Beyond Hallucinations: The Illusion of Understanding in Large Language Models

As large language models (LLMs) become deeply integrated into daily life, from casual interactions to high-stakes decision-making, they inherit the ambiguity, biases, and lack of direct access to truth inherent in human language. While they generate coherent, fluent and emotionally compelling responses, they do so by predicting statistical word patterns rather than through grounded reasoning. This creates a risk of hallucinations, outputs that are linguistically fluent yet factually untrue. Building on Geoffrey Hinton’s observation that AI models human intuition rather than reasoning, this paper argues that LLMs represent human System 1 cognition scaled up—fast, associative, and persuasive, but lacking reflection and self-correction. To address this, we introduce the Rose-Frame, a three dimensional framework for diagnosing breakdowns in human-AI interaction. The three dimensions are: (i) Map vs. Territory, which distinguishes representations of reality (epistemology) from reality itself (ontology); (ii) Intuition vs. Reason, drawing on dual-process theory to separate fast, emotional judgments from slow, reflective thinking; and (iii) Conflict vs. Confirmation, which examines whether ideas are critically tested through disagreement or simply reinforced through mutual validation. Each dimension captures a distinct failure mode. Even one trap can distort understanding, but when multiple traps occur together, their effects compound, leading to runaway misinterpretations and epistemic drift. This makes it essential to evaluate all three simultaneously. We demonstrate the application of Rose- Frame through examples in which human and AI reasoning become entangled, resulting in escalating misunderstanding. By tracing how these failures emerge and interact, the framework moves beyond theory to operational practice, showing how misalignments can be detected and corrected. Rose-Frame does not attempt to “fix” LLMs with more data or rules. Instead, it offers a reflective tool that makes both the model’s limitations and the user’s assumptions visible, enabling more transparent and critically aware AI deployment. It reframes alignment as cognitive governance: intuition, whether human or artificial, must remain governed by human reason. Only by embedding reflective, falsifiable oversight can we align machine fluency with human understanding.

This ambiguity is not just a problem for machines; it also affects human cognition. People routinely exaggerate, reinterpret, or distort their own stories, sometimes for rhetorical effect, sometimes unconsciously. In some cases, such distortions are obvious; in others, they may be deeply internalized and hard to detect even by the speaker11. This makes the task of designing a language-based artificial intelligence that never hallucinates especially difficult, because it is trained on, and replicates, a medium (human language) that is itself prone to distortion, inconsistency, and subjectivity12. Understanding why AI systems reproduce these distortions requires examining the kind of intelligence they emulate: not human reasoning, but human intuition

Rose-Frame – Identifying Cognitive Challenges

This paper develops a framework to diagnose where misunderstandings arise between humans and large language models (LLMs). Rose-Frame identifies points where AI outputs diverge from user expectations or from reality itself, focusing on both machine errors and human misinterpretation, including hallucinations and false interpretations.

At the heart of science lies ontology, the study of what exists. Humans can never fully grasp reality; our understanding is always partial, limited by the language and concepts we use13. To approach ontology, we construct epistemology, knowledge systems that aim to describe reality as accurately as possible. These are provisional, constantly refined as science progresses13.

Rose-Frame (Realistic Ontology, Strong Epistemology) builds on this distinction. “Realistic Ontology” means best-effort models, never the ultimate truth. “Strong Epistemology” means science-based reasoning that is open to correction. The goal is not final answers but clarity: a map without confusing it for the territory14

Human cognition rarely aligns with this scientific ideal. Mental shortcuts, useful for survival, distort understanding15. We tend to believe that 1) opinions are facts, 2) that our decisions are based on careful reasoning when they are often driven by intuitive gut feelings, and 3) that being confirmed by others is the same as being correct. These cognitive traps are woven into all human communication, books, articles, conversations, and data. Since large language models are trained entirely on this human generated output, they inevitably inherit these same errors. AI does not just replicate our knowledge; it amplifies our cognitive biases, scaling our misunderstandings alongside our insights.

Cognitive Trap 1: Mistaking the Map for the Territory

The first trap is confusing models of reality with reality itself. In LLMs, outputs may sound true but are only statistical patterns of language. Korzybski’s map–territory14 distinction makes this clear: a map (epistemology) reflects perspective, but it is not the territory (ontology). When users treat fluent answers as ontologically true rather than probabilistic guesses, illusions arise. Avoiding this requires constant questioning: is this fact or belief, description or interpretation?

Cognitive Trap 2: Mistaking Fast Intuition for Grounded Reason

Kahneman’s dual-process theory15 distinguishes fast, intuitive System 1 from slow, analytical System

2. Intuition enables quick judgments, while reasoning allows deliberate problem-solving16,17. Both are essential, but people often mistake gut feelings for careful reasoning. This creates misplaced confidence, for example, believing an LLM “understands” because its answers feel fluent, as in the case of the Google engineer convinced the AI was conscious18.

By mapping user responses and AI interpretations onto these dual process dimensions, we can begin to understand whether a miscommunication results from incorrect snap judgments, failures of deep reasoning, or a mismatch between the AI’s linguistic fluency and the user’s reflective capacity.

Cognitive Trap 3: Being Confirmed is Not Being Correct - Conflict vs. Confirmation

The third trap is confusing agreement with truth. Human evolution favoured social cohesion, making confirmation bias and acquiescence a default19–21. Yet science advances through falsification and constructive disagreement, as emphasised by Socrates and Popper22, and by Korzybski’s call to separate the map from the territory14.

Rose-Frame and Its Application

Rose-Frame provides a lens for analysing AI–human interaction by examining not only AI outputs but also the user’s interpretive stance and cognitive biases. LLMs produce text that is coherent and persuasive26, yet this fluency can create an illusion of understanding27. Rhetorical plausibility often triggers intuitive, System 1-style acceptance28, leading users to treat probabilistic guesses as facts.

Because LLMs optimise for linguistic plausibility rather than truth, their outputs are epistemological maps rather than ontological descriptions29. When this distinction is lost, polished language conceals the absence of grounding, producing confident but fabricated statements. Compounding this, LLMs tend to favour confirmation over conflict30, reinforcing user assumptions and creating feedback loops of false confirmation4. Science relies on falsification, yet both humans and models are biased toward agreement, heightening the risk of undetected error31.

Rose-Frame addresses these challenges by mapping three dimensions: ontology vs. epistemology, intuition vs. reasoning, and conflict vs. confirmation. Rather than aiming to eliminate hallucinations— which may be impossible—it focuses on diagnosing when and why they occur, and on detecting them early to limit their impact.

Our goal is therefore not to eliminate hallucinations, but to diagnose why they happen and to prevent their amplification. Rose-Frame reframes the question from “what does the AI know?” to “how do we interpret what it says, and why?” By integrating ontology vs. epistemology, fast vs. slow cognition, and conflict vs. confirmation, the framework offers a practical lens for aligning human–AI interaction—not by altering algorithms, but by improving interpretation.

Ultimately, our aim is to assist LLM operators, designers, and users in recognising patterns of failure, not only in the outputs themselves, but in how those outputs are processed, trusted, and acted upon. Rose-Frame does not attempt to “fix” hallucinations through stricter code or rules. Rather, it enables a shift in perspective: from asking “what does the AI know?” to asking “how do we interpret what it says, and why?” In this sense, it re-centres the human in the AI loop, not as a passive consumer of machine intelligence, but as a critical interpreter embedded in an evolving ecology of meaning. In doing so, it reinstates human System 2 reasoning as the governor of scaled System 1 intuition—ensuring that coherence is tested against truth, not mistaken for it.

By integrating these ancient philosophical dimensions— epistemology and ontology, between fast and slow cognition, and confirmation and conflict—Rose-Frame offers a renewed way of thinking about thinking itself. Not a revolution in algorithms, but a reflection on interpretation —and a reminder that progress in AI depends not on smarter machines alone, but on wiser governance.