From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data

arXiv:2606.07537 · cs.CL, cs.AI, cs.LG · Submitted 2026-04-29 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data".

Jane: The paper was written by Md. Rejaul Korim Sadi, Toufiqur Rahman Tasin and Golam Mostofa Naeem from Department of Computer Science and Engineering, Metropolitan University.

Tom: Stay tuned as we take you through the paper and discuss its implications.

The Core Findings: Tom: So, after seeing how these three mechanisms work together in "From Architecture to Output," what does this paper suggest we should understand about our current approaches to LLMs?

Jane: The authors propose a major conceptual shift by mapping the specific output categories in existing taxonomies—things like intrinsic or extrinsic failure—to the actual underlying architectural mechanism that produces them. This is a huge leap from previous work.

Lu: It’s not enough for us to just know if an output is extrinsic; we need to know *why* it's extrinsic, and that's traced back directly to the Maximum Likelihood Estimation or MLE objective. That provides so much more specificity than any generic description allows.

Meng: This suggests a new diagnostic level where we can target interventions at the machine level instead of just managing the symptoms at the inference stage. We can pinpoint exactly where the failure occurred in terms operational mechanics and design flaws.

Lalam: If I understood this correctly, we are moving from a system where we are just hoping for better outputs, to one where we know exactly which structural lever to pull when an error appears. That gives us a clear path toward understanding how AI operates at all its levels.

Tom: Exactly, Lalam. The authors emphasize that current frameworks only describe the symptom of hallucination; they don't explain the cause of these failures in a way that is useful for real engineering change.

Jane: And by linking intrinsic failures to self-attention and logical inconsistency to autoregressive decoding, they provide a much more granular view than just saying "it's a general hallucination." It’s specific and traceable.

Lu: It’s giving us a roadmap for targeted intervention based on these specific mechanism failure modes, which is something that has been entirely missing from the research. We finally have the tools to see the internal workings of AI systems with this level of detail.

Meng: I wonder how practical it is to build tools that can identify these specific operational failures inside the complex structure of the transformer architecture. We need a way to monitor those internal circuits accurately to validate this paper’s claims.

Lalam: The idea of moving beyond merely fixing the symptom is incredibly exciting; we are talking about addressing the structural root causes of AI behavior itself, which is a huge step forward for accountability in how we deploy these tools.

Tom: That leads us into Segment three where we will discuss the three pillars of failure and how they tie together in this paper’s core findings.

The Three Pillars of Failure: Jane: We’ve established that the paper identifies three distinct architectural pillars—self-attention, MLE, and autoregressive decoding—and now I want to talk about what these specific failures mean for the practical application of LLMs.

Tom: That's a massive improvement over just describing the symptom; it tells us *how* the failure is happening, not just *that* it happened. The authors provide a clear breakdown of each pillar’s structural vulnerability.

Lu: For example, self-attention learns based on statistical co-occurrence, not semantic meaning. When that learned association fires in a context where it doesn't belong, we get intrinsic hallucination—the model is confidently misattributing facts from one scenario to another.

Meng: That’s a critical distinction; the model isn't reasoning incorrectly, it’s just applying statistical habits that don't hold up. We need to design systems that can detect when those learned patterns are being over-applied.

Lalam: It means we can no longer just blame the data for these issues; we have a specific mechanism to pinpoint the failure. This allows us to understand the inherent limits of current AI design and push back against them constructively.

Tom: Exactly, Lalam. The authors show that MLE trains the model to maximize next-token probability without any constraint on factual accuracy, which is why it produces those imitative falsehoods.

Jane: And by linking logical inconsistency to autoregressive decoding, we can also see how a single wrong token cascades forward into a coherent but factually broken sequence.

Lu: It’s giving us the specific vocabulary to talk about these failures—we can discuss "self-attention driven entity confusion" instead of just saying "the model made a mistake."

Meng: The engineering challenge is figuring out how to measure that specific failure mode in practice, not just assuming it exists. We need ways to observe those internal weights and probabilities.

Lalam: Addressing the structural root causes is exciting because it means we are building toward AI with greater integrity, recognizing its limitations while working to improve its reliability.

Tom: This deep dive into the mechanics leads us to Segment four where we will look at how these distinct failures tie together in this paper’s unified system.

The Unified System: Jane: We have seen the three pillars of failure, and now I want to talk about how this research suggests they are not working in isolation.

Tom: That's a huge realization—the paper clearly demonstrates that these three architectural components—self-attention, MLE, and autoregressive decoding—are not just failing independently in isolation. They are interdependent.

Lu: They are operating as a unified compound failure system where the weaknesses of each mechanism feed into and amplify the others over time. It’s not one mistake; it’s a whole chain reaction of design choices that makes the failures much more persistent than we thought.

Meng: And the findings from their empirical tests on GPT-two really show that even a relatively small model exhibits these structural flaws when we control the inputs, confirming its universality across scale is impressive and worrying for us in deployment.

Lalam: It feels like this is a definitive statement that LLM failures are architectural, not just data problems, and it's incredibly important for how we view the potential of AI systems.

Tom: It’s a powerful argument because, as the authors show, dataset pathologies like long-tail deficiencies are merely amplifying these inherent vulnerabilities; they aren't the primary drivers of this systemic failure.

Jane: This structural view allows us to see how issues like training bias are simply exploiting a pre-existing vulnerability within the machine design. The machine is ready for the data to cause trouble, and it's ready to capitalize on it.

Lu: The whole concept is that it's not an anomaly; it's what the design dictates when those learned patterns don't hold up against real-world context, which is a very different kind of failure than something breaking down randomly.

Meng: I hope this framework helps guide future development and tells us where to focus our engineering efforts for better reliability in deployment by pointing out these specific structural weak points.

Lalam: It suggests that we need to move toward building more robust, less brittle systems, recognizing the limitations of current architectures while striving for a greater degree reliability.

Conclusion: Jane: As we wrap up our discussion on "From Architecture to Output," it really clarifies that we aren't dealing with a single type of error, but three distinct failure modes that have been structurally mapped onto the architecture.

Tom: The paper has provided a huge diagnostic tool for the field, showing us exactly *how* and *why* these failures are happening, which is far more valuable than just knowing they exist.

Lu: I think the next phase of research is in finding ways to observe these specific mechanisms at runtime and measure their function against actual ground truth. We need visibility into those internal circuits to see them in action.

Meng: As we look at this from a practical standpoint, I’m looking forward to figuring out how we can build safeguards into the autoregressive process that actually allow for revision when a wrong token is committed. That's where the real engineering challenge lies in making these systems safer.

Lalam: My hope is that this leads to a culture of greater accountability in AI design, recognizing the inherent limits of what's possible with current architectures and demanding more transparency in how they operate.

Tom: It’s definitely not just about fixing errors; it’ about understanding *why* we make those structural mistakes in the way we build these models. That deep understanding is our biggest lesson today.

Jane: We appreciate you all joining us today as we share this paper, "From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data."

Lu: I'm excited to see how this shifts our understanding from the possibilities, knowing we have a map of the internal mechanics now.

Meng: I'm ready to start building systems that address these specific operational vulnerabilities identified in this research.

Lalam: It feels like we are moving towards a more responsible way of interacting with AI, using this structural knowledge to guide our development and ensure greater accuracy.

Department of Computer Science and Engineering, Metropolitan University

cs.CL, cs.AI, cs.LG

Submitted: 2026-04-29

Updated: 2026-09-04

Comments: 24 pages, 6 figures, 1 appendix

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 88/100

The gist: The persistence of hallucination—the production of "fluent, confident, factually wrong outputs"—remains a critical challenge in large language models (LLMs).

Key concepts

Pillars of Failure
The paper identifies three core architectural components—self-attention, Maximum Likelihood Estimation (MLE), and autoregressive decoding—as the primary sources of LLM failure. These mechanisms work together in a unified system where their individual weaknesses feed into and amplify each other, leading to persistent errors.
Self-Attention
This mechanism learns based on statistical co-occurrence rather than true semantic meaning. When these learned associations fire in an incorrect context, it results in 'intrinsic hallucination,' where the model confidently misattributes facts from one scenario to another.
Autoregressive Decoding
This process is linked to logical inconsistency. A single wrong token generated by this mechanism can cascade forward, resulting in a sequence that appears coherent but is factually incorrect.

Terminology

Summary

The persistence of hallucination—the production of fluent, confident, factually wrong outputs—remains a critical challenge in large language models (LLMs). While existing taxonomies classify these failures by their output characteristics, this paper argues that such descriptions are insufficient. It provides a structural analysis of hallucination as a compound failure system, identifying three specific architectural decisions that together create the conditions for error regardless of dataset quality or model scale.

The Three Architectural Mechanisms

The paper identifies three distinct architectural mechanisms that form the structural origin of hallucination:

  1. Self-attention, which substitutes statistical co-occurrence for semantic meaning, leading to entity confusion and fact misattribution.

  2. The maximum likelihood estimation (MLE) training objective, which optimises next-token probability without factual constraint, rewarding statistical plausibility regardless of truth value.

  3. Autoregressive decoding, whose structural vulnerability is exposure bias, resulting in a cascade where a single wrong token propagates forward through the entire output sequence without revision.

These mechanisms do not operate independently; they form a unified system where attention produces the wrong associative context, MLE trains the model to reproduce statistically frequent patterns from that context, and autoregressive decoding ensures that once a wrong pattern is committed, the entire subsequent output follows it.

How Self-Attention Causes Intrinsic Failure

Self-attention is fundamentally designed to learn statistical relationships rather than causal truth. It computes token relationships based on statistical co-occurrence patterns in training data. The implicit assumption here—that statistical proximity approximates semantic relationship—is often false. When this learned association does not hold in a new context, the model applies the pattern regardless of meaning. This structural property is the origin of intrinsic hallucination, manifesting as entity confusion or semantic drift.

How MLE Causes Extrinsic Failure

The maximum likelihood estimation (MLE) training objective trains a language model to maximize next-token probability across its corpus without any constraint on factual accuracy. The objective rewards statistical frequency; truth is not the objective. Because this mechanism does not distinguish between a common falsehood and a common truth, it allows the model to reproduce statistically frequent misconceptions with the same confidence as verified facts. This failure mode maps to extrinsic hallucination—outputs that are unverifiable against external source of truth.

How Autoregressive Decoding Causes Logical Inconsistency

Autoregressive decoding’s vulnerability is its permanent left-to-right commitment. During training, the model is fed ground-truth tokens, but at inference, it must condition on its own outputs. This creates exposure bias: the model never encounters its own errors. If a wrong token is generated at inference, it becomes the input for all subsequent predictions. The resulting cascade leads to logical inconsistency—an output that is internally coherent but factually broken from the point of the first committed error onward.

How Data Amplifies Architectural Vulnerabilities

The paper argues that dataset pathologies—including long-tail deficiencies, training bias, and synthetic pollution—do not independently cause hallucination. Instead, they amplify these vulnerabilities. For instance, long-tail deficiency exploits the MLE objective by under-representing rare truths; training bias exploits self-attention by reinforcing skewed co-occurrence patterns; and synthetic pollution feeds the autoregressive cascade with data that was already produced by previous model errors. The architecture is the necessary condition, and the dataset is merely the amplifier.

Improvements for AI systems

The core finding is that hallucination is a structural failure resulting from three distinct architectural mechanisms: Self-Attention (statistical substitution), Maximum Likelihood Estimation (lack of factual constraint), and Autoregressive Decoding (exposure bias/cascade). Mitigating this requires moving beyond descriptive output taxonomies toward mechanism-level interventions.

(Targeting Intrinsic Hallucination: Entity Confusion, Fact Misattribution)

Improvement: Implement a Semantic Grounding Constraint (SGC) layer integrated into the attention computation (QK). This constraint requires that the learned co-occurrence weights not only reflect statistical frequency but also satisfy a high semantic similarity threshold derived from an external knowledge graph or embedding space.

  • Mechanism: Before applying softmax(QK / sqrt d k,), a penalty term is introduced to the attention weight calculation if the semantic distance between Query and Key vectors exceeds a predefined threshold, even if statistical co-occurrence is high.

  • What the Improved System Can Do: The system will resist firing learned associations (e.g., associating Sylhet with tea) in contexts where semantic grounding does not hold, thus preventing factual misattribution and ensuring that entity relationships are semantically valid, not just statistically plausible.

(Targeting Extrinsic Hallucination: Imitative Falsehood)

(Targeting Logical Inconsistency: Cascade Failure)

(Addressing Long-Tail Deficiency, Training Bias, and Synthetic Pollution)

Abstract

Large language models produce fluent, confident, factually wrong output. Existing taxonomies classify these failures by output type -- intrinsic versus extrinsic, faithfulness versus factuality -- but say nothing about which computational component produced a given failure. We ask what would be required to attribute an individual hallucination to a specific component of the decoder-only stack. We treat three components -- self-attention's associative retrieval, the maximum-likelihood pretraining objective, and autoregressive commitment under exposure bias -- as candidate failure surfaces, justify their separability rather than assuming it, and specify an attribution procedure requiring only sampling access: an ordered set of three interventions on prefix, context, and frequency competition, together with a validation design based on independent annotation and a classifier baseline. We state five falsifiable predictions and identify competing accounts each would discriminate against. We analyse how instruction tuning, RLHF, DPO, retrieval augmentation, scale, and calibration bear on the argument. We execute a direct, pre-registered test of the commitment prediction (P3) across three model families: substituting a correct continuation at the point of divergence reduces downstream failing claims by 46.7 percentage points relative to baseline (p<10-9). However, a wrong-fact substitution reduces errors at a statistically indistinguishable rate, and the model answers correctly in isolation on only 2.2% of items where substitution succeeded -- a genuine partial result rather than a confirmation. Dataset pathologies amplify each component without originating failure independently, supporting an asymmetric-dependence claim: components are necessary intermediaries for data-induced failure, but data defects are not necessary for component-induced failure.

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