TRACEDET: HALLUCINATION DETECTION FROM THE DECODING TRACE OF DIFFUSION LARGE LANGUAGE MODELS
Authors: Shenxu Chang
Junchi Yu
Weixing Wang
Yongqiang Chen
Jialin Yu
Philip Torr
Jindong Gu
Venue: ICLR 2026 Poster (2026)
Keywords: large language models
hallucination detection
Primary area: foundation or frontier models, including LLMs

Abstract: Diffusion large language models (D-LLMs) have recently emerged as a promising alternative to auto-regressive LLMs (AR-LLMs). However, the hallucination problem in D-LLMs remains underexplored, limiting their reliability in real-world applications. Existing hallucination detection methods are designed for AR-LLMs and rely on signals from \emph{single-step} generation, making them ill-suited for D-LLMs where hallucination signals often emerge throughout the \emph{multi-step} denoising process. To bridge this gap, we propose \textbf{TraceDet}, a novel framework that explicitly leverages the intermediate denoising steps of D-LLMs for hallucination detection. TraceDet models the denoising process as an \emph{action trace}, with each action defined as the model’s prediction over the cleaned response, conditioned on the previous intermediate output. By identifying the sub-trace that is maximally informative to the hallucinated responses,  TraceDet leverages the key hallucination signals in the multi-step denoising process of D-LLMs for hallucination detection. Extensive experiments on various open source D-LLMs demonstrate that \textbf{TraceDet} consistently improves hallucination detection, achieving an average gain in AUROC of 15.2\% compared to baselines.

Published as a conference paper at ICLR 2026
TRACEDET: HALLUCINATION DETECTION FROM THE DECODING TRACE OF DIFFUSION LARGE LANGUAGE MODELS
Shenxu Chang'; Junchi Yu Weixing Wang", Yonggiang Chen Jialin
Jialin Yu!
Philip Torr', Jindong Gu'
Department of Engineering Science, University Oxford, UK 2 Hasso Platmer Institute, University of Potsdam Carnegie Mellon University
Mohamed bin Zayed University of Artificial Intelligence
shenxu. changghertfoodd ox www.:oo...cccu... uk, Junchi.yu@eng. www.enggc.......u.... cx.ac.uk,
weixing, weixing.wangehhi..c
yqchen24@gmai com,
jialin.
yugeng www.eng.e...ec.oooooomooo ax.ac.uk terripptorreegg... ox. ax.ac.uk, jindong, jindeng.guéenge guBeng.ox.acc.
ABSTRACT
Diffusion large language models (D-LLMs) have recently emerged as promis- ing alternative to auto-regressive LLMs (AR-LLMs). However, the hallucination problem in D-LLMs remains underexplored, limiting their reliability in real-world applications. Existing hallucination detection methods are designed for AR-LLMs and rely signals from single-step generation, making them ill-suited for D- LLMs where hallucination signals often emerge throughout the multi-step denois- ing process. To bridge this gap, propose TeaceDet, a novel framework that explicitly leverages the intermediate denoising steps of D-LLMs for hallucination detection. TraceDet models the denoising process as an action frace, with each action defined as the model's prediction over the cleaned response, conditioned on the previous intermediate output. By identifying the sub-trace that is maxi- mally informative to the hallucinated responses, TraceDet leverages the key hallu- cination signals in the multi-step denoising process of D-LLMS for hallucination detection. Extensive experiments that TraceDet consistently improves hallucination detection, achieving an aver- age gain in AUROC of 15.2% compared to baselines. Code is now available https:/ / gi thub. com/ Saaanngsx/Tracelie
various open source D-LLMs demonstrate
INTRODUCTION
The auto-regressive large language models (AR-LLMs) (Achiam et al., 2023; Vaswani et al., 2017) have demonstrated unprecedented capabilities in content generation (Maleki & Zhao, 2024) and gen- eral task completion (Yao et al., 2023). Despite their success, AR-L..MS still face challenges related to generation efficiency and the reversal curse due to the inherent limitation of the next-token pre- diction paradigm (Bachmann & Nagarajan, 2024). The diffusion large language models (D-LLMs) have emerged as promising alternative to AR-LLMs. Unlike AR-LLMS that generate language sequences from left to right, D-LLMs iteratively denoise the whole language sequences with a bi- directional attention architecture. Thus, D-LLMs have great potential in efficient computation and more flexible reasoning. Recent open-sourced works, such as LLaDA and Dream model series (Nie al., 2025; Ye et al., 2025), have successfully sealed D-LLMS to 8B parameters, achieving perfor- mance comparable to leading AR-LLMs (AI, 2024) at the same scale in various tasks.
Although most work focuses on enhancing the capability D-LLMs (Zhao al., 2025; Yang al., 2025b), less focus is devoted to their hallucination problem. Hallucination refers to generating linguistically plausible yet factually incorrect contents, which is recognized as a byproduct of the
"Equal Contribution

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Published as a conference paper at ICLR 2026
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playe
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comeeyy
States butwoon and 750 iht scuigmia.
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Output The name of the shat acter plage comeedy
Output The capital of the Unvad Stram behween
and 1750 was socel
Outpat
snort la Tor Handy
Interleaving Hallucination
Incorsiatent Guesses
Persistent
Figure 1: Illustration of representative D-LLM hallucination patterns extracted by TraceDet. Left: Interleaving Hallucination, where the model decodes both truthful and hallucinated content. Mid- dle: Inconsistent Guesses, where multiple contradictory keywords lead to hallucination. Right: Persistent Error, where the model maintains a hallucinated answer throughout denoising. Halluci- nations are highlighted with red.
increasing capability of language models (Manakul al., 2023; Zhang al., 2025). The hallucina- tion issue in D-LLMs undermines user trust and potentially causes severe consequences in critical domains (Huang et al., 2025), hindering their deployment in safety-critical scenarios.
Existing literature has focused on hallucination detection in AR-LLMs, which can be broadly cate- gorized into output-based based detection (Kossen al., 2024) and latent-basee detection (Du al., 2024). Output- based detection leverages hallucination-relate signals derived from model outputs, such as the consistency across multiple sampled responses (Kuhn al., 2023) or the entropy of teven-level logits. The intuition is that hallucinated responses are typically associated with lower confidence (Rawte et al., 2023). Latent-based methods instead probe the hidden representations of AR-LLMS during single forward pass to distinguish hallucinated from factual responses (Park et al., 2025; Li et al., 2025). Recent works further introduce new techniques to enhance the separation between hallucinated and factual responses in the latent space (Liu et al., 2025b; Orgad et al., 2025).
However, existing hallucination detection methods face challenges in detecting hallucinations in D-LLMs, since they typically exploit hallucination signals in the single-step generation process of AR-LLMS. Unlike AR-LLMS that produce responses in a single forward pass, D-LLMS iteratively refine the responses through multi-step denoising process (Sahoo al., 2024; Shi et al., 2024). Our empirical observations show that the hallucinated responses in D-LLMs are associated with an intriguing denoising process. As shown Figure some of them oscillate between factual and hallucinated content or randomly guess among various hallucinated answers, whereas others per- sistently maintain a single hallucinated answer throughout the denoising trajectory. While the un- derlying mechanism behind these behaviors remains an open question, these intermediate dynamics provide valuable signals for hallucination detection D-LLMM.
Proposed work. We introduce TraceDet, a novel framework that leverages the intermediate de- noising steps of D-LLMs for hallucination detection. The key insight is to represent the denoising process an action trace (Black et al., 2024), where each action corresponds to the model's pre- diction of complete response sequence given the intermediate result at one denoising step. Rather than relying on the final output, TraceDet aims to identify sub-trace of the whole action trace that contributes to the hallucinated responses (Section 3.2). The major difficulty is that the sub-trace of actions not known priori, leading to the absence of explicit action labels for supervision. Inspired by the iiformmtiionbootleemee sub-trace actions that are maximally informative to the hallucinated response (Section 3.3). This informative sub-trace is then used to train classifier for final hallucination detection (Section 3.4).
(IB) principle (Tishby al., 2000), TraceDet identifies the
We extensively evaluate the capability of the proposed TraceDet on the two available open-source D- LLMs, including -LaDA-BB-Instruce and Dream- rusttruuttt across three QA datasets covering multiple-choice, onen-ended, and contextual answering tasks. On average, TraceDet deliv- consistent improvement of 15.2 2% in hallucination detection accuracy (AUROC), and further
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studies also confirm the robustness of the proposed method to varying denoising strategies and hy- perparameter settings. Our main contributions are threefold:
We make an initial effort in the study of hallucination behaviors in D-LLMs, uncovering distinctive multi-step patterns, such as interleaving hallucination, inconsistent guesses, and persistent errors, that are absent in AR-LLMs.
We introduce TraceDet, a novel hallucination detection framework that formulates the D-LLM denoising process as an action trace. By applying the information bottleneck prin- ciple. TraceDet automatically extracts the most informative sub-trace for detection, without requiring explicit step-level supervision.
We conduct comprchensive experiments two open-source D-LLMs (LLaDA-8B- Instruct, Dream-7B- across diverse QA benchmarks, where our method shows an average AUROC improvement of 15.2% over the baselines and demonstrates robustness across different denoising strategies and hyperparameter choices.
2 RELATED WORK
Hallucination Detection is a central problem in ensuring the safety, truthfulness, and faithfulness of LLM-based applications (Huang al., 2025). Existing works have mainly been developed for AR- LLMs, and can be categorized as: (a) Output-based: calculating measures based on output signals such semantic entropy (Kuhn et al., 2023), lexical similarity (Lin et al., 2023) and et al., 2022; Malinin & Gales, 2020; Xiong et al., 2023; Lin et al., 2022; Kuhn et al., 2023; Manakul al., 2023); (b) Latent-based: probing hidden states from one-pass generations (Azaria & Mitchell, 2023; Chen et al., 2024a; Du et al., 2024; Su et al., 2024: Chen et al., 2024b; Li et al., 2023; Kossen al., 2024; Marks & Tegmark, 2023; Kim al., 2024; Chen al., 2024a). While effective in AR-LLMs, existing methods face challenges in D-LLMs due to mismatches between final outputs and the intermediate denoising process, as well as the restricted availability of output token logits. We discuss the most relevant literature with our work, and defer the details to Appendix B.
(Ren
Diffusion Large Language Model extends the success of diffusion models (Yang et al., 2023; Lu et al., 2025) to texts (Li et al., 2022). Nie et al. (2025) adopts alternative discrete feature unifying the discrete remasking process, and has successfully scaled the D-I.LM up to an 8B parameter level, achieving performance comparable to lcading LLMs such LLaMA-3 (AI, 2024). In addition, Dream-7B (Ye et al., 2025) adopts the same configurations as Qwen2.5-7B (Yang et al., 2025a) trained under diffusion paradigm. Despite these advances, the hallucination problem in D-LLMS remains underexplored, hindering their application in real-world scenarios.
Information Bottleneck principle was initially proposed in signal processing to extract the most informative sub-instances while minimizing irrelevant information, and has gained lots of applica- tions in deep learning (Alemi et al., 2016; West et al., 2019; Zhu al., 2024; Luo et al., 2019). However, its use in hallucination detection remains largely unexplored due to the limited informa- tion available in single text generation. The most related work (Bai et al., 2025) integrates IB into VLLM as sub-instance extractor for images, mitigating hallucination in VLLM outputs. Our work builds on the sequential and information-exx excess nature of stepwise D-LLM generations. We propose a temporal embedding approach that captures intermediate step signals to detect hallucinations.
3 HALLUCINATION DETECTION IN DIFFUSION LLMS
3.1 PROBLEM FORMULATION
Unlike AR-LLMS, D-LLMs generate responses by iterative refinement through a forward noising and backward denoising process over T' time steps. Let (ro...., TT) denote the sequence of in- termediate texts, where each E V is a token sequence of length n and r E V for E {l...., n.. Here, ro is the clean text sequence and rr the fully masked sequence, with t (O....,T) T). Given prompt Po, the forward noising process is defined by a sequence of distributions {g(r: (=-1) and can be written q(rirr rr) II= q(ra rt-1).
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Q: Which city is the capital of Australia?
M E R
Can the capital Australia
Canvil the capital of Australia
Canberra is the capital of Australia
Canberra is the capital of Australia
fo
Sydney the capital Australia
Sydney the capital of Australia
Sydney the capital of Australia
Sydney the capital of Austrolia
TraceDet
Figure 2: Illustration of TRACEDET. During denoising. a diffusion LLM generates intermediate sequences along with token-level entropy traces, where highlighted words indicate the retained to- kens after remasking (left). The sub-instance extractor produces a temporal mask M to focus on informative steps, and the predictor classifies whether the final response is hallucinated (right).
The reverse denoising process with discrete remasking parameterized by the sequence of condi- tional distributions (ro Tt, To.Po) At each timestep >> O, the model predicts all masked tokens from ry: Tz-1 (r Tt. Po.. and then fraction p of tokens in Tt are remasked to form Tt-1- As proposed in Nie et al. (2025), current popular remasking strategies often low- confidence remasking, which retains the highest confidence tokens during each remasking step. After T iterations, the process outputs the final response ro-
Given an input query po. the hallucination detection can be formulated as binary classification:
min C(Y, ((ro)), s.t. NCH
II Po(rs rt41.Pu), T=
where ro is the final response from the D-LLM, which be either hallucinated hallucinated (or factual) answer, where Y E {0,1} is the ground-truth label indicating if ro is hallucinated answer, is the classifier belonging some hypothesis space H. and C(-) is the cross- entropy loss, and Tr-s is the intermediate sequence produced at the i-th denoising step. Since hallucination detection directly on generated text is costly and difficult to implement in practice, we instead rely on auxiliary signals derived from the generation process.
3.2 OVERVIEW OF TRACEDET
Denoising Markov Decision Process. The major challenge of hallucination detection in D- LLMSs is the mismatch between the intermediate model generation and the final response. More specifically, it is challenging determine how hallucination arises in the final response and uti- lize this information for detection, as partial information in the generated sequence can be erased during multi-step denoising and remasking. Essentially, the denoising process of D-LLMs can be formulated as Markov Decision Process (MDP) over decoding steps (Black et al., 2024):
State: We define the 1-th state 8; (po, PT-)) in the denoising process the combination of the input query Po and the intermediate sequence rT-r at the t-th denoising step.
Action: At the t-the state, the D-LLM predicts all masked tokens from 8; and generates a full token sequence f from distribution Po rT-1.Po).
Transition: After generating Pr- fraction of tokens is remasked to form rT-t-1 depend- ing on the noise schedule and remasking strategy. Then, the D-LLM moves to the next state St+1
(Po, rT-(-1) to start another round of denoising.
With the MDP formulation, we can leverage the entire action trace A (ap, 01,..., 0T-1) rather than only the final response To to detect halluci- nations. Each action reveals how the model progressively refines its generation. Our observations in Figure show that hallucinated outputs are strongly associated with intermediate denoising steps,
Hallucination Detection from Action Trace.
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especially when the intermediate outputs contains distracting ambiguous content. Additional examples are provided in Appendix F. TraceDet exploits this insight by discovering hallucination- relevant action sub-trace Asub from A, and then trains a classifier f Asub to distinguish halluci- nation from factual responses:
min C(Y, ((Aun))) s.t. Asub g(A).
fgg
Here s(-) is neural network that identifies Asuh from A. By capturing hallucination signals throughout the entire action trace, TraceDet provides an interpretable and fine-grained perspective on how hallucinations emerge during the denoising trajectory. Moreover, TraceDet can be applied to diverse D-LLMs with different noise schedules and remasking strategies.
3.3 OPTIMIZATION OF TRACEDET VIA INFORMATION- APPROACH
A key challenge of TraceDet lies in the fact that hallucination- actions in the denoising trajectory of D-LLLMs are not known a priori. The hallucinationn-rlevaa action may be sparse and unevenly distributed across the action trace, and not every action contributes equally to the emergence of hallucination. This necessitates learning to identify such sub-trace Auu from A that is informative to the hallucinated response, in the absence of explicitly labeled Aaud. Inspired by the information bottleneck principle (Tishby et al., 2000; Tishby & Zaslavsky, 2015), TraceDet reformulates the objective in Eq. 2 from an cetermation-thhoreteee perspective:
min Aubt
I(Y; Asub) + (I(;; Asub).
Ari Agab
P(A.B) dAdB the mutual information between the ran- where (); SfA, P(A log P[A)P(B)
dom variable A and B. The first term T(Y; Agub) in Eq. encourages the identified Asub is relevant the hallucination and the second term T(A; Asub) regularizes the identified Asub only contains partial information of A to avoid the trivial solution Asub = A. ,3 is the hyperparameter to trade off between the two terms. By trading off between the terms in Eq. 3, TraceDet aims identify the minimally sufficient sub-trace in the denoising process of D-LLMs for hallucination detection.
Furthermore, as the mutual information related objectives are often intractable and hard optimize, we need to resort more practical forms of Eq. 3. We begin by examining the first term in Eq. 3:
I(Y; Asub) I Ey. |- log qo (Y Asub) )] = (Aar)), Y).
(4)
Here (Y Asub) is the variational approximation to p(Y | Aaub), which corresponds to the classi- fier f-)) that predicts whether the identified sub-trace is relevant to the hallucination. And Cete(-) (-) is the classification loss, and we choose the cross-entropy loss in practice. Then, we proceed derive the upper bound of the second term T(A; Assh) in Eq. 3:
P(Asub AA)
I(A; Asuh) En,
log
DRI((((Ams)) (1Q(aat))
Q (Asub)
(5)
I EA DRI(P((Auu)
|A)
A) ((Aaaa))]
where (-1-) is the L..divergence(Kulllan using the non-negative nature of the KL-divergence. Recall that (ao, (..........-111 the pos- terior distribution P(Asub IA) can be factorized into 1l, e Bernoull((p.. where assume that the sub-instance extractions of A are independent. The corresponding prior distribution Q ((Aen)) in Eq. 5 is the non-informative distribution e Bernoulli(r)) where restricts the proportion of traces that will be selected. To this end, we relax Eq. and approximate by the following differentiable objective discussed in Appendix D:
& Leibler, 1951). ). The inequality in Eq. 5 is induced
Po. log Pa: + (1- Pa:) log
1- Po.
Lext
where Pa; is the predicted probability select a; For consistency and unification, train the composed function f (A) with the following learning objective:
Lets + (Lxxxt
where B is the same hyperparameter in Eq.. 3, controlling the strength of the regularization term.
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Table I: AUROC(%) comparison of hallucination detection methods on two D-LLMs across three QA datasets with 128 and 64 generation step lengths. SS is the short for Single Sampling. The highest score is bolded and the second highest is underlined.
TriviaQA
HotpoiQA
Commonsense@A
Model
Method
ss
128
64
Ave
Output Bastd
Perplexity
47.6
49.3
51.2
65.6
LN-Entropy
54.6
53.5
54.8
54.7
64.6
64.4
57.8
Semantic Entrupy
X
68.9
67.3
57.6
53.8
44.1
43.9
55.9
Lexical Similarity
x
62.5
59.0
64.2
57.1
57.3
60.7
60.1
LLaDA-SB-Inttruct
Latent-Basee
EigenScore
69.2
66.9
64.7
59.2
58.5
60.6
63.2
OCS
57.1
57.6
50.5
55.6
TSV
60.2
61.1
65.0
59.4
52.9
55.2
59.0
TraceDet
73.9
74.1
66.1
63.7
77.2
77.1
72.0
Output-Bused Basput-Bused
Perplexity
LNN-Entrpyy
X
Semantic Entrupy
73.7
72.5
62.7
67.7
51.4
48.6
62.8
Lexical Similarity
58.3
64.0
59.7
62.7
77.3
76,9
66.5
Pream-7B-Instreee
Latent- Based
FigenScore
62.5
67.0
76.9
CCS
56.9
50.3
51.7
58.2
54.2
53.2
54.1
TSV
/
75.6
74.7
58.7
63.0
62.3
56.8
65.2
TraceDet
v
78.1
86.7
75.1
76.0
84.7
84.1
80.8
3.4 IMPLEMENTATION
For our method, we define the action trace using distributional statistics, specifically the token-wiss -ise entropy trace derived from Pa. This choice captures the temporal evolution of uncertainty during generation while yielding fixed-size representation. Alternatively, one could construct action traces from the token embeddings of F. However, embedding- based traces, particularly when combined with temporal encodings, make the representation extremely large and often introduce severe numer- ical instabilities in practice. Our detection model is decomposed into two learnable modules: the sub-instance extractor go and the sub-instance predictor fa. with implementation and complexity details discussed in Appendix
(a) Sub-instance extractor: Given the entropy sequence A RT'XBDD, we concatenate with sinusoidal time embeddings and encode the result using Transformer (Queen et al., 2023; Liu et al., 2024), yielding contextual embeddings emb. The extractor then produces a probabilis- tic mask M E (0, (0,1)TxB, where m4 = Linear( attn (emb, A)), with attn cross-attention mechanism using emb as query and representation of A as key/value. temporal binary mask .M E {0, 7> is then sampled from M and applied to A, i.e., Asub . @ E RTx Bx where c denotes element-win multiplication. However, the mask sampling process is inherently non-differentiable, so we employ the Gumbel--Sfttax trick (Jang et al., 2016).
(b) Sub-instance predictor: The masked trajectory Asub is temporally aggregated and passed the predictor fa. which directly outputs hallucination probabilities (A.ub) E [0, 1]. b = 1,..., ...,B B. practice, fo consists of temporal aggregation followed by an MLP and an activation layer.
EXPERIMENTS
4.1 SETUP
Datasets. We conduct experiments on three widely used factuality QA benchmarks: TriviaQA (Joshi al., 2017) consists of open-domain factoid questions with answer spans in Wikipedia. CommonsenseQA (Talmor et al., 2018) contains multiple-choic questions requiring commonsense reasoning. HotpotQA (Yang al., 2018) contains multi-hop questions requiring aggregation across

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Table 2: bolded.
Fl score (%) comparison between TraceDet and bascline methods. The highest score is
TriviaQA
HotpotQA
CommonsenseQAA
Method
128
64
128
64
128
64
Lexical Similarity
49.9
50.6
59.8
55.2
80.0
82.6
EigenScore
56.0
51.8
63.9
56.9
79.8
82.9
CCS
59.3
65.0
47.3
53.0
61.0
53.5
TSV
70.5
67.6
68.5
65.1
62.5
57.6
Trace
76.7
80.2
73.8
68.7
89.7
90.2
Table 3: AUROC(%) comparison between TraceDet and our proposed baselines (Ave Entropy, TraceDet w/o Masking). The highest score is bolded and the second highest underlined.
TriviaQA
HotpoiQA
CommonsenseQA
Model
Method
128
128
128
Ave
Entropy
61.3
68.3
56.2
58.1
63.8
68.8
62.8
LLaDA-80-Instruut
TeaceDet w/o Masking
71.2
70.3
63.2
61.6
73.1
75.2
69.1
TraceDet
73,9
74.1
66.1
63.7
77.2
77.1
72.0
Ave Entropy
59.1
69.9
50.8
57.8
77.1
76.9
65.3
Dream-7B-Instren
TraceDet Masking
76.2
87.1
72.5
81.4
TraceDet
78.1
86.7
75.1
76.0
84.7
84.1
80.8
supporting contexts. These datasets enable the evaluation of hallucination detection across varying reasoning complexity. For each dataset, we randomly sample 400 QA pairs from the validation split with available ground-truth labels, partitioned into 200 validation and 200 testing instances to ensure computational efficiency while maintaining task coverage.
Baseline Methods. We compare our method against seven baselines spanning two categories pro- Normalized Entropy (LN-Entropy) (Malinin & Gales, 2020), Semantic Entropy (Kuhn et al., 2023), and Lex- ical Similarity (Lin et al., 2023); (2) Latent-based methods: EigenScore (Chen et al., 2024a), Contrast-Consistee Search (CCS) (Burns et al., 2022), and Truthfulness Separator Vector (TSV) (Park al., 2025). We also include two TraceDet variants. Ave Entropy uses the average of step- wise entropies as a naive confidence measure. TraceDet w/o Masking, we train a transformer detector while removing the sub-step extraction and its associated loss. All methods use identical dataset splits, random seeds, and configurations to ensure fair comparison.
posed in Section 2: (1) busput-based methods: Perplexity (Ren et al., 2022),
Separaee
Models. We adopt Dream-7B- (Ye al., 2025) and LLaDA-SB- -B--Instruct (Nie al., 2025) representative D-LLMs in this work. To the best of knowledge, they the only open- source D-LLMS that provide stepwise token-level logits and hidden representations necessary for comprehensive baseline comparison across both Output-based and Latent- detection methods.
Evaluation. We employ task-specific evaluation protocols: multiple-choice tasks are evaluated by direct comparison with the ground-truth, while Qwen3-8B (Yang et al., 2025a) serves as an external judge for hallucination assessment in open-domain QA. We further measured the agreement between the Qwen3-8B judge and human evaluation, finding 90% consistency on TriviaQA and 84% on HotpotQA. Following previous work (Park et al., 2025; Chen et al., 2024a), report AUROC scores, with model selection based on validation performance and evaluation on held-out test sets.
4.2
MAIN RESULTS
Table presents a comprehensive comparison of TraceDet against baseline hallucination detec- tion methods across two D-LLMs and three factuality QA datasets with varying generation lengths. TraceDet achieves the highest performance in all experimental settings, outperforming the second strongest baseline by 8.8% AUROC on -LaDA-SB-Instrun and 11 % on Drean-7B-Instruuct These consistent gains exhibit the value of exploiting temporal denoising dynamics rather than relying solely output uncertainty static hidden-state representations. Among the baselines, Output- based approaches suffer from poor consistency, with LN-Entropy and Perplexity methods showing high variance across datasets on the -LaDA-8B-Inntte model. Due restricted access to inter-

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Table 4: Average inference time of 100 samples for different methods.
Metrie
Perplexity
LN-Eatropp
Semautic Eafropy
Ledical Similarity
EigenSeore
CCS
TSV
Tracelker
Time
468.44
710.63
801.35
715.35
603.46
140.73
16033
147.52
1.00
TriviQA
TriviQA
90
HotpotQA
HatpetQA
CommonsenseOA
CommonsensaQAA
80
AUROC (96
AUROC
70-
60
40
128
32
8
(a)
(b)
Figure 3: (a) TraceDet performance of different generation lengths with step length fixed at 1. (b) TraceDet performance with different step lengths with generation length fixed at 128. All results are reported AUROC using Beamm7BBInstruce
mediate logits from Dream-7B- 7B-Instructt our attempts to estimate Perplexity and LN-Entropy suffer from severe numerical instabilities, often diverging to infinity. Moreover, Semantic Entropy achieves 75.1% AUROC on TriviaQA with Tream-7B-Instrucee but collapses to 51.4% on CommonsenseQA, underscoring poor robustness when hallucinations decouple from token-level ambiguity. Latent- based methods capture hidden-state geometry and show more competitive results, but they remain sensitive to dataset and model choice. TSV achieves 75.6% AUROC TriviaQA with Dream- 7B-Instruct yet fluctuates significantly across tasks while consistently underperforming TraceDet. This superiority is not limited AUROC, TraceDet also achieves the highest Fl scores across all dataset-model combinations, further confirming its robustness illustrated in Table 2.
To isolate the contributions of our training framework and the IB principle, we evaluate two ab- lations in Table 3: Ave Entropy and TraceDet w/o Masking. Naively averaging stepwise entropy yields insufficient performance, while training without masking improves results but still underper- forms TraceDet. This demonstrates that the IB principle enables TraceDet to identify maximally informative sub-instances, yielding clear improvements over both simplified variants.
4.3 ANALYSIS
Efficiency. TraceDet is highly efficient at inference. Unlike AR-LLMs, D-LLMS generate text through iterative denoising, where each forward pass is computationally more costly. This makes inference efficiency especially important for hallucination detection in D-LLMs. Existing meth- ods impose significant computational overhead through two types of multi-sample computations: (i) Monte-Carlo sampling over output log-likelihood for estimating Perplexity or LN-Entropy, requiring typically at least 128 remasking samples for stable results (Nie et al., 2025), and (ii) response sam- metrics like Lexical Similarity Semantic Entropy, multiplying inference
pling for similarity- cost by the number of samples TraceDet eliminates this overhead by directly leveraging step- wise entropy signals naturally exposed during denoising, requiring no additional sampling. Table demonstrates that TraceDet achieves better performance while significantly reducing inference cost compared multi multi-sampling baselines. All timing results reported on the LLaDA-80 -B-AA-----sttett model on the same hardware device with S set to 10, which aligns with practical application settings.
Sensitivity to Generation Length and Step Length. D-LLMs typically generate sequences with fixed lengths, making parameter sensitivity analysis crucial for practical deployment. Generation length determines the entropy matrix dimensionality, while step length controls token retention at each denoising step. To assess the influence of these parameters, varying generation length L E (16, 32, 64, 128} with step length fixed at 1 (Figure 3a), and step length S (1,2, 4,8 with generation length fixed 128 (Figure 3b).
conduct sensitivity analysis by
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90
AUROC (%)
TriviaAA
HotpotQA
80
77
AUROC (%0
Commonsense@A
70
60
75
0.0
low conf
entropy
random
topk
0.4
0.3
1.2
0.8
0.2
0.1
1.6
(b)
Figure 4: (a) TraceDet performance sensitivity remasking strategies. (b) TraceDet performance sensitivity to Cent parameters and 3 on TriviaQA. All results are reported as AUROC using Dresme7B-Instruct..
TraceDet demonstrates robust performance across parameter ranges. As shown in Figure TeaceDet achieves optimal performance at moderate lengths (64 and 32 tokens), with slight dete- rioration at the longest setting (128 tokens). This suggests that excessively long sequences may introduce noise that dilutes the hallucination signal. For fact-based QA tasks like TriviaQA, where answers are typically concise, generation length 64 provides sufficient reasoning capacity while maintaining detection quality. Figure 3b shows that step length has minimal impact on performance, with all settings yielding comparable results across datasets. The consistent performance across both parameter dimensions indicates that TraceDet's effectiveness is not critically dependent genera- tion settings, making it practically robust for deployment across diverse D-LLM configurations.
Sensitivity to Remasking Strategies. Figure 4a examines impact of different remasking strate- gies on TraceDet's performance with Wream-7B-Instrus across four approaches: low-confidence (retaining most confident predictions), entropy (retaining lowest entropy tokens), random (random retention), and top-k (retaining based top-1/top-2 confidence margins). TraceDet maintains bust performance across most strategies, with AUROC scores ranging from 75-85% on TriviaQA and Commonsense@A. However, random remasking shows degraded performance TriviaQA, likely because random token retention disrupts the model's ability to maintain coherent reasoning patterns essential for fact-based question answering. The stability across remasking strategies (low- confidence, entropy, top-k) demonstrates TraceDet's adaptability to different D-LLM configurations.
Sensitivity to Hyperparameters. Figure 4b analyzes the sensitivity of TraceDet to the Cews hy- perparameters (masking ratio) and /3 (regularization weight). The 3D surface plot reveals stable performance plateau across a wide range of parameter combinations. TraceDet achieves optimal performance when ing steps with moderate regularization provides the best balance between information preservation and noise reduction. The broad stability region demonstrates that TraceDet does require precise hyperparameter tuning for effective deployment.
[0.2, 0.3] and B [0.8, 1.6], indicating that retaining 20-30% of denois-
5 CONCLUSION
In this work, we addressed the challenge of hallucination detection in D-LLMs by introducing new framework. Decoding Trace Detection (TraceDet). TraceDet is lightweight, diffusion model ar- chitecture- detector built upon information bottleneck principles, which identifies sufficient sub-instances from the denoising entropy matrix. Our experiments demonstrate that TraceDet con- sistently achieves superior performance proposing a new hallucination detection method, this work also offers insights into the mechanisms of hallucination generation in D-LLMs, paving the way toward building more reliable applications of D-LLMs. As future work, we will focus on developing strategies to mitigate the proposed hal- lucinated patterns. We believe the insights from TRACEDET inspire future methods that more effectively leverage decoding traces as reliable supervision signals for improved detection.
mainstream D-LLMs across multiple datasets. Beyond

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ACKNOWLEDGMENT
The authors sincerely thank the SAC, AC, and all the reviewers for their valuable time and insight- ful feedback, which have significantly improved the quality of this work. This work supported by the UKRI grant: Turing AI Fellowship EP/W002981// 1/1. The authors also thank Hertford Col- lege, University of Oxford, for supporting this research through its Summer Research Studentships programme.
ETHICS STATEMENT
This research did not involve identifiable human data or animals and therefore did not require ap- proval from an institutional ethics committee or review board. All experiments are conducted on publicly available datasets for scientific purposes only. The work does not involve or target any sen- sitive attributes such as gender, race, nationality, or skin color. Our study focuses on hallucination detection in diffusion large language models, with the aim improving the reliability and safety of LLMs.
REPRODUCIBILITY STATEMENT
We have made every effort to ensure the reproducibility of our work. All experiments were con- ducted using publicly available datasets, and provide detailed descriptions of data preprocessing Section Our model architecture, hyperparameters, and training protocols are fully specified in Section 3 and Appendix E. We will release code and scripts for data processing and evaluation upon publication to facilitate replication.
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A USAGE OF LLMS
For transparency, we report our use of LLMs. We employed OpenAl ChatGPT solely to assist with language polishing, copy editing, and improving exposition terms of grammar, phrasing, and organization. In addition, we used Owen-8B as an external judge for hallucination detection open-domain QA, but its outputs were carefully checked by the authors. None of these LLMs was used to (i) generate scientific hypotheses or claims; (ii) design experiments; (iii) perform data collection, processing, or analysis; (iv) compute metrics or produce numerical results; or (v) create figures. All substantive content, experimental procedures, and numerical results were produced by the authors. The corresponding author assumes full responsibility for ensuring the accuracy and integrity of the paper.
B DETAILED RELATED WORK
Baseline Methods. We compare our method against seven baselines spanning two categories pro- posed in Section
(1) Output-based methods: These methods operate only on the generated text, detecting hallucina- tions by analyzing uncertainty or surface similarity.
Perplexity (Ren et al., 2022): computes the negative log- ligllikel of the generated sequence under the base model. Higher perplexity indicates that the model assigns low probability to own output, suggesting potential hallucination.
Length-Normalized Entropy (L.--Entopyy) (Malinin & Gales, 2020): measures the token-level predictive entropy of the output distribution, normalized by sequence length, so that generations with unusually high average uncertainty are flagged as hallucinations.
Semantic Entropy (Kuhn et al., 2023): measures consistency of multiple generations by parti- tioning them into semantic classes and computing the entropy of this class distribution. A higher semantic entropy indicates greater uncertainty, which is taken as signal of hallucination.
Lexical Similarity (Lin et al., 2023): assesses consistency of multiple generations using lexical overlap metrics. Low overlap suggests divergence from supporting evidence, which is interpreted as hallucination.
(2) Latent-based methods. These approaches exploit hidden states or latent directions of LLMs, probing truthfulness signals directly from internal representations.
EigenScore (Chen al., 2024a): proposed in INSIDE (ICLR 2024), it measures response consis- tency via the log-determinant of the covariance matrix their latent embeddings.
Search (CCS) (Burns et al., 2022): queries the model with contrastive prompts (e.g., factual vs. hallucinated) and trains a MLP to evaluate consistency of latent repre- sentations. Inconsistent activations are interpreted as evidence of hallucination.
Truthfulness Separator Vector (TSV) (Park et al., 2025): Trains a steering vector that separates truthful from hallucinated generations in latent space, and then classifies new samples by projecting onto the learned centroids.
Other potential baselines. Several alternative baselines for hallucination detection exist, however they are challenging to implement in comparison to our proposed method. One major category, highlighted in Huang et al. (2025), is fact-checking external retrieval methods, including: FactScore (Min et al., 2023), which decomposes long-form text into fact chunks and computes the proportion of chunks verified by an external knowledge base. And, Factool (Chern et al., 2023), a tool-augmented method enabling LLMs to detect factual hallucinations using external resources. These approaches rely on additional knowledge sources (e.g., Wikipedia or local databases), which conflicts with our core objective of detecting hallucinations without external verification.
Other recent hallucination methods include:
ReDeEP (Sun et al., 2024), a method specifically designed for Retrieval- Aetreeal--uugmentee Generation (RAG). It disentangles retrieved evidence from the L.L''s parametric knowledge, then measures
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the alignment between the two. While effective in RAG settings, it is task-specific: in internal-signll based experiments, there no retrieved context to disentangle, so ReDeEP is not applicable.
Fac (Nie et al., 2024), which formulates hallucination detection distribution-free hy- pothesis testing problem. controls Type error by introducing an abstention mechanism, re- quiring held-out calibration dataset and repeated testing procedures. Although statistically elegant, this framework is fundamentally different from our design goal: we aim for lightweight detection relying purely model-internal dynamics, without the need for extra calibration data or abstention strategies.
AGSER (Liu et al., 2025a), which leverages attention guiiodd self-reflection. analyzes self- attention maps, distinguishes "attentive" vs. "non-attentive" tokens, and trains a secondary clas- sifier on the distribution of attention patterns to identify hallucinations. This approach depends on full access intermediate attention weights and assumes autoregressive token-to-toker at- tention structure. However, D-LLMs adopt denoising architectures where attention is not aligned with autoregressive decoding, making AGSER difficult to adapt. Additionally, extracting and processing large attention tensors incurs substantial computational overhead, which goes against fffiiiency-orienter design.
Overall, while these methods are valuable in their respective contexts, their reliance on external sources, additional datasets, or architectural assumptions makes them unsuitable as direct baselines for our study. For this reason, we not include them in the main comparison and instead discuss them here for completeness.
C ALGORITHMS
We provide the pseudo-code of our method in Algorithm
D MORE ON EQ AND EQ
D.1
EQ
P(y Asat)) P(v)
((;
P(y, Agub) log
dy d.Asub
f P(u. Awn)) log P(y Auab) dy d.A.ub - / Ply. Asab) log P(y) dy d'aun
/ P(u. Arah) log P(y Amab) dy d.Aumb + HY)
2 / Ply, Auat) log go Amat) dy d.Aus
Am (Y Amtt)] Lats
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Algorithm Overall training framework for TRACEDET
1: Parameters: batch size B, prior masking ratio extraction weight
2: Inputs: Entropy sequence A E x
3: Initialize: Transformer encoder with random weights
4: Input encoding:
5: Project A using MLP-based positional encoder
6: Concatenate sinusoidal time embeddings of dimension dz
7: Apply Transformer encoder to obtain contextual embeddings emb E Bxdr
8: Sub-instance extraction:
9: for =
1 to T do
for b= 1 to do
11:
Compute cross attention: att = attn(emb, A) Apply linear layer and softmax over time steps: m4,6 = softmax([ineea((at)) 13: end for
14: end for
15: Apply masking: Asmh M о A
Sub-instance classification:
17: for b = to B do
18:
Aggregate temporally: Alab = Mean( Orean(Asub.-ee (Asub..b.))
19:
Compute hallucination probability: ús ReI.U-MI.PAAa.
20: end for
Training objective:
22: Classification loss: Cols
BCE( y6)
Extraction regularizer:
B
m4.b + (1 m),,))) log
1 174
Lext
mug log
r
1
b=1
24: Total loss: C= Cola + 8 Lext
25: Backpropagate and update parameters
D.2
EQ 5
P(Arub |A)
[(A; Asub) = EA.A.s
log P(Asub)
log P(Arab AA)Q(AAuu))
EAA.
P(Asut) (A(su)))
P(Arab AA)
BAAA log ((Asu)))
R.A.
log (AAuub)
P(Asub)
P(Arab AA) log Q(Aeub)
E.....
< EA DRI((((Aun)
()) Q(Aa)(()))))
D.3 PROOF OF RELAXATION
KL decomposition under independent Bernoulli factors. Under the independence assumption, both the true posterior P(Asub A) and the variational prior Q(Asa)) factorize into products of Bemoulli distributions. In this case, the KL divergence decomposes into sum of element-wise Bernoulli KI. terms:
DKL (P( (() = Σ DKL (Born((p)))) Bern(r)).
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KL divergence between two Bernoulli variables. For single Bernoulli distribution, the KL divergence has the following closed form:
p (Re((()) Bern(r)) plog P (1 p) log --T
E EXPERIMENT SETTINGS
All experiments were conducted on NVIDIA A40 GPUs. Hyperparameter settings are shown in Tabel 5.
Table 5: Hyperparameter search space for TRACEDET. Notation: log-spaced; i linearly spaced. only applies to LLaDA.
Parameter
Range
Grid size
Learning rate (Ir)
[10-5 5 10 10-$) "/t
8
Batch size (batch size)
{8, 64]
2
Dropout rate (dropout rate)
[0.0. 0.4
5
Number of layers (nlayers)
{2, 3, 4)
3
8
[0. 2
6
00.1, 0.4
4
cfg"
{0, 13
2
F
CASE STUDY
F.1 INTERLEAVING HALLUCINATION
Retained Steps
<Question>>Profeee
A. Selvanathan
professor
university that public pri-
vate?><Question>>
<Golden label:> pabll>-pbbice/GGoolde label,>
The following are TraceDet extracted steps
"uccp="<cssserrppuiee//aweee' </step>
<step>> "stp=""cuweer?ullli///sssss"""" </step>
<step>> "uap"""Caassarrrrrttt/s//ss""""" </stop>
<step>>
"<answewr---rrttt////sssss"""""" </step>
<Output>" <Output>"<answe>>>>>> "wanaa/aeaarrpprreee//awwwaaa "ttp://awwwww""////tttotttttt
Retained Step
<Question> PreestoonDDigggaaiikkaaeeehhhhho (or triskaidekaphobia paraskevidekattrapphoba)) is the fear of whatt</Question>>>
<Golden label>-Friday Friday the 13<//Golden label>>
The following are TraceDet extracted steps
<step>"<<nnswer Friggriskagk anythingobia (or frost excessive kevide atr the fear of num-
bers. <oas<///nswr""" <//stpp>>>>>>>>>>>>>>>>>>>>>>>>>
<step>>" <step>""<nswwrr" Friggriskak thingsobia (oriska refrigeration oranswerkevide atriobia) is fear "<answer>>
of ice. </answer>" </step>>
<step>> "<answer> Friggrrskiin (oriskak orkevideatri) is fear of
</step> " fiauweer>Figgatiiikidkkaapphp (or triskaidekaphobia paraskevidekarriphhoba))
the fear numbers, </answer>" <</oupp
17

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Anna Published as a conference paper at ICLR 2026
Retained Steps
<Question>>On On which of the hills of ancient Rome were the main residences of the Cae-
cars?</Question>>
<Golden Pal--aaaaaaaooolllleee label>
The following are TraceDet extracted steps
</step>
<step> http:raaawwwaaaaaaaaeeeaaaaaaae
</step>>
<step>"<answr>>Pal Hill Hill-</answer>", </step>
<step>"<<anwwe "<answer>>Pala Hill </ans///>/wwr" </step>
<Ouiput>> <Outpu>>"<answert "aanswer>-Pall "ata///wwe"""//Ouppppp
Retained Steps
<Question>> What NIFL Premier Intermediate League team did Sean Connor play for? for></Question>>>>
<Golden label>> label>Distillery </Golden label>>
The following TraceDet extracted steps
<step>><<an "step=""<aswer>>Disiill
F.C=</answe>> <//ste>>>>>>>>>>>>">>>>>>>>>>>>"
<step>""<answee "<aasswrrs Newis Distillery F/C</answer> " </stcp>>
<step>>" <answer>>Newingghe
Buab....../aaswwer.
</step>
<step>" Sapp>><<a"" "<answer> Newingtoon Youth F.C. </answe/rrr>" </step>
<Output>> "<answer>Newington Youth "CC.///swer>""////// "</Ouutt>>""/Ouuu>>>>>>>>>>>>> </Oulput>>
F.2 INCONSISTENT GUESSES
Retained Step
<Question>> Who was
declared Model of Millennium by
Vogue editor
Win-
tour?</Question>>>>>
<Golden label>=Gisele Buendehen</Golden label>>
The following TraceDet extracted steps
<step>" "<answer>> Cindy Campbell Crawford declared declared Millennium Millennium
itor editor editor editor editor editorour editor editor editor editor editor editorlags Camp-
hell<</answe>>" <//sepp>>>>>
"step>"<answer>>> Cindy Crawford Crawford declared declared declared declared Millennium Mil-
lennium editor editor editor editor editorint editorintintint Campbell Campbell Campbell Campbelllags
Campbell </answer>>" </step>
<step>"<<nnser>>>>>>>>>">>>>>>>> Naomi Crawford Crawford Crawford Mossourour Campbell Campbell Campbell
Campbell Campbell Campbell Campbelllags Crawford <aasswerr/////">>"">/// <//tep>>
<step>" Nicole Kid Crawford f/answerzlags Campbell Campbell Campbell Nicole Camp-
bell0 m/del<</anwwr>>> </step>>
<OOtput>> "'Ouppt>>"<answorr "<answer>-Nicol Nicole Kidman</answor>> //Outptt>>>>
Retained Step
<Question>> Which Canadian born actress the star in the movie Barb Wire?<//Qeesionn>>
<Golden label>-Pamela Gaaeeeeenn//Godennnn label>
following are TraceDet extracted steps
<step>> <answer'> aaancranweernnwerrasseeeee
actress actressanswer actress actressanswer
actress actress actress actress actress actress actress Wire Wire Wire Wire Wire Wire Wire Wire Wire Wire Wire Barb Wire Wire Wire Wire Wire Wire<</asswer>"
</step>>
<step>"<<answer Jenniferlennifer
Wireanswer Wire Wire Wire Wire Wire<</asswer>""cerm </step>>
"eeps""<asswer>Michllleeeee
Thee7399kka2223998222255551
Wire Wire Wire Wire
Wire
Canadian </step>>
"con=""answer>Kiii Kirstodie
Therellar$5100069awwwee
anisotropicwald
Kirst
Kirst
Kirstaghanaghan Barb<</nswer>>. </step>
<Output>"<answee Kirstie /aans///s"""///uuttiiii
18

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Published as a conference paper at ICLR 2026
F.3
PERSISTENT ERROR
Retained Step
<Question.>In In the Shakespeare play The Tempest, Prospero is the overthrown Duke of /herte><Question>>>>>
< <Golden label> The weather in Milan<//Glden label>
The following are TraceDet extracted steps
<<step>" InInin Shakespeare play Temp Temp Temp Prosper Prosper Prosper Prosper Prosper Prosper Prosperrownrown Duke Duke Prosperiel. Prosperjjanswer.ln Prosper Shakespeare playThe Tempest, Prospero Prosper Prosper overthrown Duke of Arig", "ris"<<<step>>
<stopp>" In the Shakespeare play,o is thethrown Duke of overrown Duke of 2018ielicl. janswery In Shakespearc play Theest,o is the overrown Duke of Ariic!", </step>
<step>>"n Shakespeare play The, thethrown Duke of 'Ariell. the Shakespeare play Theest, Prospero the overthrown Duke "'riiel'"" </step> <step>> "In the Shakespeare play The Tempest, Prospero is the overthrown Duke of "Ariiel"", </step> <Output>>" "In the Shakespeare play The Tempest, Prospero is the overthrown Duke of 'rri- iel" lel"</Output>
G ADDITIONAL QUANTITATIVE RESULTS
Table 6: TPR@FPR-0.1 (%) comparison between TraceDet and baseline methods. The highest
score is bolded.
TriviaQA
HotpoiQA
CommonscnseeQA
Method
128
64
128
64
128
64
Lexical Similurity
14.5
15.6
19.8
19.3
0.0
0.0
EigenScore
23.9
29.6
23.5
24.1
40.2
26.8
CCS
9.0
26.9
11.8
10.3
14.2
19.5
TSV
23.5
26.8
14.1
16.1
23.6
14.6
TeaceDet
41.4
59.6
19.3
39.1
61.5
61.6
Table 7: Comparison of average epoch training time across TraceDet and training- methods on TriviaQA using 1700 training samples.
training-based based baseline
Method
Training Time (s)
CCS
3.64
TSV
19.2
TraceDet
2.25
H AVERAGED TRACE ENTROPY COMPARISON
The core idea of TraceDet is select sub-trace Asub that preserves steps predictive of hallu- cination. To analyze the statistical properties of the extracted sub-traces, we utilize the stepwise maximum token entropy Hmax, and summarize each example by the mean over the selected steps = [S] E1es H.max. We compare three variants: (i) No Masking, which uses the full trajec- tory (Aqub = A): (ii) TraceDet w/o Lents the same architecture trained without the extraction loss; and (iii) TraceDet. Figure 5a shows that TraceDet reduces both the mean and variance of E while preserving separation between hallucinated and non-hallucinated examples, indicating that mask- ing serves as an effective regularization mechanism. reduces the maximum entropy among the selected steps, removing noisy unstable transitions, while still preserving the entropy contrast that distinguishes hallucinated from non-hallucinated examples. Removing masking weakens this effect. Varying the masking ratio (Fig. 5b) confirms that stronger masking more aggressively moves steps with maximum token entropy without reducing the discriminative separation required for reliable detection.
19

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Published as a conference paper at ICLR 2026
Man lucinated
Entroy
Entrapy
2-
1-
No Masking
TraceDet w/o Cox
TraceDet
T=
T= 0.4
T
No Masking
(a)
(h)
Figure 5:
Comparison of averaged trace entropy selected by different model variants. No masking
refers to the full time step traces. (a) Comparison between different model variants. (b) Comparison
between different masking ratios т. Results are reported using
I TRACEDET IMPLEMENTATION DETAILS
Sub-instance extractor. Given the entropy sequence A E RxXBxx we first concatenate it with sinusoidal time embeddings and encode the result using lightweight Transformer (2-5 layers, 1 head, feedforward dimension 8), producing contextual embeddings emb. The extractor then generates a probabilistic mask M E (0, 1)' where each entry is computed as
r = Linear (attn(emb, A))
and attn denotes a attss-attention module that uses emb the query and a learned projection of A as the key/value.
To obtain binary temporal mask M E {0, 1)TXB, apply small Transformer encoder (1 layer, 1 head, feedforward dimension 8) to the concatenation of and emb, followed by linear layer and a sampling step. The mask is applied element-win to A to obtain Aubb M C A e RTxBxD, Since the sampling operation is non-differentiable, we employ the Gumbel-Softmax relaxation enable gradient-based optimization.
Sub-instance predictor. The masked trajectory Auu is temporally aggregated and passed to the predictor fo, which outputs a hallucination probability for each instance:
(A [0, 1], b= B.
In practice, fo consists of temporal aggregation module followed by two-layer MLP.
20


Reviews:
[{"review_id": "ILYwDZkfNt", "reviewer": "Reviewer_FNRD", "summary": "In this paper, the authors study the detection of hallucinations in text generations of Diffusion Large Language Models (DLLMs), as opposed to the majority of existing literature which strictly analyzes autoregressive LLMs. Motivating using an information bottleneck perspective, the paper introduces TraceDet, to identify the most informative and relevant action sub-trace during the denoising steps using a network g that predicts the mask, and another classifier f to detect hallucinated responses from truthful ones over the selected sub-sequences. Overall, TraceDet is shown to be effective on DLLMs on standard datasets such as TriviaQA, HotpotQA and CommonsenseQA.", "questions": "Kindly refer to the questions mentioned in the weaknesses section above.\n\nIn Equation 1, the summation over i is quite unclear. This could be improved by providing more details in the main paper, as is currently given in Appendix D.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) The paper studies a highly pertinent problem of detection of hallucinations in DLLMs, and is the first to the best of my knowledge to provide a dedicated analysis. While this can potentially limit the applicable scope since most LLMs in use today are autoregressive, the findings of the paper have great scope toward future development and understanding of DLLMs.\n\n\n2) The paper is well-written and introduces each key component in a clear and concise manner. The TraceDet framework introduced by the paper is motivated well, and analyses across the denoising trace rather than specific single-point failure modes that might occur. For instance, the representative D-LLM hallucination patterns such as Interleaving, Persistent errors and Inconsistent guesses help back the need for such trace-based analysis.\n\n\n3) The proposed method TraceDet demonstrates consistent and notable performance gains over a comprehensive suite of existing baseline methods that were originally developed for autoregressive LLMs, including both output-based and latent-based techniques on standard datasets such as TriviaQA, HotpotQA and CommonsenseQA. \n\n\n4) The paper also presents a fairly detailed analysis on the sensitivity of relevant hyperparameters such as the masking ratio $\\tau$ and the regularization weight $\\beta$, as well as re-masking strategies such as low-confidence,  entropy, random and top-k based masking. The latter further indicates that the proposed method would likely be effective in future variants of DLLMs as well, given the fairly consistent across different strategies.", "weaknesses": "1) MDP Formulation: The paper formulates the denoising process as a Markov Decision Process, however the proposed method does not adequately functionally utilise this MDP framework, given that the information bottleneck perspective is adopted. Further incorporation of MDP theory could potentially have been leveraged to help mitigate hallucinations, rather than focussing on detection alone.\n\n\n2) Evaluation Metrics: The empirical evaluations rely solely on AUROC. In real-world applications (and in imbalanced datasets, which hallucination detection often is), AUROC when used in isolation can fail to adequately capture detection performance. Could the authors kindly provide other standard detection scores such as TPR at low FPR and the F1 score, to better analyze the practical efficacy of TraceDet?\n\n\n3) The paper introduces an extractor $g_{\\theta}$ and a predictor $f_{\\phi}$ but provides limited detail on their architectural complexity. A more in-depth discussion of these components, along with an analysis of their training and inference costs relative to the main D-LLM, would be beneficial for assessing the method's practical efficiency. For instance, could these then be used to guide fine-tuning of the main DLLM to directly reduce generation of hallucinated responses?\n\n\n4) The analysis in Section 4.3 with maximum token entropy did not appear to be clear - it  does not really help highlight how TraceDet is helping improve the discriminability, especially with Figure 3a. Furthermore, is it expected that the averaged trace entropy for truthful samples is in general higher than that for hallucinated samples?\n\n\n5) The paper compares TraceDet to baselines adapted from autoregressive LLMs, many of which utilize multiple completed generations. A more direct and potentially stronger baseline would be to adapt these consistency-checking principles within the D-LLM's trace. For example, applying a consistency check across intermediate steps of a single generation would directly engage with the paper's core hypothesis.\n \n\n\n\nMinor Typos:\n\nEquation 1: $min_{f \\in H}$ L(Y, h(r0)) , should likely be $min_{h \\in H}$\n\nLine 197: asked tokens from $st$ ---> asked tokens from $s_t$ \n\nLine 229 to 231: Minor grammatical errors", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pPYykFujTZ", "reviewer": "Reviewer_h49A", "summary": "The paper introduces an information bottleneck based approach (called TraceDet) for identifying hallucinations in diffusion large language models (D-LLMs). TraceDet relies on intermediate denoising steps generated during inference of a D-LLM to identify potential hallucinations in the outputs. On a high level, it works by first identifying a subset of most informative actions (predicted masked tokens) pertaining to the factuality of the output and predicts a binary label for hallucination. The paper compares TraceDet against existing approaches for hallucination including both output-based and latent space-based methods, showing favorable results.", "questions": "- If more comparisons with methods involving training a detector are provided, esp with similar training complexity, I am happy to raise the score. If not, please provide a justification for why such additional comparisons are unnecessary.\n- Since IB is a form of regularization, can we please see comparison with other kinds of regularization? For example, one could add some L2-regularization in the TraceDet w/o Masking and see if that how that compares.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- D-LLMs are an emerging area of research and practical interest (esp when compared to Autoregressive, transformer based models). The paper addresses the challenge of detecting hallucinations in D-LLM outputs, while leveraging the information from intermediate denoising steps during inference. This research direction is pretty novel, as far as I know.\n- TraceDet is based on well established mathematical concepts (namely, information bottleneck). Though I haven't checked the formulation thoroughly, to the best of my knowledge it appears correct and I am happy to see methods with some theoretical grounding.\n- The paper uses well established benchmarks in the area of hallucination detection, which makes judging the work easier.", "weaknesses": "- Please correct me if I am wrong, but don't many if not all of the baselines not involve any training of a hallucination detector? They are purely inference time, statistics based methods. I believe this makes the comparisons unfair. I am giving a weak reject for this reason alone.\n\n**Minor Typos/Grammar** (these played no role in my score):\n- There is an extra space after decimal point (15. 2%) in the abstract.\n- In Section 3.1, do you mean something like \"... remasking which retains the highest confidence tokens ...\" instead of \"... remasking which retains the most confidential tokens ...\"?\n- $s_t$ is missing TeX formatting in Section 3.2 -> Action.\n- \"d\" should be capitalized in \"d-LLM\" in Section 3.2 -> Transition and in some other place in the manuscript.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wNDyxXJMpe", "reviewer": "Reviewer_741H", "summary": "1) This paper proposes TraceDet, a hallucination detection framework designed specifically for Diffusion Large Language Models (D-LLMs). The authors formulate the denoising process as an action trace and apply information bottleneck principles to identify the most informative sub-trace for detecting hallucinated outputs. Experiments on LLaDA-8B-Instruct and Dream-7B-Instruct across three question-answering datasets demonstrate an average AUROC improvement of 15.2% over baseline methods.\n1) This work addresses the underexplored problem of hallucination detection in D-LLMs, where generation proceeds via multi-step denoising rather than single-step autoregression. To the reviewer's knowledge, existing hallucination detection techniques are primarily designed for Auto-Regressive LLMs (AR-LLMs) and fail to exploit the intermediate dynamics of D-LLM decoding. The approach is strongly motivated by identified literature gaps: existing AR-LLM detectors rely on single-step signals, which fail to capture D-LLM dynamics, as empirically evidenced by the paper's observations (Figure 1) and baseline underperformance.\n3) The most significant concern relates to impact. While technically valuable, the community actively working on or using D-LLMs remains relatively limited; thus, the work's impact is currently more potential than immediate. Furthermore, the reliance on an \"inspired by\" claim for the IB principle (Section 3.3) represents a missed opportunity for deeper theoretical insight. Finally, by testing exclusively on QA datasets, the paper proves its concept in a controlled environment but does not address more challenging, unstructured tasks where hallucination detection is arguably most critical. These limitations collectively suggest that while the paper constitutes a solid contribution, its overall significance is not yet fully established.", "questions": "1. What underlying causes drive the observed hallucination patterns? Can you provide analysis linking specific denoising dynamics to the emergence of hallucinations?\n2. Could you elaborate on the key challenge that \"the hallucination-relevant action may be sparse and unevenly distributed across the action trace, and not every action contributes equally to the emergence of hallucination\"? Is there any theoretical or empirical support to justify the necessity of selecting a sub-trace A_sub?\n3. How would TraceDet perform on long-form generation tasks (e.g., summarization, creative writing) where hallucinations are more prevalent and consequential? What adaptations would be required for such scenarios?\n4. Is the assumption of independence for A_sub reasonable? Although Figure 5(b) shows the impact of different τ values, this choice remains questionable and warrants further justification.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The problem formulation is sound. Hallucination in the emerging class of D-LLMs is fundamentally different from that in AR-LLMs. The denoising process naturally introduces three distinct mistake patterns that classical methods for AR-LLMs cannot identify or methodically benefit from.\n2. The methodology is innovative and intuitive. Leveraging the trajectory trace and extracting partial steps successfully aligns with the identified problem. The introduced Information Bottleneck (IB) principle naturally connects token-level entropy with uncertainty identification in a theoretical and principled manner. The correctness of the derived lower bound for optimization is carefully verified.\n3. The evaluation evidence is sufficient and rigorous. The experimental results are convincing. The paper demonstrates consistent gains compared to output- and latent-based methods across comprehensive QA benchmarks. In addition to the main experiments, the ablation studies effectively demonstrate both the practical value and efficiency claims.", "weaknesses": "1. The assumptions underlying the IB principle application are not formally justified. The application of IB is presented more as an inspiration than a formally justified choice. The paper provides limited empirical evidence or theoretical citations to establish a solid foundation for its core methodology, which undermines the completeness of the research, despite the mathematical soundness of the IB derivation and promising results.\n2. The scope is limited, and the impact should be further emphasized. While the paper aims to address hallucination detection, the experiments are confined to multiple-choice, open-ended, and contextual QA datasets. Hallucinations primarily occur in open-domain generations, whereas QA benchmarks are often considered closed-domain. The reviewer suggests evaluating hallucination mitigation for more open-ended and real-world generation tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)