“Artificial-intelligence hallucination risk can arise from excessive internal coherence without sufficient external grounding.”

The most effective analytical synthesis defines hallucination as content which a system generates with certain semantic confidence but does not have sufficient backing from its corresponding evidence base or environmental context or operational task rules or original source information. … Intermediate explanation is internally coherent but not warranted

In flat semantic space, hallucination is the inevitable result of coherence without disclosure.

AI hallucination is a phenomenon where AI generates a convincing, contextually coherent but entirely fabricated response that is independent of the user’s input or previous context.

conventional large language models (LLMs) generate answers based solely on their internal knowledge, which increases the risk of hallucinations and factual inconsistencies.

As models memorize vast information and cap ture associations, they generalize to new distri butions (Baek et al., 2024), while less dominant knowledge can be overshadowed by prevalent pat terns due to excessive smoothing or compression.

One might characterize intrinsic hallucination as a generative distribution that maximizes coherence with prior tokens, while failing to ensure that the output reflects any externally valid or cross-checked knowledge.

Object hallucination critically undermines the reliability of Multimodal Large Language Models (MLLMs), often stemming from a fundamental failure in cognitive introspection—where models blindly trust linguistic priors over specific visual evidence.

Our analysis reveals the opposite: hallucinated objects can exhibit peaked attention due to contextual priors; and models often express high confidence because intermediate layers have already converged to an incorrect hypothesis. … Once the model latches onto a confounded hypothesis, it can propagate through subsequent layers, ultimately causing hallucination.

Despite substantial performance gains, LVLMs suffer from hallucinations due to limited training data, lack of * Equal contribution precise grounding, and over-reliance on language priors (Liu et al., 2024a) .

Thus, LLMs’ tendency to hallucinate can be traced to their optimization focus on linguistic fluency and coherence, rather than factual precision, especially when faced with queries outside of their training distribution or when internal knowledge conflicts arise (Augenstein et al. 2024).

Hallucinations could be perceived as credible due to their fluency, coherence, and authoritative tone (Zhang et al., 2023).

The distribution of language is initially learned from a corpus of training examples, which inevitably contains errors and half-truths. However, we show that even if the training data were error-free, the objectives optimized during language model training would lead to errors being generated. … Our analysis explains what types of errors should be expected after pretraining. To do this, we draw a connection to binary classification. … The analysis shows how pretraining directly contributes to errors. Furthermore, it shows that the same statistical factors contributing to errors in binary classification also cause language model errors.

Unlike perceptual experiences, confabulations represent erroneous reconstructions of information that are influenced by internal and external knowledge and the environment. … This aligns more closely with the behavior of LLMs, which generate outputs that are erroneous yet plausible based on their training data and context.

In the context of LLMs, hallucination arises from the model's reliance on probabilistic associations learned from large corpora rather than explicit knowledge of facts.

Taken together, hallucinations—whether human or AI—underscore the limitations of predictive inference without robust error-checks.

Without access to external verification tools, the model relies on linguistic patterns to generate plausible but entirely unverifiable content, marking a fundamental failure in factual grounding

This broad coverage enables exceptional coherence and fluency; however, it also increases the risk of inaccuracies.

Epistemic uncertainty is closely tied to hallucination in LLMs: when the model is forced to generate outputs in areas where it lacks sufficient knowledge, it is more likely to produce unsupported or fabricated content. … Recent studies show that LLMs tend to generate plausible but false answers when they are uncertain, rather than admitting their inability to provide valid outputs Kalai et al. (2025). Another line of evidence further suggests that low-confidence situations are more likely to produce hallucinations, as they generate answers regardless of a lack of knowledge or unstable reasoning.

a mismatch between the answer supported by the prompt and the answer favored by statistically salient latent associations … a high-frequency shortcut path receives greater posterior weight than the constraint-sensitive path required by the prompt … the model may instead rely on high-frequency shortcuts. These shortcuts bias the model toward inference paths that are statistically dominant but semantically incorrect

The proliferation of autonomous AI agents can amplify the impact of hallucinations. In a multi-agent or multi-hop workflow, a single hallucination can become a starting assumption for later steps, with each downstream agent treating the hallucination as a verified fact. … Agent A invents a falsehood. Agent B retrieves or reasons from that falsehood. Agent C summarizes B’s output. By the end, the system produces a polished answer that feels internally consistent even though the original premise was wrong.

Initially, next-word pretraining creates statistical pressure towards hallucination even with idealized error-free data: using learning theory 8, 9, we show that facts lacking repeated support in training data (such as one-off details) yield unavoidable errors, whereas recurring regularities (such as grammar) do not.

When benchmarking metrics scored only perceived coherence or helpfulness, teams’ trained models to score better on these measures, but with the side effect of wild hallucinations—entirely ungrounded references, links, and factual claims.

The results instead support a more precise interpretation: the CMC writes verbalized confidence conditioned on the model’s committed answer, while remaining largely insensitive to whether that answer is factually correct. That is, the model is confident in what it said, not in what is true.

The model encodes the correct information in its parameters but generates a contradictory output with high certainty. This reflects some misalignment between the model’s internal knowledge and its output behavior.

Hamzah and Teng [2026] frame hallucination as a failure of epistemic commitment control. … EBP governs whether commitment should occur; EFT provides the risk signal informing that decision. Together, they form a complete framework: EFT predicts relia bility, EBP enforces appropriate epistemic behavior.

In this work, we define and investigate a distinct type of hallucination, where a model can consistently answer a question correctly, but a seemingly trivial perturbation, which can happen in real-world settings, causes it to produce a hallucinated response with high certainty.

Another perspective on understanding hallucination causation is presented by (Mündler et al., 2023), who explores self-contradiction as a contributing factor.

Here, I would like to narrow down the problem of hallucination to cases where the model output is fabricated and not grounded by either the provided context or world knowledge.

Pattern Matching Over Factuality: The model prioritizes generating text that sounds coherent and grammatically correct, even if the underlying information is factually incorrect or nonsensical.

In the case of AI, these misinterpretations occur due to various factors, including overfitting, training data bias/inaccuracy and high model complexity.

Large language models (LLMs) often suffer from hallucinations, posing significant challenges for real-world applications. Confidence calibration, as an effective indicator of hallucination, is thus essential to enhance the trustworthiness of LLMs.

Generative AI tools can produce fabricated information that appears authentic—a problem widely known as “hallucination” (Generative AI Working Group, n.d.).

Reasoning hallucinations occur when the model follows a logical chain that seems internally consistent but is built on a flawed initial premise, leading to a conclusion that is wrong despite appearing well-supported.

Corollary 1: All computable LLMs cannot prevent themselves from hallucinating — LLMs cannot solely rely on internal mechanisms (self-correction, CoT prompting); external safeguards are essential.

Memory errors, recalling nonexistent events, can create self-reinforcing hallucination loops.