Rebuilding the Architecture for Semantically Grounded AI

What must change inside AI systems - not just philosophically, but structurally and infrastructurally - for them to internalize the principles of General Semantics

If the earlier piece outlined the theoretical foundation general semantics provides to AI, this revision digs into the practical: what must change inside AI systems - not just philosophically, but structurally and infrastructurally - for them to internalize those principles? This is no longer about whether AGI can exist. It is about whether we are designing for the right kind of intelligence.

Modern LLMs excel at intensional reasoning: rearranging symbols based on statistical context. But they do not recognize the referential hierarchy of those symbols. They have no sense of abstraction level, temporal relevance, or observational grounding. These are not peripheral issues. They are central flaws that prevent the emergence of robust, scalable, and accountable intelligence.

Transcoding: Beyond Tokenization

Current systems convert language into tokens - fragments of words treated as atomic units. This process ignores critical semantic cues. Each word carries context, specificity, temporal grounding, and abstraction level. A semantically grounded system would use a richer transcoding layer - a "semantic compiler" - that parses input for abstraction tier, timestamp, context index, and usage type (literal, metaphorical, belief-based). This metadata should flow through preprocessing, training, and runtime evaluation. Effectively, language becomes structured meaning, not just compressed symbol.

Emerging work in semantic parsing and compositional semantic representations is relevant here, such as research that maps utterances into logical forms (semantic parsing) and integrates them with distributional representations (Liang and Potts style) and frameworks like DisCoCat for compositional semantics. These efforts signal movement toward encoding meaning structure more explicitly.

Model Weighting: Learning What Deserves Attention

Models today learn via co-occurrence frequency. But in general semantics, epistemic reliability is determined by referential integrity, not frequency. Claims grounded in observed data and linked to credible provenance should carry more weight. Ambiguous or speculative content should bear higher uncertainty. This demands re-engineering of attention mechanisms and memory weighting: shift from co-occurrence scoring to extensional coherence scoring.

Some emerging neuro-symbolic systems and metamodel approaches already attempt this. For example, a metamodel that explicitly integrates symbol grounding, cumulative learning, and abstraction dynamics - drawing inspiration from Korzybski's general semantics - has been proposed and tested in multi-modal settings.

Validation and Grounding: Closing the Loop

LLMs today generate text fluently but do not validate outputs against the real world. Fluent but ungrounded outputs are inevitable when no extensional feedback exists. General semantics demands a loop: outputs must be checked against observational data, knowledge graphs, or sensor streams. Logic or ontological assertions must be tested for internal coherence.

Recent studies in semantic grounding - for instance, frameworks integrating semantic digital twins with LLMs for robotic planning - demonstrate the viability of grounding natural-language inference in environmental context and feedback loops. Work on embedded modal grounding in situated neurosymbolic agents also reinforces the importance of grounding to generalizable learning.

Infrastructure-wise, this requires connections to external APIs, real-time sensor data, timestamped and versioned semantic representations, and validation frameworks that score or flag outputs at inference time. The architecture would resemble a distributed epistemic operating system more than a flat transformer stack.

Reflexive Reasoning and Meta-Awareness

Chain-of-thought prompting simulates reasoning, but it does not evaluate its own logic. Absence of abstraction-level awareness or contradiction detection makes models vulnerable to drift and incoherence. A semantically structured model must be reflexive: able to evaluate if its reasoning maintains coherent abstraction levels, if metaphor and fact are blurred, or if inconsistencies exist.

This demands new meta-representational layers. During inference, each step must trace back to its semantic anchor (source, abstraction, type, timestamp). Internal evaluation graphs must resolve contradictions, detect abstraction drift, and require clarification when ambiguity arises. This implies slower yet more transparent inference, deeper graph evaluation, and semantic traceability.

Tiered Corpus and Structured Knowledge Base

Today's LLMs train on flat corpora where every chunk of text is equal. That flattens abstraction quality. A semantic system would use a tiered corpus: peer-reviewed observational data, anecdotal testimony, speculative commentary, creative metaphor. Representations should remember provenance and abstraction level.

Meta-research, including survey articles on symbol grounding and analyses of strategies to solve it, highlight the need to distinguish representational tiers and delimit between sensory experience and symbol-level data.

The Infrastructure for Meaning, Not Just Speed

What is described is not a feature add-on but a paradigm shift. It demands semantic compilers, slower and more structured inference, richer memory profiles, integration with validation systems, and corpus infrastructure that supports abstraction layering and provenance. As cognitive architectures like Soar or CoALA show, building intelligence means integrating symbolic and statistical systems with meta-awareness, not just scaling token prediction.

We don't need bigger language models. We need deeper ones: models that know when they are speaking symbolically, asserting beliefs, referencing data, or expressing uncertainty. Intelligence begins not with pattern matching, but with pattern accountability. That requires meaning - and it requires infrastructure built for sense, not just speed.