arxiv: 2604.10556 · v1 · submitted 2026-04-12 · 💻 cs.CL

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Lost in Diffusion: Uncovering Hallucination Patterns and Failure Modes in Diffusion Large Language Models

Fei Tan, Zhengnan Guo

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:24 UTC · model grok-4.3

classification 💻 cs.CL

keywords diffusion language modelshallucination patternsautoregressive modelsfailure modesnon-autoregressive generationinference dynamicsmodel reliability

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The pith

Diffusion large language models hallucinate more than autoregressive ones even when architecture, scale, and pre-training weights match.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to measure hallucination rates in diffusion-based large language models against autoregressive models under matched conditions. It shows that diffusion models produce more hallucinations and exhibit different responses to added inference compute, with non-sequential decoding supporting ongoing corrections while quasi-autoregressive paths saturate early. The work also isolates failure modes that arise only in the diffusion process. These patterns matter because they limit how much the performance gap on ordinary tasks can translate into trustworthy output. The authors conclude that the diffusion mechanism itself introduces reliability problems that standard scaling does not automatically solve.

Core claim

Current dLLMs exhibit a higher propensity for hallucination than AR counterparts controlled for architecture, scale, and pre-training weights. An analysis of inference-time compute reveals divergent dynamics: while quasi-autoregressive generation suffers from early saturation, non-sequential decoding unlocks potential for continuous refinement. Distinct failure modes unique to the diffusion process include premature termination, incomplete denoising, and context intrusion.

What carries the argument

The diffusion generation process, which replaces sequential token prediction with iterative denoising and enables non-autoregressive decoding but produces its own hallucination patterns and failure modes.

If this is right

Higher hallucination rates in dLLMs persist even after matching other model factors, so reliability fixes must target the diffusion mechanism itself.
Quasi-autoregressive decoding in diffusion models reaches performance limits quickly with added compute.
Full non-sequential decoding supports continued improvement when more inference steps are allowed.
Premature termination, incomplete denoising, and context intrusion are failure modes that appear only under diffusion and require dedicated countermeasures.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Model developers might add training penalties that discourage early stopping or incomplete noise removal to reduce diffusion-specific errors.
Evaluation suites for new generation paradigms could include targeted tests for context intrusion and incomplete denoising rather than relying on standard hallucination benchmarks alone.
Hybrid systems that switch between diffusion and autoregressive steps might combine the refinement benefits of one with the stability of the other.

Load-bearing premise

Any measured differences in hallucination rates can be attributed to the choice of generation paradigm because the compared models are otherwise equivalent in architecture, scale, and pre-training.

What would settle it

An experiment that runs identical model weights through both diffusion-based and autoregressive decoding on the same prompts and finds no difference in hallucination rates.

Figures

Figures reproduced from arXiv: 2604.10556 by Fei Tan, Zhengnan Guo.

read the original abstract

While Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive paradigm comparable to autoregressive (AR) models, their faithfulness, specifically regarding hallucination, remains largely underexplored. To bridge this gap, we present the first controlled comparative study to evaluate hallucination patterns in dLLMs. Our results demonstrate that current dLLMs exhibit a higher propensity for hallucination than AR counterparts controlled for architecture, scale, and pre-training weights. Furthermore, an analysis of inference-time compute reveals divergent dynamics: while quasi-autoregressive generation suffers from early saturation, non-sequential decoding unlocks potential for continuous refinement. Finally, we identify distinct failure modes unique to the diffusion process, including premature termination, incomplete denoising, and context intrusion. Our findings underscore that although dLLMs have narrowed the performance gap on general tasks, their distinct hallucination mechanisms pose a critical challenge to model reliability. Our code is available at https://github.com/ZeroLoss-Lab/Lost-in-Diffusion

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

dLLMs appear to hallucinate more than matched AR models with some unique failure modes, but the training controls may not fully isolate the diffusion effect.

read the letter

Hey, this paper runs the first head-to-head on hallucination rates between diffusion LLMs and autoregressive ones. The headline result is that dLLMs show higher hallucination even after matching architecture, scale, and pre-training weights, plus they surface distinct failure modes such as premature termination, incomplete denoising, and context intrusion. The inference analysis is also new: quasi-autoregressive paths saturate early while non-sequential decoding keeps room for refinement as compute increases. They release code, which is straightforward and helpful. The work does a clean job laying out those failure modes and tying them to the diffusion process rather than just reporting aggregate scores. That part feels like a useful map for anyone trying to fix reliability in non-autoregressive setups. The soft spot is the control claim. Diffusion models usually train with noise schedules and denoising objectives that differ from standard AR pre-training, even when weights start similar. The abstract says they controlled for pre-training weights, but without explicit ablations or identical checkpoint details it is hard to rule out that the hallucination gap comes from those training differences instead of the generation paradigm itself. Metrics and dataset sizes are also thin in the summary, so the strength of the comparison is still unclear until the full experiments are checked. This is for people tracking emerging LLM paradigms or working on faithfulness fixes. A reader focused on dLLM development or hallucination patterns would pick up concrete failure cases to test against. It is worth sending to peer review so the controls and numbers can be tightened, but it is not ready as-is for strong claims about the diffusion mechanism.

Referee Report

2 major / 3 minor

Summary. The paper presents the first controlled comparative study of hallucination in Diffusion Large Language Models (dLLMs) versus autoregressive (AR) counterparts. It claims that dLLMs exhibit a higher propensity for hallucination even after controlling for architecture, scale, and pre-training weights; that inference-time compute shows divergent dynamics (early saturation in quasi-autoregressive generation versus potential for continuous refinement in non-sequential decoding); and that dLLMs have distinct failure modes including premature termination, incomplete denoising, and context intrusion. The work concludes that these mechanisms pose a reliability challenge despite dLLMs narrowing the gap on general tasks, and releases code for reproducibility.

Significance. If the controls for architecture, scale, and pre-training weights are shown to be rigorous and the attribution to the diffusion paradigm holds, the findings would be significant for non-autoregressive modeling research. They provide the first systematic evidence of hallucination patterns unique to dLLMs, highlight inference dynamics that differ from AR models, and catalog concrete failure modes that could guide mitigation strategies. The public code release is a strength that supports verification and extension.

major comments (2)

[Experimental Setup] Experimental Setup (likely §4 or §3.2): The central claim that observed hallucination differences can be attributed specifically to the diffusion generation mechanism rests on the assertion of controls for architecture, scale, and pre-training weights. The manuscript must explicitly document whether identical base checkpoints were used, whether noise schedules and denoising objectives were matched, and whether any additional adaptation steps were applied only to dLLMs; without an ablation or confirmation of equivalence on training details, the attribution is vulnerable to confounding and the comparative results cannot be interpreted as isolating the paradigm.
[Results] Results and Analysis sections (likely §5): The quantitative demonstration of higher hallucination propensity in dLLMs requires reporting of concrete metrics (e.g., hallucination rates with standard errors, dataset sizes, and statistical tests) together with the precise definition and detection method used for each failure mode (premature termination, incomplete denoising, context intrusion). The current description leaves the magnitude and reliability of the gap difficult to assess.

minor comments (3)

[Inference Analysis] Clarify the operational definition of 'quasi-autoregressive generation' when discussing inference-time compute dynamics, including how it differs from standard AR decoding in the dLLM setting.
[Figures] Ensure all figures comparing hallucination rates or failure-mode distributions include error bars, sample sizes, and explicit legends so that the divergent dynamics can be evaluated at a glance.
[Discussion] Add a brief discussion of how the identified failure modes relate to or differ from known hallucination patterns already documented in AR models, to strengthen the claim of uniqueness.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and have prepared revisions to strengthen the clarity and rigor of the manuscript.

read point-by-point responses

Referee: [Experimental Setup] Experimental Setup (likely §4 or §3.2): The central claim that observed hallucination differences can be attributed specifically to the diffusion generation mechanism rests on the assertion of controls for architecture, scale, and pre-training weights. The manuscript must explicitly document whether identical base checkpoints were used, whether noise schedules and denoising objectives were matched, and whether any additional adaptation steps were applied only to dLLMs; without an ablation or confirmation of equivalence on training details, the attribution is vulnerable to confounding and the comparative results cannot be interpreted as isolating the paradigm.

Authors: We appreciate the referee's emphasis on ensuring the controls are fully transparent. Our experiments used identical base checkpoints for the dLLM and AR models (same pre-training weights and scale), with noise schedules and denoising objectives aligned to standard practices for each paradigm and no exclusive adaptation steps applied to dLLMs. To address the concern directly, we will add an explicit subsection (and accompanying table) in the Experimental Setup documenting the precise checkpoints, noise schedules, objectives, and training configurations. This revision will make the isolation of the diffusion paradigm unambiguous. revision: yes
Referee: [Results] Results and Analysis sections (likely §5): The quantitative demonstration of higher hallucination propensity in dLLMs requires reporting of concrete metrics (e.g., hallucination rates with standard errors, dataset sizes, and statistical tests) together with the precise definition and detection method used for each failure mode (premature termination, incomplete denoising, context intrusion). The current description leaves the magnitude and reliability of the gap difficult to assess.

Authors: We agree that additional quantitative detail will improve interpretability. In the revised Results and Analysis sections we will report concrete hallucination rates accompanied by standard errors, the exact sizes of all evaluation datasets, and statistical tests (e.g., paired t-tests) assessing the significance of observed differences. We will also supply formal definitions and detection procedures for each failure mode: premature termination as generation halting before the target length according to a confidence threshold; incomplete denoising as residual noise exceeding a calibrated threshold in the final denoising step; and context intrusion as semantically unrelated content measured by embedding cosine similarity to the input. These will be presented with numerical tables, confidence intervals, and illustrative examples. revision: yes

Circularity Check

0 steps flagged

Empirical comparative study with no derivation chain or self-referential steps

full rationale

The paper is a controlled empirical evaluation of hallucination rates in dLLMs versus AR models, reporting experimental observations on failure modes and inference dynamics. No mathematical derivations, equations, fitted parameters, or ansatzes appear in the provided text or abstract. Claims rest on direct comparisons (architecture/scale/weights controlled) and qualitative analysis of generation processes rather than any reduction to inputs by construction, self-citation load-bearing, or renaming of known results. The skeptic concern about training-objective equivalence is a question of experimental validity, not circularity in the derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the validity of the controlled comparison between model families and on standard definitions of hallucination used in the field.

axioms (1)

domain assumption The dLLMs and AR models are controlled for architecture, scale, and pre-training weights
This control is invoked to isolate the effect of the diffusion generation process on hallucination rates.

pith-pipeline@v0.9.0 · 5475 in / 1130 out tokens · 56140 ms · 2026-05-10T15:24:01.929020+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

8 extracted references · 5 canonical work pages · 1 internal anchor

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As of now, there is no

for data generation and judgment, we stan- dardize our pipeline usingGemini-2.5-Flash(Co- manici et al., 2025). We selected Gemini-2.5-Flash for its superior reasoning capabilities and cost- efficiency in processing long-context verification tasks. This model serves three distinct roles: • Question Generator:Constructing dynamic, knowledge-seeking questio...

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