JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA
Authors: Hyunju Kang
Woohyun Lee
Jaewon Kim
Hogun Park
Venue: ICLR 2026 Poster (2026)
Keywords: Industrial Anomaly QA
Large Multimodal models
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce JUDO, a novel framework that enhances industrial anomaly detection by juxtaposing defective and normal images for domain-oriented visual reasoning while injecting domain knowledge through reinforcement learning.

Abstract: Industrial anomaly detection has been significantly advanced by Large Multimodal Models (LMMs), enabling diverse human instructions beyond detection, particularly through visually grounded reasoning for better image understanding. However, LMMs lack domain-specific knowledge, which limits their ability to generate accurate responses in complex industrial scenarios. In this work, we present JUDO, Juxtaposed Domain-Oriented Multimodal Reasoner, a framework that efficiently incorporates domain knowledge and context in visual and textual reasoning. Through visual reasoning, our model segments the defect region by juxtaposing query images with normal images as visual domain context, enabling a fine-grained visual comparative inspection. Furthermore, we inject domain knowledge through supervised fine-tuning (SFT) to enhance context understanding and subsequently guide domain reasoning through reinforcement learning (GRPO) with tailored rewards, opting for a domain-oriented reasoning process. Experimental results demonstrate that JUDO achieves superior performance on the MMAD benchmark, surpassing models such as Qwen2.5-VL-7B and GPT-4o. These results highlight the importance of enhancing domain knowledge and context for effective reasoning in anomaly understanding.

Published as a conference paper at ICLR 2026
JUDO: A JUXTAPOSED DOMAIN-ORIENTER MULTI- MODAL REASONER FOR INDUSTRIAL ANOMALY QA
Hyunju Kang*. Woohyun Lee Jaewon Kim²
Hogun Park
"Sungkyunkwan University "Seoul National University (neutor, (a edu ttecblue95j@sm..ac {hogunpark @akkurrkkkkkkkkkoomo @skkkkkkkee.
ABSTRACT
Industrial anomaly detection has been significantly advanced by Large Multi- modal Models (LMMs), enabling diverse human instructions beyond detection, particularly through visually grounded reasoning for better image understand- ing. However, LMMs lack domain-specific knowledge, which limits their abil- ity to generate accurate responses in complex industrial scenarios. In this work, we present JUDO, Juxtaposed Domain- Oriented Multimodal Reasoner. frame- work that efficiently incorporates domain knowledge and context in visual and textual reasoning. Through visual reasoning, our model segments the defect re- gion by juxtaposing query images with normal images as visual domain context, enabling fine-grained visual comparative inspection. Furthermore, we inject do- main knowledge through supervised fine-tuning (SFT) to enhance context under- standing and subsequently guide domain reasoning through reinforcement learn- ing (GRPO) with tailored rewards, opting for fomain-oriented reasoning pro- cess. Experimental results demonstrate that JUDO achieves superior performance the MMAD benchmark, surpassing models such Qwen2.5-VL-7B and GPT- 4o. These results highlight the importance of enhancing domain knowledge and context for effective reasoning in anomaly understanding.
Domain-Oriented
INTRODUCTION
Visual anomaly detection (Jiang et al. 2025 Xu et al. 2025), which aims to identify anomaly pat- terns from images in industrial environments, has evolved significantly with the emergence of Large Multimodal Models (LMMs) (Hurst et al. 2024 Bai et al. 2025 Wang et al. 2025). Although most anomaly patterns are typically domain-specific, current LMMs have demonstrated reasonable gener- alization capabilities in complex industrial environments, as shown by the MMAD benchmark (Jiang
eet al. 2025). Building upon this foundation, recent models such AnomalyR1 (Chao et al.) 2025)
and OmniAD (Zhao et al,] (2025) have employed Group Relative Policy Optimization (GRPO) (Shao
al. 2024) to further advance multimodal anomaly detection. Specifically, A I is the first
to introduce GRPO training into anomaly detection tasks, while OmniAD advances this approach by integrating anomaly segmentation to enable visual grounded reasoning.
Despite these advances, current GRPO-based models (Chao et al. 2025 Zhao et al. 2025 primarily optimize instruction-response matching, overlooking the incorporation of domain knowledge essen- tial for generalizable defect reasoning. Such domain knowledge typically encompasses character- isties of normal and defect samples expressed in textual form, including their definitions, causes, and consequences, which serve as essential priors for reliable defect analysis. In addition, normal images serve as visual context, providing references for distinguishing normality from anomaly. However, such knowledge and visual context are often highly specialized and rarely encountered during LMM pretraining, making reasoning based solely on their inherent knowledge insufficient. Recent studies (Jiang et al.] (025)) have demonstrated that providing external knowledge nor- mal samples (Zhao et al., 2026) as context in prompts during inference can alleviate this limitation. However, providing knowledge and context only at inference time remains limited when LMMs lack
"Equal contribution. Corresponning author.

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sufficient internal knowledge, they may become overly dependent on external context, leading to misaligned responses that prioritize contextual plausibility over accuracy. This limitation ultimately stems from insufficient internalized domain knowledge and contextual understanding, hindering liable and accurate domain-oriented reasoning.
To address this challenge, propose JUDO, Juxtaposed Nomain-Orienter Multimodal Reasoner, the first approach that systematically internalizes domain knowledge for industrial anomaly detec- tion through learning. Unlike prior GRPO-based models that employ post-training without do- main alignment, JUDO unifies domain understanding across visual grounding and textual reason- ing. Our framework begins in Stage by establishing visual reasoning through juxtaposed seg- mentation learning by comparing normal and defect images. This approach leverages the underex- plored potential of normal samples, which typically serve only as optional context in inference, while JUDO incorporates it in core reasoning context during training. In Stage 2, we enhance domain- oriented textual reasoning by injecting domain knowledge into model parameters, in contrast to prior work (Jiang et al. 2025) that introduces textual domain knowledge externally via prompts. This stage builds foundational domain knowledge for industrial anomaly reasoning, yielding more reliable domain- alligned reasoning. Finally, Stage 3 unifies visual grounding and domain semantics through reinforcement learning (GRPO) (Shao et al., (2024), using rewards for domain reasoning, segmentation, choice accuracy, and structural alignment rewards to ensure that the model produces accurate domain-aware anomaly understanding.
Extensive experiments on the industrial anomaly detection benchmark MMAD (Jiang et al. 2025) demonstrate the effectiveness of our approach. JUDO achieves superior performance, showing that jointly learning domain knowledge and visually grounded reasoning substantially improves indus- trial anomaly understanding. Beyond accuracy gains, JUDO enhances reliability and explainability by grounding predictions in localized defect regions through anomaly segmentation and aligning reasoning with domain-speedt ally verifiable decision-makinn makinn while maintaining strong performance across diverse defect-related tasks. The implementation of JUDO is publicly available
knowledge. This unified training enables interpretable and visu-
We summarize our contributions as follows:
We propose JUDO, the first approach systematically internalize domain knowledge and context into both visual and textual reasoning for industrial anomaly understanding.
We present a unified framework that builds domain understanding through visual segmen- tation and textual knowledge internalization. We integrate these capabilities via reinforce- ment learning with our domain- aligned reward designs.
Through comprehensive experiments, we demonstrate that JUDO achieves superior perfor- mance over prior GRPO-based models, while improving explainability through anomaly- grounded segmentation and enhancing reliability via domain-aligned reasoning for real- world industrial scenarios.
2 RELATED WORK
2.1 LARGE MULTIMODAL MODELS (LMMs)
Recent Large Multimodal Models (LMMs) have advanced visual understanding through high- quality instruction tuning (Hurst et al. 2024 Bai et al. 2025 Chen et al.] (2024) Kimi Team et al. 2025 Comaniei et al. 2025), stronger cross-modal architectures (Wang al. 2025|] Agrawal al.
2024b), and more sophisticated training pipelines (Xiaomi et al.] 2025 Deitke et al. 2025). Most
multimodal models now support multi-image inputs (Xiaomi al.) 2025), with GPT-4o (Hurst ct al. 2024), GPT-5 (Singh et al. 2025), the Gemini-2.5 model (Comanici et al., 2025). and In- tern V L3.5 (Wang et al. (025)) demonstrating improved reasoning pertormance through cross-image comparison. However, the majority of these models (Hurst et al. 2024 Comanici et al. 2025 Bai al. 22055] Wang et al., (025) remain optimized for common question question-answering answering tasks grounded in general knowledge. Consequently, their reasoning performance drops noticeably in specialized application domains, where domain knowledge is critical.
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2.2 INCORPORATING DOMAIN KNOWLEDGE INTO LMMS
To address the demand for specialized knowledge for LMMs (Song et al. 2025), specifically in fields such as finance (Qian et al. 2025), biomedicine (Liu et al. 2025b), education (Agrawal al.) 2024a), and materials (Prabhakar et al., 2025), researchers have actively studied how to integrate domain-specific expertise into LMMs. Most approaches fall into two categories: dynamic injection and learning- based integration through pretraining or fine-tuning. Dynamic injection- based methods provide the external knowledge in a similar way to RAG (Zhao et al. 2026) at inference time without additional training: their effectiveness is highly dependent retrieval quality. In contrast, learning- based methods encode the domain knowledge into the model's 's parameters. However, this line of work remains underexplored for industrial anomaly understanding.
2.3 INDUSTRIAL ANOMALY DETECTION AND LMMS
Visual anomaly detection in industrial settings has evolved significantly with deep learning. Early and still widely used methods often unsupervised, focusing on learning a model of nor- mal data and identifying deviations (Li et al. 2025 |Bergmann et al.) (021). These include ecconstruction-based methods using autoencoders or GANs, where high reconstruction error sig- nals an anomaly (An & Cho 2015 Schlegl et al. 2017), and embedding-based methods, which map normal samples to a tight cluster feature space (Roth al., 2022; Defard et al.! (02T). While effective for detection and localization on benchmarks like MVTec AD (Bergmann et al.) 2019),
these approaches typically do not provide defect analysis for their predictions.
The advent of L.Ms has introduced a new paradigm that enables comprehensive defect analy- sis. Rather than simply detecting anomalies, LMMs can now respond to diverse analytical queries about defect characteristics- such identifying defect types, describing their visual appearance, and analyzing their potential consequences. To facilitate research in this area, a MMAD bench- mark (Tiagg et al., 2025) has been presented, providing suite for evaluating LMM's reasoning abilities on industrial anomaly problems. Meanwhile, several works have recently applied LMMs to this challenge. AnomalyGPT (Gu al., (024)
early method using LMMs for zero- shot anomaly detection and generating descriptive reports. More recently, a few methods employ reinforcement learning to improve reasoning quality. AnomalyR1 (Chao et al.) (025) incorporates Group Relative Policy Optimization (GRPO) (Shao et al. 2024) to refine its reasoning. Furthermore, OmniAD (Zhao et al.) (2025) unifies the anomaly segmentation and anomaly reasoning problem for fine-grained defect understanding.
METHOD
Our proposed model, JUDO, is a domain-oriented multimodal reasoner for industrial anomaly un- derstanding. The main claim of JUDO is that domain knowledge and contextual information funda- mentally matter in industrial anomaly understanding, yet this approach remains underexplored and non-trivial. In this direction, our core contribution is a unified learning objective in which com- parative visual reasoning and domain semantics fuse together into the domain-aligned reasoning process, described in Figure T The first stage introduces a comparative reasoning ability between query and normal images for patch-level segmentation, then the second stage internalizes textual do- main knowledge into model parameters, and the final stage unifies the visual grounding and domain semantics using GRPO-based optimization with tailored reward designs. The following subsec- tions detail each training stage, with the corresponding dataset construction process described in Appendix A
3.1 STAGE 1: LEARNING ANOMALY SEGMENTATION- BASED JUXTAPOSED REASONING
For reliable and fine-grained visual reasoning, we employ anomaly segmentation to pinpoint defect regions and integrate this capability into the reasoning process. To encompass proper domain visual context, we propose the juxtaposed reasoning paradigm that explicitly compares defective images against their normal samples during training. While most existing segmentation methods achieve reasonable performance by directly predicting the target regions, the lack of clear reasoning criteria for anomaly judgment limits segmentation performance in defect detection. Additionally, although
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Query
Stage
Stage
There a defect
Segmentatee
the object
Whar an mrarss ligineee
Whatis the affeat of the defeor?
Roward
Domain Sntpper
Chosas optiors.
Arowcr
Sogmentation based Juxtaposed Ressoning
Domaln Knowledge Injection
Domein-oriented GRPO
Answer for Domain Question
Pseudo-Demain Rationale
Ground truth
Generated Output
proved aosthatics
B: No signilicant effect
(.ng7-7..2--7.44... 18.2)) (9,3) (10,)) 410,4)</sep>
The first image reveels cable routed differently from the second image, suggesting departure from the standard design that could impair functionality. Henos, the correct answer Reduced functionality.
<a (8.2)-
C: Reduoed functionality
(9.3))(9.))))).4..
dumsiiity
A. Connectors may lose their alignment due orientation concerns, including imoroper alignment twisting.
(10,4) <tsag>
<thinke The firat image shows cabia routed differently from the second image,
<think....
routing compared to the second image. This indicates ... </thini> <onswaraCsdanawer>
Figure 1: Overview of JUDO (Juxtaposed Crinnne-riieeted Multimodal Reasoner). The framework progresses in three stages: (1) Stage 1: Learning anomaly segmentation bassdd juxtaposed reasoning, (2) Stage 2: Nomain-knowledd optimization. Through the progressive stages, we incorporate the domain knowledge and context into LMMs for reliable and robust anomaly analysis.
injection, and (3) Stage 3: Domain-oriented group relative policy
using normal images as contextual reference has become common practice during inference (Jiang et al. 2025), how to efficiently harness this comparative context to enable explicit reasoning of defects during training has yet to be addressed. Our comparative training paradigm shifts the objec- tive from simple pattern memorization a fine-grained juxtaposed reasoning, enabling the model to internalize more generalized and fundamental notion normality as a baseline for anomaly understanding.
Inspired by text-based segmentation approaches, such as Text4Seg (Lan et al. 2025 Zhao et al, 2025), the model is trained output the coordinates of anomalous patches within 16x 16 grid through SFT (Ouyang et al., 2022). The instruction is given to describe the anomaly region and ex- plain the visual evidence by comparing query images with normal samples as juxtaposed reasoning. For instance, a defect region is represented as a textual sequence such as (11,12)-(11,14), (12.11), pinpointing specific patches that deviate from the norm, including juxtaposed explanations. This format compels the model to perform a direct, patch-level juxtaposition, moving beyond general comparison to a fine-grained recognition differences against the normal template. This spatial grounding is crucial, as it forces the reasoning process to be tied to specific visual evidence, making the subsequent textual explanation more accurate and reliable.
Dataset Construction. We construct training dataset for juxtaposed fine-grained anomaly seg- mentation. We utilize only one image from each defect category in the MMAD dataset, and addi- tionally, incorporate the REAL-IAD (Wang al. 2024) dataset for more generalized segmentation capability. As illustrated in Figure of the Appendix, each anomalous query image is paired with a randomly sampled normal template from the same object category. The model is then trained to gen- erate dual-part response from this pairing: first, a textual sequence of patch coordinates between <seg><<> < seg> tags that serves ground the explanation by identifying the anomalous region, and second, corresponding comparative explanation within synthetically generated describe the defect by contrasting the two images, given the anomalous region bounded by a red line and the defect type.
tags, which is
3.2 STAGE 2: DOMAIN-KNOWLEDO INIECTION
While Stage 1 equips the model with comparative reasoning ability using normal images as the do- main context, it primarily focuses visual reasoning processes. However, the model still lacks sufficient domain knowledge for industrial anomaly problems. As a result, the model struggles to deliver accurate textual reasoning and often fails to derive correct solutions without proper foun-
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dational knowledge. To address this limitation, inject the domain knowledge (Mecklenburg et al. 2024) by constructing domain QA datasets and employing supervised fine-tuning (SFT). This approach differs from conventional supervised learning that directly trains correct answers to queries in the MMAD dataset, as it focuses on supervised learning of domain knowledge that may not directly correspond to specific instructions but provides foundational knowledge essential for solving related problems. Through supervised fine-tuning on the domain QA dataset, the model acquires a more generalizable understanding of how domain knowledge applies across different ob- ject categories and defect types. This enhanced understanding forms the basis for Stage 3, where reinforcement alignment further refines the precision and reliability of domain reasoning.
Dataset Construction. We generate question answer pairs using the raw domain snippets provided in MMAD (Tiang et al. 2025). Fach snippet contains an unstructured textual description of the char- acteristics of an object category and its associated defect types, and serves as a textual knowledge source for constructing the Stage 2 dataset (see Table f in the Appendix). Specifically, we prompt GPT-4o to reflect inspection knowledge from the unstructured domain snippet into structured QA pairs -for example, questions related to defect criteria, functional implications (e.g., "What criteria indicate that the fabric border is defective?" or "Why is detecting loose threads important for prod- uet reliability?"). These questions are derived purely from the textual information in the snippet and not tied to any specific anomalous image sample. To improve robustness, each QA is further paraphrased into multiple semantically consistent but lexically diverse variants. We show a more detailed data construction process in Section A Finally, every QA instance is paired with a nor- mal image from the corresponding object category. grounding the textual domain knowledge in its object-level visual context and naturally prompting the model recall relevant domain knowledge during reasoning. We provide examples of the generated QA pairs in Section B.2
3.3 STAGE DOMAIN-ORIENTED GROUP RELATIVE POLICY OPTIMIZATION
The final stage of our framework. Stage is designed elevate the model's capabilities in the visual and textual domain-orientee reasoning, mainly obtained from prior stages. While Stage builds a foundation in visual juxtaposed reasoning and Stage 2 internalizes textual domain knowledge, these skills are not yet integrated. Thus, we employ Group Relative Policy Optimization (GRPO) (Shao) al., 2024), which is reinforcement learning method that optimizes policies by comparing rela- tive performance within groups of trajectories for better generalization. We design GRPO policies to align the model's behavior, ensuring that the final output seamless integration of accurate visual grounding and deep domain understanding. This alignment is guided by multi-faceted reward functions that provide a comprehensive feedback signal, composed of three primary components. We explain each policy with respect to reward functions follows.
Domain Reasoning Reward. The primary objective of the Domain Reasoning Reward is to guide the reasoning process presented within the <think></thinn tags toward aligning with the domain-oriented reasoning process, specifically queries targeting the MMAD benchmark. Since there is ground-truth for each instruction encompassing domain knowledge and context for MMAD datasets, we generate the pseudo-domain rationale for the 1-shot train split using GPT-4o, providing full contexts such as the query, correct answer, images including normal reference, jux- taposed reasoning from Stage 1 pipeline, and relevant domain knowledge. We treat this generated pseudo-domain rationale as the semantic target and design the policy to guide domain-oriennee di- rection. Specifically, we define the reward, Ratomain using the cosine similarity between the embed- ding vectors of the model's generated reasoning as Fggnn and a pseudo-domain rationale denoted as Epdoomin using the representation space of all-Min MMiniIM-----
SentenceTrane Transformer (Reimers) & Gurevych 2019) model, which effectively encodes the semantic meaning of one or multiple sen- tences, denoted as c).). This reward mechanism is formalized as:
o( (Egen) o( Epdomain) [ Egen)) [ Epdomain)
Raomain X
The domain reasoning reward leverages semantic similarity to align the model's reasoning with a reference rationale that is grounded strictly in the provided evidence. The pseudo-domain rationale reorganizes the inputs without introducing new knowledge, ensuring that GPT-4o serves only as an evidence structurer. This reward differs from typical GRPO settings that emphasize answer correct-
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ness output format, since it directly encourages consistent Nomain-orienter reasoning patterss while remaining flexible to the phrasing of the generated explanation.
Segmentation Reward. This reward evaluates the spatial precision of the anomaly patch coor- dinates generated within the <seg></ seg> tags through Fl score calculation (Zhao et al. 2025), reinforeing the skill developed in Stage 1. A reward of 0 is immediately assigned for any improperly formatted coordinate strings. For validly formatted outputs, the reward function compares the set of predicted grid cells (P) against the ground-truth set (Pa) using the following piecewise function:
1.0
P P and Pa = 0,
Raeg
0.2 + 0.8 Pc(P. if P + 0 and Pa + 0,
0.0
otherwise.
This formulation incentivizes not only high-overlap localization but also correct format adherence and accurate identification of anomaly-frre instances, penalizing cases where only one set is empty.
Choice and Structural Alignment Reward. This composite reward ensures correct, well- structured, and logically sound output through three components (Shao et al., 2024 (1) Choice Reward rewards correct multiple-choice selection within <answer>< answer> tags to pro- mote accurate decision- makinn: (2) Format Reward ensures adherence to the required format as <seg>..... <thi nn> <answer r> structure, ensuring the structure of responses is consistent and parsable; and (3) Reasoning Structure Reward encourages both the conclusive answer within the reasoning and final answer to be correct while penalizing answer choices mentioned in the first half of reasoning text, preventing premature commitment.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We evaluate our approach on the MMAD benchmarks Jiang et al. 2025), which integrate four datasets: MVTec AD (Bergmann al. 2019). MVTTe LOCO (Bergmann et al. 2022), VisA (Zou et al. 20222. and GoodsAD (Zhang et al. 2024 datasets, with multiple-choice QA covering seven key subtasks. We report averaged accuracy as the evalua- tion metric, computed as the ratio of correct predictions to the total number of predictions. For Stage 1, we sample 10 instances from each category in the Real-IAD dataset (Wang et al.) (024) together with one instance per category from MMAD, yielding 1.4k and 293 images, respectively. In Stage 2, we construct a domain-speciff QA corpus by leveraging the MMAD domain knowledge JSON files. For each category, we generate 30 unique questions and augment them with two paraphrased variants, resulting in approximately 13k QA pairs. Finally, Stage 3 applies GRPO using a sparse sampling strategy, where only one training instance per category from MMAD-identicn to the samples used in Stage i-is utilized, yielding 1.4k QA pairs for reinforcement aligmment.
Datasets and Benchmarks.
Implementation Details. Our framework is built on PyTorch 2.5.1 with Hugging Face Transform- ers and the TRL GRPO trainer. We use 20we22.5-V--7B as the base model, initialized from a vasion-language pre-trained checkpoint. Training runs GPUs using torchrum and DeepSpeed ZeRO-3. For Stage and Stage 2, we train for 8 and epochs, respectively, using learning rates of 1 10- 6 and 5 x 10-7 in bfl precision. For GRPO, we use 16 generations per prompt, a batch size of 8, and train for 14 epochs in bf16 precision. Additionally, we set the domain reasoning reward coefficient À 0.1 to avoid over-emphasizing semantic similarity to the pseudo-domann rationale, ensuring that it acts as soft alignment signal rather than dominating the multi-reward optimization.
single node with 4xNVIDIA H200
Baselines and Inference Settings. We benchmark JUDO against leading general- purpose models such as Qwen2.5-VI, Intern VL.35,, and various commercial models. Our primary industrial spe- cialized baseline is AnomalyR1 1 (Chao et al. 2025); another recent method, OmniAD (Zhao et al.) 2025), was not included because its public codebase was unavailable as of February 2026. For con- sistency with JUDO's architecture, we re-implemented the AnomalyRI approach on Qwen2.5-VI 7B model using the author's ' provided code and our sampled dataset. The result is referred to as the AnomalyRI in Table 1 and the Base GRPO model in our ablation study in Section 4.3) All models are evaluated under a strict 1-shot inference protocol in the same way as MMAD, using both a query image and normal template as input to ensure fair comparison.

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Table 1: Performance comparison of both commercial and open-source LMMs MMAD with the standard 1-shot setting. Anomaly Discrimination uses the average accuracy (%) across the normal and abnormal categories. The best scores are highlighted in bold, while the second best are under- lined. indicates the best performance among the open-source models.
Model
Scale
Anomaly Discrimination
Clsssifcation
Localization
Defect
Description
Analysis
Classification
Object
Analysis
Average
Random Chunce
50.00
25.00
25.00
25.00
25.00
25.00
25.00
28.57
Human (expert)
95,
75.00
92.31
83.33
94.20
86.
80.37
86.65
Human (ordinary)
86.90
66.25
85,58
71.25
81.52
89.58
69.72
78.69
Clande-3 3.noe-3.5--onnntt 5-4ooncct
60.14
60.14
48.81
67.13
79.11
85. 19
79.83
68 36
70.38
Gomini-2.55 2.5ppo
83.07
73.86
67.20
79.97
86.27
94.88
83.08
81.19
Gemini 2.5--lshh
93,38
69.85
63.30
76.41
81.57
94.04
82,00
80.08
GPT-4o
68.66
65,80
55.62
73.21
83.41
94,98
74.92
GPT-5-mini
64.10
67.35
69.07
79.02
86.72
93.96
83.37
77.65
Qwen2.5-VL
LaaVA-OneVision
7B
$1.77
46.13
41.85
62. 19
69.73
90.31
80.93
63.27
IL3.
67,50
49.37
57.90
58.07
77.66
72.01
$1.11
66.30
Kimi-V1.-.A3
53.49
59.66
72.39
MiMo-Vi VL.
8B
$4.45
59.56
60.77
71.30
78.48
90.56
$1.60
70.99
AnomalyR1
60.93
64.81
70.72
79.06
85.52
86.91
77.29
JUDO
7B
65.04
74.74
73.01
84.56
89.41
94.04
87.58*
$1.20"
4.2 EXPERIMENTAL RESULTS
Table presents a comparison of our model with both general-purpose LMMs and the anomaly- focused baseline, such as AnomalyR1. JUDO achieves strong performance across the MMAD seven subtasks, reaching the highest overall averaged accuracy of 81.20%. Advantages of JUDO are par- ticularly evident across the four defect-related subtasks: classification, localization, description, and analysis. Since these tasks critically depend on domain-specifie knowledge, the performance gains highlight the effectiveness of incorporating domain knowledge in reasoning through our progressive stages. Specifically, Stage establishes a strong foundation by using juxtaposed segmentation train- ing foster fine-grained comparative analysis, which directly boosts defect localization 73.01 % (from 61.17% for the base Qwen). This visual grounding is then enriched in Stage 2, where the integration of comain--peciffc knowledge provides the conceptual basis needed for accurate expla- nations, as seen in the 84.56% and 89.41 %% accuracy the defect description and analysis. respec- tively. Finally, Stage
critical for merging these capabilities. The multi-reward GRPÔ training ensures the model's 'ss final output is coherent synthesis of juxtaposed visual evidence and textual expertise. This integrated process allows JUDO to not only locate defects precisely but also to clas- sify, describe, and analyze them with Nomain-alignes thinking process, leading to more robust and reliable reasoning.
However, JUDO does not achieve superior performance in anomaly discrimination tasks, with rich domain-specific foundational knowledge. Similarly, other models trained with a GRPO framework, such AnomalyRI, exhibit trade-off in raw binary detection accuracy. This is ev- ident the results: AnomalyR1 achieves 60.93% accuracy, while JUDO shows only a modest improvement 65,00 55.04%. Both remain below the 72.93% and 71.39% reached by recent LLMs such as KL, as well as the base model Qwen2.5-VL. respectively. We attribute Kimi-VL's higher performance to its more advanced visual encoder, although still lacks domain-specialized knowl- edge, as reflected in its relatively low performance on defect-related tasks. Commercial multimodal also show strong performance on anomaly
models such as Gemini-2.5- 2.5-Prr and Gemini-2.5 discrimination, likely due to their highly capable vision encoders.
Even in the base model Qwen2.5-VL, which is not equipped with a stronger vision encoder, we observe a similar performance drop in the base model Qwen2.5-VL. when switching from direct an- swering to reasoning mode. This observation suggests that the degradation not solely attributable to encoder capacity but is instead attributable to the introduction of reasoning itself. Notably, the drop is more pronounced in relatively simple anomaly discrimination settings, a phenomenon that has also been observed in recent studies on related tasks (Liu et al. 2025al Yang al. (025). We provide a more detailed analysis of this phenomenon in Section 4.44 where we examine the impact of our multi-staged learning framework the anomaly discrimination task.

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Table Ablation study of different methods. We denote Stage SegJux, Stage 2 as DomInj, and the full Stage 3 GRPOdom, where the domain reasoning reward used. The segmentation reward is only used in methods that go through Stage training. Average accuracy (%) is reported.
Method
Average
Qwen2.5-VL-7B
72.56
+ GRPO
77.29
+ GRPO + RAG
76.29
+ GRPO + Domlnj
79.82
+ GRPO + SegJux Dominj
80.35
+ GRROdem + SegJux Dominj (JUDO)
81.20
Except for the relatively simple anomaly discrimination task, the performance of both recent open- and closed-source LMMs on domain-level defect reasoning remains overall lower than JUDO's. s. This contrast highlights key distinction: while large commercial LMMs excel broad visual pattern recognition, they do not have sufficient defect semantics or comparative industrial cues in way that generalizes across classification, localization, description, and analysis. In contrast, despite being built on smaller open source backbone, JUDO consistently achieves higher defect-reasonice accuracy because its training explicitly incorporates domain knowledge and visual context. These results demonstrate that domain-aligned training can surpass much larger commercial systems when the task requires specialized industrial reasoning rather than generic visual understanding.
4.3 ABLATION STUDY
We conduct an ablation study to systematically evaluate the contribution of each component in the JUDO framework, with results summarized in Table 2 The study begins with the baseline Qwen2.5-VL-77 model, which achieves average accuracy of 72.56%. We note that this result is obtained without using the Chain-of-Thouse process, not requiring the reasoning process generally in <think> tags, but outputting the answer directly, since it not trained learn the proper reasoning, yet. Applying standard GRPO training stage improves general instruction-folloviin and raises the accuracy to 77.29% the equivalent AnomalyR1. To evaluate the effectiveness of domain knowledge injection, we employ RAG, which dynamically provides external information upon the model trained on vanilla GRPO, referring to the model as (+ GRPO RAG). The way of employing RAG is followed in the MMAD (Jiang et al.) 2025). While RAG-based approaches have been claimed to often improve the performance, the external context attached to our trained reasoning model acted in the negative direction rather than bringing in positive gain. In contrast, impact of domain injection was remarkably effective, resulting an improvement of almost 5% gain.
The effectiveness of JUDO's learning-basce approach becomes evident in the subsequent steps, as shown in the result. The most sig- nificant performance leap comes from Stage 2's Domain Injection (+ GRPO + DomInj),
90
Anomaly Discrimination
Defect (Avg.)
Accuracy (%)
which internalizes domain knowledge through supervised fine-tuning and increases accuracy 79.82%. This result is substantially higher than the RAG-based method, demonstrating the superiority of integrating textual knowledge di- rectly into the model's parameters. Building this, the addition of Stage I's juxtaposed seg- mentation training (+ GRPO + SegJux + Dom- Inj) brings the accuracy to 80.35%, highlight- ing the value of grounding textual reasoning in fine-grained juxtaposed visual analysis. The fi- nal refinement, which uses the complete Stage
70
60
50
Qwen-2.5-Vl.
Qwen-2.5.VL
Stage
Stage
JUDO
Model Training Stage
Figure 2: Performance across different stages.
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Q. There is defect in the object.
Base GRPO Model Response
Where is the defect?
There sppears be visible defect across the entire sanister or .. in good condition without any diffccts.
Query image
Normal Image
Answer: C
Base GRPO RAG Model Response
TI TeoBogrerrssoo
noticeable Indentation or dent discuats the normal eylindrical appearance of containst... on body
JUDO (Ours)
A: On the body of the canister
The defect located at the center the moets body. .. appears as irreglla white area that disrupts the smooth surface.
B: Across the entire canister
C: On the label the canister
D: On the lid of the canister
Answer: D
Figure 3: Response comparison between Base GRPO, Base GRPO RAG and JUDO. The anoma- lous region in the query image is highlighted in green, while the segmentation output from JUDO is represented as red patches in the image next to JUDO's response.
GRPO with specialized domain reasoning reward (+ GRPO achieves the peak performance of 81 81.20%. This confirms that each stage of the JUDO framework provides meaningful and incre- mental benefit, leading to its final sfate-of-the-as art performance.
4.4 DISCUSSION OF MULTI-STAGED LEARNING FRAMEWORK
Figure presents the progression of anomaly discrimination accuracy and averaged accuracy of de- feet subtasks (classification, localization, description, and analysis) across the key checkpoints of our pipeline: the Qwen2.5 V.. baseline, its Chain-of-Thhught Thought (Co'T) variant, and each JUDO stage. The figure shows that the most significant performance shift occurs before any of JUDO's optimization takes place. When the model switches from direct answering to CoT reasoning, anomaly discrimi- nation accuracy drops sharply (71.39% 61. 1,90)), and the averaged accuracy over the four defect subtasks decreases from 65% to 57. 57.00%. This confirms that the initial degradation stems from reasoning-mon mode behavioral change rather than from the design of our multi-stage training. The drop in anomaly discrimination in particular is consistent with a recent finding (Liu et al. (2025a) that ex- plicit verbal reasoning can impair performance on perception-heav tasks by interfering with rapid, pattern-based visual judgments, a phenomenon analogous verbal overshadowing. After entering the pipeline, anomaly-detection performance remains largely stable, with only a small fluctuation from 35.42% (Stage 1+2) to 65.04% in the final model, indicating that catastrophic forgetting is present but limited.
In contrast, the progressive optimization produces substantial and consistent gains across the four defect subtasks. As shown in Figure 2 accuracy increases from 62.58% after Stage 1 to 67.18% after Stage 1+2, and ultimately reaches 80.43% in the full model. This pattern demonstrates that the GRPO-based domain alignment strengthens domain-aware classification, localization, descrip- tion, and analysis, light that JUDO's multi-stage design primarily enhances domain-oriented reasoning capabilities while preserving binary anomaly detection performance. Overall, despite minor drop in vision- oriented anomaly discrimination. JUDO significantly improves performance on reasoning-intensivee defect subtasks, while additionally improving reliability and explainability through localized defect grounding and domain domain-aligned aligned reasoning.
as low-level anomaly discrimination remains stable. These results high-
intensive
4.5 QUALITATIVE ANALYSIS
Figure 3 provides qualitative comparison that highlights the limitations of baseline models and the effectiveness of JUDO's s integrated reasoning. The Base GRPO model exhibits a critical failure, as it is unable to detect the defect and states there is "no visible defect," while paradoxically offering

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Q. There is a defect the object. What the potential effect of the defect?
JUDO (Ours)
Query Image
The defect involves bent pin on image, which deviates from its intended alignment. This contraste with the second image, where all pins are straight and uniformly aligned. misaligned pin may hinder the board's ability connect properly with other components, thereby effecting its overall functionality. Therefore, the correct answer is Reduced functionality.
Incressed power consumpttio
No impast on performance
G: Reduced functionatity
improved performance
Answer: C
Q. there any defect in the object?
JUDO
Query Image
Normal Image
The defect in the first image involves an empty fruit compartment where two fruits are missing. resulting imbalance compared to the normal second image. This discrepency affects the expected arrangement and variety of items in the breakfest Therefore, correct
A: No B: Yes
Answer:
Figure 4: Examples of JUDO's output on the MMAD dataset. The anomalous region in the query image is highlighted in green, while the segmentation output from JUDO is represented as red patches in the image next to JUDO's response.
an incorrect answer ("C"). The Base GRPO RAG model demonstrates a different failure mode rooted contextual distraction. Misled by the retrieved domain snippet, which contains descriptions of multiple potential defects, it latches onto a plausible but incorrect description of a "noticeable indentation or dent"-likely related deformation-inntead This failure to ground the textual context in the visual evidence leads to mislocalization of the "body of the canister" and the wrong answer ("A"). This negative impact is also confirmed by the quantitative results, where the RAG model's accuracy (76.29%) is lower than the Base GRPO model's s (77.29%)
the correct "opened" defect type.
In stark contrast, JUDO leverages its internalized knowledge for successful and coherent analysis. It correctly identifies and locates the defect on the "lid of the canister," provides disually-groundde explanation of "irregular white area," and supports its reasoning with an accurate segmentation map to arrive at the correct answer ("D"). This direct comparison clearly illustrates that JUDO's integrated, learning-based approach is essential for achieving reliable and fine- finegrained reasoning, overcoming the weaknesses of models that either lack domain knowledge or are distracted by it.
5 CONCLUSION
In this work, we presented JUDO, novel framework that addresses the core challenge of aligning domain knowledge in industrial anomaly understanding. By unifying comparative visual reasoning and domain semantics into a domain-aligned learning objective, our approach significantly enhances anomaly understanding capabilities. Experimental results on the MMAD benchmark demonstrate superior performance compared to state-of-the-ard models in defect-reasoning tasks. These findings suggest that internalizing domain knowledge and context during training is not only highly effective but also significantly more beneficial than providing context solely at inference time. This approach, therefore, enables more reliable and accurate multimodal reasoning systems in complex industrial scenarios.
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ACKNOWLEDGEMENTS
This work was supported by the Institute Information and Communications Technology Plan- ning and Evaluation (IIT)) and the National Research Foundation of Korea (NRF) through
grants funded by the Korean government (RS-2024-003330668, RS-2024-00433636, I1TP-2026-RS- 2020-11201821, RS-2019-11194422 -TTP--2225-RS-2222-00066666 25442569, RS RSS-2025-24803183 RS-2025-00222 202-2-020263111))
RS-2025-02218768 RS-2025-
ETHICS STATEMENT
The authors have read and adhered to the ICLR Code of Ethics. This research based on publicly available datasets for industrial anomaly detection (MMAD, REAL-IAD), which contain images of inanimate industrial objects. No human subjects were involved in this study, and no personally identifiable information was used, thereby minimizing privacy concerns. Our work uses GPT-4o for the programmatic generation of structured training data, as detailed in Appendix [ to ensure a consistent and replicable data construction pipeline and grammar correction. The goal of this research is advance industrial quality control and automation, and we do not foresee any direct negative societal impacts or ethical concerns arising from the proposed method.
REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility our work. The complete source code for our framework, including data preprocessing scripts, implementation of all three training stages, and evaluation protocols, is provided in the supplementary materials, accessible via the repository at com/ http://oodavvd11//111mm JUDO The methodology of our three-stage train- ing process is detailed in Section 3 Our experimental setup. including the specific datasets, base- lines, and key implementation details such the base model, libraries, and hyperparameters, is described in Section 4.1 Furthermore, comprehensive description of our programmatic dataset construction pipeline for all training stages can be found in Appendix A We believe these resources provide the necessary details for the research community to reproduce our results.
https://gi
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Domain Knowledge
Domain QA Genersting Process
Stop 1: Generate Domain QA pairs
Step 2:
Paraphrase Domain QA pairs
inspccted plus pamoriy charecterieed concamination enomates contamination piesents irrqultrr disectorsions, protrusions, toxtuice....
visual
Q How visually identily contamination
contamination
in the pilss?
Contariiation indicated by ireqular
A. Signo contamination include abnormal discaloration, raised areas, oruneven textures.
dilcollantions protrusions,, unseen textures.
Figure 5: Stage 2 domain Q&A construction pipeline. Illustration of generating domain domain-speciff specific Q&A pairs from unstructured domain snippets and augmenting them via paraphrasing, using pill contamination defects as an example. The resulting Q&A pairs are paired with normal images to associate textual knowledge with visual context.
A DETAILED DATASET CONSTRUCTION PROCESS
We construct the datasets using GPT-4o within a programmatic pipeline. This design ensures high quality, structural consistency, and scalability across all data. The pipeline consists of three stages: constructing comparative explanations (Stage 1), constructing domain Q&A datasets (Stage 2), and generating pseudo-domain reference reasoning (Stage 3). The detailed process for each stage is described below.
Stage 1: Comparative Explanation. The primary goal of this stage construct dataset that en- ables fine-grained comparative reasoning between anomalous and normal images. The construction process for each data instance is as follows:
Input: Each instance includes query image containing anomalies, its corresponding bi- nary mask, and a randomly selected normal template image from the same object category.
2. Anomaly Patch Generation: The provided anomaly mask is used to identify the precise location of the defect. We overlay 16x16 grid the image, and any grid cell overlap- ping the mask is marked as anomalous. The coordinates of these anomalous patches are then converted into structured textual sequence. This sequence uses (row, col colu))) notation for individual patches and (row, col-start) (row, collend) for con- tiguous horizontal patches. This text string is then encapsulated within <seg> tags (e.g., <seg> (11, 12) (11, 14), (12, 11) </ </s)>>).
3. Comparative Explanation Synthesis: To generate the reasoning text, we first annotate the anomalous query image by using its mask to draw red outline around the defect region. This visually grounded image, along with the normal template image, the defect type, and any relevant domain notes, provided input GPT-4o. The model is prompted act as an expert and generate a concise comparative explanation, describing the visual differ- ences between the two images with focus on the defective region's 's characteristics. This synthetically generated text forms the content for the <think> tags.
4. Final Data Assembly: The final training instance combines the visual and textual compo- nents. It consists of the original anomalous image and the normal template image inputs, paired with the generated dual-part response: the <seg> tag containing the patch coordi- nates, followed by the <think> tag containing the comparative explanation. Figure shows an example of training data for Stage 1.
Stage 2: Domain Q&A. This stage aims to inject category-specific domain knowledge into the model through supervised fine-tuning (Mecklenburg al., (024). The dataset is constructed by converting the unstructured textual knowledge from MMAD into structured Q&A format. The overall process described in Figure
1. Input:
We utilize the "domain snippets" from the MMAD benchmark as the foundational knowledge source. Each snippet describes the characteristies of a specific object cate- gory and its associated defect types. Table 4 shows an example of a domain snippet for "Squeezed Teeth" defect of the object "Zipper".
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Table System prompt for data construction in Stage
Generate unique QA pairs grounded in the snippet with enforced categories.
You are tasked with generating high-quality Q&A pairs grounded strictly in the given snippet.
Rules:
DO NOT mention "snippet" "according to the snippet".
All answers must be strictly grounded in the text, no outside knowledge.
Produce exactly count unique question-answer answer pairs.
Use variety of question styles, including:
Criteria-based (e.g., "What criteria indicate a defect in."""
2. Defect understanding (e.g.. "How does this affect...
Comparative reasoning (e.g.. "What distinguishes X from Y?")
4. Functional impact (e.g.. "Why does this defect matter?")
5. Recognition (e.g.. "What is...?")
6. Quality control reasoning (e.g., "Why is it important detect... ?")
7. Aesthetic/struucuuaal concerns
Ensure balance: include multiple styles, not just one.
Keep answers factual and strictly tied to the snippet. Return as JSON array: ("quustion". "..", "answer": "..."]] Snippet: ( [snippet]
Table 4: Example of a domain snippet for "Squeezed Teeth" defect of the object "Zipper".
The defect manifests as teeth of the zipper that appear squeezed or compressed together, disrupting the otherwise uniform alignment and spacing of the zipper teeth. This irregu- larity can occur at various locations along the zipper, often concentrated in the central or top/bottom areas. Characteristics of the defect include noticeable distortion in the shape of the teeth, which appear pinched and may vary in spacing compared to the adjacent, properly aligned teeth. The presence of squeezed teeth could potentially hinder the func- tionality of the zipper, leading to difficulties in smooth opening and closing or causing the zipper slider to get stuck during use. The affected area is identifiable by its misshapen and irregular appearance, contrasting with the uniform and symmetrical pattern of the healthy zipper teeth surrounding it.
2. Initial Q&A Generation: For each domain snippet, we employ GPT-4o to generate an initial set of 30 unique question-answer pairs. The generation is guided by prompt that instructs the model formulate questions reflecting industrial inspection knowledge, such as inquiries about defect criteria, visual appearance, functional impact. All answers are strictly grounded in the provided text snippet, without introducing external knowledge.
3. Paraphrasing for Robustness: To enhance the model's robustness and prevent overfitting on specific phrasing, each of the initial 30 Q&A pairs is further paraphrased. We use GPT-4o again to generate two semantically identical but lexically diverse paraphrases for each pair. This step expands the dataset by introducing varied phrasings of the same core knowledge.
4. Final Data Assembly: The final dataset consists of the original and paraphrased Q&A pairs. To anchor this textual knowledge to a visual context, each Q&A instance is paired with a randomly sampled normal image from the corresponding object category. This encourages the model to associate the domain knowledge with the visual appearance of the object, facilitating better knowledge recall during multimodal reasoning tasks. Figure shows examples of the final Q&A pairs generated from the domain snippets for Stage 2.
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B
DATASET EXAMPLES
B.1 STAGE
Query Image
Normal Template Image
(3,7= (4,7)-(4,8), (5,8), (6.8)</seg2<thinkkkhh first image shows component with defect where one of the metal pins bent out alignment, leaning one side. In contrast, the second image displays normal component with all pins straight, indicating proper bligmment<</hhnke
Query Image
Normal Template Image
<seg> (10,4), (11,4)-(11,5) </seg> <think> first image shows cylindrical object with a defect. This defect appears as a reddish discoloration, suggesting possible surface damage. In contrast, the second image shows smooth surface without any irregularities, indicating normal condition. </think>
Query Image
Normal Template Image
<seg> (11,13), (12,12) (13,10)-(13,12) (14,8)-(14,11)) 11)
</seg> <think The first image shows a roll of tape with a defect on its outer surface. The defect appears disrupting the smooth surface seen in the second image. The second image shows without any irregularities. </think>
irregular line,
Figure 6: Stage 1 training dataset examples
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B.2 STAGE 2
Original Q: What might cause fabric border fraying in zippers? A: Fabric border fraying in zippers might be caused by improper stitching or material degradation.
Paraphrased Q-1: What are possible reasons for fabric border fraying zippers?
A: Possible reasons for fabric border fraying in zippers
include improper stitching or material degradation.
Paraphrased Q-2:
Why might fabric border fray in a zipper?
A: Fabric border might fray in zipper due to improper stitching or material degradation.
Original Q: What ensures the pasta is of standard quality? A: The well-defined ridges on the exterior help indicate that the pasta is of standard quality.
Paraphrased Q-1: How the quality pasta indicated? A: The quality of the pasta is indicated by the well-defined ridges on its exterior.
Paraphrased Q-2: What feature suggests the pasta standard quality?
A: The feature suggesting the pasta standard quality
the well-defined ridges its exterior.
Figure 7: Stage 2 training dataset examples
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C
ADDITIONAL JUDO RESPONSES
JUDO (Oura)
there any defect object?
Query Image
Image
prominent that splits the surface into three uneven sections. This crack characterüed by sharp edges and dark which
image
the spackled contrwet, eecond lmnage displays a uniform speckled pattern without any visible crecks iregularties.
Therefore, the the central crack in the first image
Therafore, correct answer
Anewer:
Q. There is a defecc the object. What the appearance of the defect?
JUDO (Ours)
Query Image
Narmal
The defect the first image shows a jagged and irregular opening where the shell has been broken or damaged. This contrasts with intact second image, which displays smooth without any visible cracks or openings. Therefore, correct answer jagged end Irregular opening
surface
smooth dot-lkke C: jagged and Irrauuarr opening,
D: series of fine lines.
Answer:
(Ours)
Q. Is there any defect in the object?
Query Image
Normel Image
The first image shows macaroni pleces with minor surface scratches abrasions, which are typical manufocturing not common and do not affect the quality. The second image displays macaroni pieces w without visible imperlections, maintaining
imperfections defects.
Therefore, detect image characterized these marks rather than structural anomalies. Therefore, the correct A.
A: No B: Yes
Answer:
Q. There is defect in the object. What is the appearance of the defect?
JUDO (Ours)
Query Image
Normal Image
The defect appears es an empty space where multiple pushpins expected that these compartments are devoid of the usual contenta, This contrasts with the normal Image, each compartment Thercfore, the correct answer D.
indicating
pushpin.
Pushpin broken
Pushpin
C: Pushpin with bent head Empty spece where pustipin whould be
Answer:
Figure 8: Examples of JUDO's output the MMAD dataset. The anomalous region in the query image is highlighted in green, while the segmentation output from JUDO is represented as red patches in the segmented image.
19


Reviews:
[{"review_id": "sb0APqwSJX", "reviewer": "Reviewer_wrJU", "summary": "1. The paper presents JUDO (Juxtaposed Domain-Oriented Multi-modal Reasoner), a framework designed to enhance industrial anomaly detection by integrating domain-specific knowledge and contextual reasoning into large multimodal models (LMMs).\n2. JUDO performs visual comparative reasoning by juxtaposing defect and normal images for fine-grained inspection, and injects domain expertise through supervised fine-tuning followed by reinforcement learning (GRPO) with tailored rewards.\n3. Experiments on the MMAD benchmark show that JUDO outperforms strong baselines such as Qwen2.5-VL-7B and GPT-4o, highlighting the value of domain-oriented reasoning for anomaly understanding.", "questions": "1. Dataset Scale and Composition:\nHow many QA pairs were generated for the “general inspection knowledge” subset? It would be useful to know the relative proportion of these conceptual QA pairs compared to those derived directly from domain snippets in MMAD.\n\n2. Use of External Knowledge in Closed-Source Models:\nGiven that open-source VLMs still lag slightly behind but demonstrate strong reasoning capabilities, could the proposed “general inspection knowledge” QA corpus be used as external prompt knowledge for closed-source models such as GPT-4o, GPT-5, or Gemini 2.5 Pro?\nHow would this prompt-based knowledge injection compare to the performance of the proposed fine-tuned JUDO model in terms of reasoning accuracy and interpretability?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Novel Multistage Training Framework:\nThe paper introduces a well-structured two-stage pipeline combining supervised fine-tuning (SFT) and reinforcement learning (GRPO), effectively aligning domain-specific knowledge with multimodal reasoning objectives.\n\n2. Conceptual Contribution in Knowledge Integration:\nIt provides a meaningful conceptual advancement in how to inject out-of-domain or domain-specific knowledge into large multimodal language models, enabling more accurate and interpretable reasoning for industrial anomaly understanding.", "weaknesses": "1. Clarity and Presentation:\nThe paper’s presentation is somewhat difficult to follow, particularly in the implementation and dataset construction sections.\nFor example, in Lines 216–224, it would be helpful to include a concrete example comparing a QA pair derived directly from the domain snippet versus one generated for general inspection knowledge, to clearly illustrate their differences.\n\n2. Incomplete Baseline Comparison:\nThe experimental section lacks evaluation against recent state-of-the-art vision–language models (VLMs) such as Gemini 2.5 Pro, which would provide a stronger and more convincing benchmark for JUDO’s effectiveness.", "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": "0k2xRezC0N", "reviewer": "Reviewer_ixi3", "summary": "This paper presents JUDO, a Juxtaposed Domain-Oriented Multimodal Reasoner, to address the current weaknesses in industrial anomaly detection using LMMs (Large Multimodal Models). While the GRPO-based LMMs are optimized for tasks grounded in general knowledge, pinpointing the defects in industrial setups requires superior domain knowledge and visual context through a three-stage progressive framework. The framework uses juxtaposed segmentation learning, supervised fine-tuning (SFT) that injects domain-specific knowledge, and reinforcement learning (GRPO) with tailored multi-component rewards for domain-aware reasoning, resulting in an average accuracy of 80.73% and improvement in explainability through domain-aligned, visually grounded reasoning.", "questions": "1. Could the authors provide an ablation comparing different reasoning styles – (a) direct answer (no reasoning), (b) free-form Chain-of-Thought, and the proposed structured `<seg>/<think>/<answer>` reasoning – to quantify the trade-offs between interpretability and task accuracy?\n\n2. JUDO underperforms compared to general-purpose models in binary anomaly discrimination. Could the authors clarify whether it is due to catastrophic forgetting, visual encoder limitations, or reinforcement objectives prioritizing reasoning quality over detection precision?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The three-stage learning framework proposed in the paper is logical and helps advance the integration of domain knowledge into multimodal reasoning. The three-stage progressive learning pipeline (SegJux, DomInj, GRPOdom) effectively combines visual segmentation, textual domain knowledge, and reinforcement learning. This hierarchical design is both conceptually strong and technically sound, marking a meaningful step beyond conventional GRPO-based post-training methods.\n\n2. The visual grounding via segmentation and the domain-aligned textual reasoning make outputs transparent and practical for industrial use. By segmenting the input images into semantically meaningful regions (e.g., defect areas, product components, the model anchors its subsequent textual explanations to specific visual evidence. This ensures that every reasoning step is traceable to an observable region within the image, reducing errors and improving trustworthiness.\n\n3. This paper addresses a significant industrial challenge of creating an automated, explainable anomaly detection system. The findings have potential applicability across various industry segments like manufacturing, logistics, etc.\n\n4. State-of-the-art average on MMAD versus both general LMMs and AnomalyR1; ablations demonstrate additive benefits of each stage.", "weaknesses": "1. Compared with other general-purpose models, JUDO lags in binary anomaly detection tasks. JUDO’s score (64.51%) in simple anomaly-vs-normal classification tasks is lower than that of general-purpose VLMs such as Kimi-VL (72.93%). Although the paper claims this can be attributed to the advanced vision encoder of Kimi-VL, this could also suggest that the model’s complex reasoning optimization may not translate well to simpler binary tasks.\n\n2. Reasoning mode not systematically evaluated: The paper argues that free-form CoT can hurt MMAD accuracy, thus opting for a constrained `<seg>/<think>/<answer>`  format with reward shaping. However, the trade-offs among no reasoning, free CoT, and structured reasoning are not empirically studied. A controlled comparison would clarify why structured reasoning helps and when CoT hurts, strengthening claims.\n\n3. The paper does acknowledge that sequential training across three stages can cause performance degradation in earlier-learned tasks. Although addressed to some extent, this issue remains a known weakness of multi-phase fine-tuning and may affect scalability to more diverse industrial domains.", "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": "M3HQ60nzuN", "reviewer": "Reviewer_mhqc", "summary": "This paper proposes JUDO, a multimodal reasoning framework for industrial anomaly understanding. The method integrates visual comparative segmentation, domain knowledge injection via supervised QA fine-tuning, and domain-oriented GRPO alignment to improve both defect localization and explanation quality.", "questions": "1. My interpretation is that the method essentially fine-tunes an existing VLM on domain-specific multimodal data, achieving better performance due to domain alignment. Could the authors clarify what they consider the core novelty beyond a well-designed training pipeline?\n2. The paper argues that GPT-based multimodal models lack sufficient industrial anomaly reasoning capability. However, GPT-4o is still used as the source of \"golden reasoning\" in the training pipeline. How do the authors justify the reliability and consistency of GPT-generated reasoning as ground truth in a domain where GPT is claimed to be insufficient? More specifically, how do you ensure the reasoning produced is correct and not hallucinated?\n\nIf I have misunderstood the intended contribution or positioning, I would appreciate clarification from the authors.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The problem is well-motivated, addressing the domain knowledge gap that limits existing VLMs in industrial anomaly analysis. \n2. The three-stage training pipeline is clear, systematic, and practically meaningful. The incorporation of juxtaposed segmentation grounding enhances fine-grained visual reasoning, while domain QA fine-tuning contributes to more reliable textual analysis. \n3. The paper is clearly structured and the experimental results are presented in a well-organized manner.", "weaknesses": "1. The proposed framework is mainly an integration of existing components—comparative segmentation, supervised domain knowledge tuning, and GRPO-based post-training—and thus reads more as pipeline engineering rather than introducing a fundamentally new reasoning mechanism or architectural innovation. The methodological novelty therefore appears limited.\n2. The model depends on GPT-4o–generated reasoning chains as pseudo ground truth, yet the paper does not evaluate whether these rationales are reliable, consistent, or susceptible to hallucination. This creates a conceptual gap, since the paper also claims that general LMMs (including GPT models) lack domain reasoning capability.\n3. The paper attributes the performance degradation in anomaly discrimination to catastrophic forgetting but provides no empirical analysis or mitigation. Without verification or intervention, this explanation remains speculative and raises concerns about the robustness of the multi-stage training pipeline.", "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": "edtKcKEPg1", "reviewer": "Reviewer_EjVp", "summary": "This paper focuses on multi-modal anomaly reasoning via large-scale multi-modal language model. Specifically, it considers how to make the multi-modal language model be more aware of the domain-specific anomaly. To achieve this, it proposes a three-stage model training/post-training pipeline by considering the anomaly discrimination of normal and abnormal cases, the supervised instruction tuning and reinforcement-learning-based post training. The proposed method acgieves state of the art perfroance of anomaly reasoning on the MMAD benchmark.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed method is well-motivated, seeking to enhance domain-specific anomaly understanding by injecting fine-grained anomaly knowledge throughout the training stages of a large multi-modal language model.\n\n- The proposed three-stage training pipeline, which integrates both visual and linguistic anomaly data, is logically sound and effectively develops strong anomaly reasoning capabilities.\n\n- The reward engineering implemented in the reinforcement-based post-training stage is well-designed and appropriate for the task.\n\n- The paper is well-written and easy to follow, presenting a clear and logical flow of ideas.", "weaknesses": "- The methodological contribution appears limited, as the proposed framework primarily involves a sequential application of standard techniques (instruction-based fine-tuning and reinforcement learning-based post-training). While effective, this approach offers fewer novel insights into the fundamental challenges of injecting anomaly knowledge into multi-modal language models.\n\n- The proposed method demonstrates a significant performance degradation in anomaly discrimination compared to the Qwen-2.5 baseline. The paper offers several hypotheses for this outcome in the analysis section, but these are not experimentally validated, which undermines their credibility. A more rigorous analysis, such as an ablation study, is needed to clarify which training stage introduces the apparent trade-off between anomaly detection and anomaly reasoning capabilities.\n\n- The manuscript lacks a detailed discussion of the training data division and statistics, which are critical for evaluating the effectiveness of the post-training stage. Furthermore, the paper's central claim—that injecting domain knowledge during training is superior to doing so at test-time—is not substantiated with experimental evidence. A direct comparison is needed to validate this assertion.\n\n- The comparison to general-purpose LLMs could be strengthened. While the inclusion of GPT-4o is a good start, this baseline is rapidly becoming dated. To provide a more current and comprehensive understanding of the performance landscape, it would be beneficial to include comparisons with more recent state-of-the-art commercial models (e.g., Claude 3, Gemini 1.5).", "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)