Why AI still struggles with meaning and how dialectics may solve it

While modern AI systems excel at pattern recognition, prediction, and generation, they often struggle to explain how they form concepts, revise beliefs, or align meanings across different contexts. This limitation has become increasingly visible as AI models are deployed in scientific discovery, policy analysis, and high-stakes decision-making, where stable and interpretable concepts matter as much as raw performance. A new theoretical study argues that this weakness stems from a missing foundation in how AI systems define and evolve concepts in the first place.

That argument is laid out in the study Dialectics for Artificial Intelligence, published as a research paper on arXiv. The work proposes a formal framework that treats concept formation not as a labeling problem or a static representation task, but as a dynamic, information-driven process rooted in experience, compression, and continual revision. By grounding concepts in algorithmic information theory, the study offers a unifying lens for understanding how artificial systems can develop, test, and communicate meanings over time.

Rethinking what a concept means for artificial intelligence

The study challenges a core assumption that underlies much of current AI development: the idea that concepts are fixed entities defined by labels, features, or external supervision. In many machine learning systems, concepts are inferred indirectly through optimization objectives, classification boundaries, or embedding spaces. While these approaches work well for narrow tasks, the paper argues they lack a principled account of what a concept actually is.

Instead, the study defines a concept as an information object that emerges from experience and remains fully determined by it. Drawing on algorithmic information theory, the author proposes that a valid concept must satisfy a condition called determination. In this framework, experience is decomposed into parts, and each part must be reconstructible from the others using short descriptions. If this reversibility fails, the concept is considered unstable or artificial.

This approach reframes concept formation as a structural property of information rather than a semantic annotation. Concepts are not assigned from outside but discovered through the internal organization of data. The paper introduces excess information as a key diagnostic tool. Excess information measures how much redundant or arbitrary structure is introduced when experience is split into multiple representations. Low excess information indicates a natural conceptual decomposition, while high excess information signals that a concept is forcing structure where none exists.

By grounding concepts in compression and reversibility, the study positions concept learning as an intrinsic process rather than an externally imposed one. This has direct implications for how AI systems generalize, explain decisions, and adapt to new information. Concepts that are tightly bound to experience can evolve without collapsing when conditions change, while poorly grounded concepts tend to fragment or fail under pressure.

Dialectics as a mechanism for concept evolution

The paper formalizes dialectics as a learning mechanism for artificial intelligence. In this context, dialectics does not refer to philosophical debate but to a structured process of competition and revision among concepts. As new information enters an AI system, existing concepts attempt to explain it using minimal additional description length. Concepts that compress new information efficiently gain relevance, while those that fail to do so lose explanatory power.

This competitive process leads to observable dynamics. Concepts may expand to cover new cases, contract when they overgeneralize, split into more precise sub-concepts, or merge with others when distinctions become unnecessary. Importantly, these changes are not driven by external labels or manual intervention. They arise naturally from the pressure to maintain low excess information while accommodating new experience.

The study shows that dialectical evolution provides a formal explanation for phenomena commonly observed in human reasoning and scientific progress. Scientific theories evolve not by accumulating facts but by reorganizing concepts to better compress observations. Similarly, everyday categories shift over time as new cases challenge existing boundaries. By encoding this process mathematically, the paper argues that AI systems can replicate this adaptive behavior without explicit symbolic rules.

This framework also reframes learning as an ongoing negotiation rather than a one-time optimization. Traditional machine learning often treats training as a finite process followed by deployment. In contrast, dialectical AI treats learning as continuous, with concepts constantly tested against incoming data. This approach aligns closely with real-world environments where conditions change, assumptions break, and new variables emerge unexpectedly.

The study further connects dialectics to existing AI techniques. Clustering, segmentation, representation learning, and even some neural-symbolic approaches are shown to implicitly rely on compression-based trade-offs similar to those described in the dialectical framework. However, without an explicit theory of concepts, these methods remain fragmented. The paper positions dialectics as a unifying principle that explains why these techniques work and how they can be integrated more coherently.

Communication, alignment, and the future of interpretable AI

The study addresses one of the most pressing challenges in artificial intelligence: concept alignment between agents. Whether in multi-agent systems, human–AI collaboration, or cross-domain knowledge transfer, successful interaction depends on shared understanding. Current approaches often rely on explicit definitions, ontologies, or large shared datasets, which are costly and brittle.

The paper proposes an alternative grounded in dialectical reconstruction. Instead of transmitting full concept definitions, agents can share small informational seeds that allow others to reconstruct the same concept through their own experience. This shifts the burden from communication to computation, mirroring how humans often align concepts through minimal cues when they share similar backgrounds.

This insight has significant implications for AI safety and interpretability. If concepts are grounded in reversible information structures, then explanations can focus on how a concept compresses experience rather than on opaque internal activations. This creates a path toward explanations that are both faithful to the model and meaningful to humans.

The framework also offers a new perspective on alignment and control. Many alignment problems arise when AI systems form concepts that diverge subtly from human ones, even when surface behavior appears correct. By measuring excess information and reversibility, dialectical AI provides tools to detect when concepts are drifting or becoming artificially constrained. This could support earlier intervention before misalignment leads to harmful outcomes.

The study does not claim to offer an immediate implementation roadmap. Instead, it positions dialectics as a foundational theory that can guide future algorithm design. The author emphasizes that existing AI systems already exhibit dialectical behavior in limited forms, but without explicit recognition. Formalizing these processes makes it possible to reason about them, compare approaches, and design systems that are robust by construction.