent concepts, inconsistent reasoning, weak causal understanding, limited transparency, and challenges integrating information across modalities. Previous approaches have attempted to address these limitations through symbolic reasoning systems, knowledge graphs, ontologies, and multimodal neural networks. However, these approaches have generally solved only portions of the problem. This proposal introduces the Dynamic Concept Graph (DCG), a hybrid cognitive architecture that combines the strengths of neural representation learning, symbolic knowledge structures, multimodal perception, and analogical reasoning. The objective is not to replace foundation models, but to provide them with a persistent semantic substrate: a continuously evolving network of grounded concepts. The Problem Current AI systems are exceptionally effective at pattern completion. However, pattern completion is not equivalent to building a stable model of the world. A language model may correctly describe a hammer, but its representation does not necessarily correspond to an explicit object with: physical properties typical uses relationships to other tools causal effects historical context uncertainty visual characteristics affordances When encountering an unfamiliar object, humans do not require a complete definition. Instead, we construct hypotheses: “I have never encountered this object, but it resembles a tool, functions similarly to another object I know, appears in a workshop context, and shares properties with objects used for striking.” This capacity depends on structured conceptual relationships. Previous Approaches Symbolic AI Systems such as SHRDLU demonstrated that explicit representations enable reasoning. However, these systems operated within carefully restricted environments and struggled with open-world complexity. Knowledge Bases Projects such as Cyc attempted to encode common sense knowledge explicitly. The limitation was scalability: Human-created knowledge structures require enormous effort and become difficult to maintain. Semantic Networks Resources such as WordNet demonstrated the value of structured relationships between concepts. However, taxonomic relationships alone are insufficient. Knowing that a hammer is a tool does not explain how it interacts with the world. Neural Language Models Modern transformer architectures solved many problems previously considered difficult by learning statistical relationships from enormous datasets. However, knowledge remains distributed, difficult to inspect, and difficult to update. Multimodal Models Vision-language systems connect images and language, but generally do not create persistent conceptual objects shared across modalities. Proposed Architecture The Dynamic Concept Graph treats concepts as first-class entities. A concept is not merely a word embedding. It is an evolving structure containing: linguistic representations visual prototypes physical properties affordances taxonomy causal relationships contextual associations uncertainty estimates examples and counterexamples Example: Name: Hammer Type: Tool Related: Mallet, Nail, Wood, Workshop Affordances: Can strike, Can apply force, Requires hand interaction Physical: Hard, Portable, Usually metallic/wooden Historical: Derived from early stone tools Architecture: Multimodal Input ↓ Perception Layer ↓ Dynamic Concept Repository ↓ Relationship Engines (Taxonomy, Causality, Affordance, Analogy, Physics) ↓ Inference Engine ↓ Language Model Interface Concept Formation Unknown objects should begin as uncertain hypotheses. Example: Unknown object: “blicket” Initial state: Portable object: 80% Tool: 60% Decoration: 25% Food: 5% New observations update the concept. They are not fixed definitions but are evolving probability distributions. Analogical Reasoning A major capability is inference through similarity: “If A resembles B, and B has relationship X with C, then A may also have relationship X with C.” This supports reasoning about novel objects without requiring prior direct experience. Research Roadmap Phase 1: Create a small-scale concept graph under a single domain, such as household objects, animals, or tools Phase 2: Connect existing vision and language models to the graph. Phase 3: Develop automated concept updating to allow the model to update the graph as it discovers new information. Phase 4: Evaluate against human-like learning tasks such as novel word learning, unfamiliar object reasoning, causal inference, and analogy problems Conclusion The next advance in AI may not come solely from increasing model scale. It may come from giving artificial systems something analogous to a conceptual memory: a persistent, revisable, multimodal model of the world. The Dynamic Concept Graph provides a framework for combining the strengths of symbolic AI and neural AI into a system capable of learning not only patterns, but concepts. Note: I’m currently thinking through the best way to structure this graph… a sort of hybrid database/file directory system that allows for repeated concepts to stay consistent while individual concepts can stay connected, too: DynamicConceptGraph/ ├── Core/ │ ├── concepts.db (Nodes, categories, definitions) │ ├── relationships.db (Edges, predicates, confidence values) │ └── ontology.db (“Mammal implies…” inheritance rules) │ ├── Memory/ (Raw evidence and sources, including temporal data such as when facts became valid or changed) │ ├── observations/ │ │ 2026-07-07.json │ │ 2026-07-08.json │ │ │ ├── experiences/ │ └── conversations/ │ ├── Reasoning/ (Reasoning chains and derived conclusions) │ ├── inference_engine/ │ ├── reasoning_history/ (Including Bayesian probability updates) │ └── confidence_updates/ │ └── Concepts/ │ ├── Dax/ │ ├── summary.json │ └── semantic_genome.json (The compressed conceptual identity) │ ├── Blicket/ │ ├── summary.json │ └── semantic_genome.json Probably need some sort of uncertainties.json as well (Open questions, missing information, unresolved concepts) Earlier models required people to fill all these out manually… I feel like LLMs could now handle a lot of this work, and like a human student, we could then just give this new model a test. Since this new system wouldn’t simply be “auto-completing”, you could check to see if it learned concepts incorrectly and adjust them. Or am I totally off base here? submitted by /u/PrometheanPolymath
Originally posted by u/PrometheanPolymath on r/ArtificialInteligence