Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thought
Authors: Chao Huang
Benfeng Wang
Wei Wang
Jie Wen
Chengliang Liu
Li Shen
Xiaochun Cao
Venue: NeurIPS 2025 poster ()
Keywords: video anomaly reasoning; chain-of-thought; multimodal large languge model
Primary area: applications

Abstract: Recent advancements in reasoning capability of Multimodal Large Language Models (MLLMs) demonstrate its effectiveness in tackling complex visual tasks. However, existing MLLM-based Video Anomaly Detection (VAD) methods remain limited to shallow anomaly descriptions without deep reasoning. In this paper, we propose a new task named Video Anomaly Reasoning (VAR), which aims to enable deep analysis and understanding of anomalies in the video by requiring MLLMs to think explicitly before answering. To this end, we propose Vad-R1, an end-to-end MLLM-based framework for VAR. Specifically, we design a Perception-to-Cognition Chain-of-Thought (P2C-CoT) that simulates the human process of recognizing anomalies, guiding the MLLMs to reason about anomalies step-by-step. Based on the structured P2C-CoT, we construct Vad-Reasoning, a dedicated dataset for VAR. Furthermore, we propose an improved reinforcement learning algorithm AVA-GRPO, which explicitly incentivizes the anomaly reasoning capability of MLLMs through a self-verification mechanism with limited annotations. Experimental results demonstrate that Vad-R1 achieves superior performance, outperforming both open-source and proprietary models on VAD and VAR tasks.

Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Connitton Chain-of-Thought
Chao Huang'
Benfeng Wang'
Wei Wang!* Jie Wen
Chengliang Liu?
Li Shen Xiaochun Cao
Shenzhen Campus of Sun Yat-sen University Parrbi Institute of Technology. Shenzhen
31 Labboraoory for Artificial Intelligence in Design, The Hong Kong Polytechnic University
*Shenzhen Loop Area Institute
fhuangch253, wangwei29, caoxiaochun) Emacll ayau. waw..adu.ua........ venjiedhit. edu.cn 1iuc119960163.cm mathshenli@gail..om
edu.cn cn wangbf230mai12.spg syw...du..uu...............u.... edu.
Abstract
Recent advancements in reasoning capability of Multimodal Large Language Models (MLLMs) demonstrate its effectiveness in tackling complex visual tasks. However, existing MLLM-based Video Anomaly Detection (VAD) methods remain limited to shallow anomaly descriptions without deep reasoning. In this paper, we propose new task named Video Anomaly Reasoning (VAR), which aims enable deep analysis and understanding of anomalies in the video by requiring MILMs to think explicitly before answering. To this end, we propose Vad-R1, an end-to-end MI.LM M-baeedd framework for VAR. Specifically, we design Perception- to-Cognition Chain-of-Thought (P2C-CoT) that simulates the human process of recognizing anomalies, guiding the MILMSs to reason about anomalies step-by-step. Based on the structured P2C-CoTT, we construct Vad- bad-Reasnnnng, a dedicated dataset for VAR. Furthermore,
propose an improved reinforcement learning algorithm AVA-GRPO, which explicitly incentivizes the anomaly reasoning capability MLLMs through self ceff-verification mechanism with limited annotations. Exper- imental results demonstrate that Vad-RI achieves superior performance, outper- forming both open-source and proprietary models VAD and VAR tasks. Codes and datasets will released at https: http::///iitti /gtggtt......... wtwm///wwoodrrfuuuuuuoo
Introduction
Video Anomaly Detection (VAD) focuses on identifying abnormal events in videos, and has been widely applied in range of domains like surveillance systems [53] and autonomous driving [39, 79]. Traditional VAD methods typically fall into two paradigms: semi-supervised and weakly-supervised VADs. The semi-supervised VAD methods [79, 34, 20, 36, 19, 17, 22] aim modeling the features of normal events, while there are only video-level annotations available for weakly- seakkly-upervised VAD methods [71, 53, 18, 17, 86, 26, 21, 70]. With the development of vision-language models, some studies introduce semantic information into VAD [64, 73, 72, 80, 7, 23]. However, traditional VAD methods only remain the level of detection, lacking understanding and explanation of anomalies.
Recently, the reasoning capability large language models has emerged as key frontier [44, 9, 58). Unlike daily dialogue, reasoning requires models to think before answering, enabling them to perform causal analysis and further understanding. In particular, DeepSeck- RI demonstrates the effectiveness of Reinforcement Learning (RL) in stimulating and refining reasoning capability [9]. Concurrently, parallel efforts have begun to extend reasoning to the multimodal domain [57, 60].
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

(a)
Perception- Coopppiiont--oogiiion Chain Thought
(c) Training
Stage1: Supervised Fine- Tuning
Perception
Cognition
Answer
Teaching
Annotations
Reasoning
C Giobal
Which
Stage2: Reinforcement Learning
Shallw
Snd Deep
Why
-eff-Inceniivizinn
Week Labels
Vidco-leve
Anomaly Verification Augmented GRPO
(b) Datoset
Output
(d) Performance
Vad-R1
VideoLCM
OpenSORA
Inpul
Enirumer
M.ST2
SORA
PrompT
abnornai event video and think before arswering.
Avenue
Pod2
Video
Fine-Grained Anomaly Categories
Vod-R1 (Ours) HelmesVAU HelnesVAD HAWK
Figure I: Overview of Vad-R1. Vad-RI is an end-to-end framework for video anomaly reasoning. A structured Perception- Prreppion-to-Cogmitton Cogmition Srain-of-Thhughee is introduced to guide Vad-R1 in performing
step-by-step reasoning. Based on the structured CoT, new dataset for video anomaly reasoning constructed, including fine-grained anomaly categories. Then, A two-stage training pipeline adopted to progressively enhance the reasoning capability of Vad-R1. Finally, Vad-R1 outperforms existing MLL.s-basee VAD methods with a great margin on VANE benchmark.
Despite the growing interest in reasoning capability, existing Multimodal Large Language Models (MLLMs) based VAD methods still fall short in this regard. Those methods can be divided into two categories based the role of MILMs. Some methods regard MILMs auxiliary modules [38, 88, 89, 11], where MLLMs provide supplementary explanation after the classifier predicts the anomaly confidence. In this context, anomaly understanding step after detection, and the output of MLLMs does not directly promote anomaly detection. Subsequently, although some methods utilize MLLMs directly perform anomaly detection and understanding [54, 40, 77, 83, 13, 12], MLLMS only generate anomaly descriptions perform simple anomaly question answering based on video content, lacking thinking and analytical abilities. Thus, reasoning remains underexplored in VAD.
To bridge this gap, we propose a new task: Video Anomaly Reasoning (VAR), which aims to empower MLLMs with the ability to perform structured, step-by-step reasoning about anomalous events in the videos. Compared with existing video anomaly detection or understanding tasks, VAR targets a deeper level of analysis by mimicking the human cognitive process, enabling contextual understanding, behavior interpretation, and norm violation analysis. To this end, we propose Vad-R1, the first end-to-end MLLM-based framework for VAR, which explicitly performs reasoning before generating its response. However, enabling reasoning in video anomaly tasks presents two major challenges. Firstly, existing VAD datasets lack structured reasoning annotations, making them unsuitable for training and evaluating anomaly reasoning models. Secondly, how to effectively train models to acquire reasoning capability remains an open challenge. Unlike tasks with clearly defined objectives, open-ended VAR requires models to perform multi-step causal reasoning, making it difficult to define clear training objectives and directly guide the reasoning process.
For the first challenge, we design a structured Berceptiontto-oognitie Chain-of-Thought (P2C- CoT) for video anomaly reasoning, as shown in Figure ((a). Inspired by the process of human understanding the anomalies in the vidcos, the proposed P2C-CoT first guides the model to perceive from the global environment of the video to the suspicious clips of the video. After perception, the model will make cognition based on visual clues from shallow to deep level. Finally, the model gives the analysis result answer, including the anomaly category, the anomaly description, the temporal range of anomaly, the approximate spatial position of the anomaly and so on. Then based on the CoT, we construct had-Reesnnnng, a specially designed dataset for VAR, which includes fine-grained anomaly categories as shown in Figure (bb). Vad-Reasoning consists of two complementary subsets. One subset contains videos with P2C-CoT annotations, which are generated by proprietary models step-by-step. The other subset contains larger number of videos, where there only video-level

---

weak labels available due to high annotation costs. For the second challenge, inspired by the success of DeepSeck-RI, we propose a training pipeline with two stages as shown in Figure I(c). In the first stage, Supervised Fine-Tunnig (SFT) is performed to equip the base MILMM with fundamental anomaly reasoning capability. In the second stage, RI. is employed to further incentivize the reasoning capability with the proposed Anomaly Verification Augmented Group Relative Policy Optimization (AVA-GRPO) algorithm, an extension of original GRPO [50] specifically designed for VAR. During RL training, the model first generates group of completions. Based on these completions, the original videos are temporally trimmed and the trimmed videos are then fed back to the model to generate completions. The two sets of completions are subsequently compared, and additional anomaly verification reward is assigned if a predefined condition is satisfied. Finally, AVA-GRPO promotes MLLM's 's video anomaly reasoning capability through this self- velfffeeiiinntion mechanism with limited annotations. In summary, the contributions of this paper are threefold:
We propose a new task named Video Anomaly Reasoning (VAR), which extends traditional VAD from surface-level recognition to deeper cognitive understanding. To this end, develop Vad-R1, a novel end-to-end MLLM-based framework tailored for VAR, which aims at further analysis and understanding of anomalies in the video.
We design structured Dnreptiin-to-Cognnii performing step-by-step structured reasoning. Building upon this paradigm, we construct Rad-Reasoning. specially designed dataset for video anomaly reasoning with two subsets. Furthermore, we propose an improved reinforcement learning algorithm AVA-GRPO, which incentivizes the reasoning capability of MLLMs through self-verification way.
Mhain-of-Thoughh guide the model in
The experimental results demonstrate that the proposed Vad-R1 achieves consistently su- perior performance across multiple evaluation scenarios, surpassing both open-source and proprictary models in video anomaly detection and reasoning tasks.
2 Related Works
Video Anomaly Detection and Dataset Video anomaly detection aims at localizing the abnormal events in the videos. Based on the training data, traditional VAD methods typically fall into two paradigms, the semi-supervised VAD [79, 34, 20, 36, 19, 17, 48] and weakly supervised VAD [71, 53, 18, 17, 26, 21]. Furthermore, some studies try to introduce text description to enhance detection [64, 73, 72, 80, 7, 8]. Recently, there has been growing interest in integrating MLLMs into VAD to improve understanding and explanation [38, 54, 40, 77, 83, 88, 89, 11, 13, 12]. However, current studies remain at shallow understanding with MI.LMs, lacking in-depth exploration of reasoning capability. In this paper, we propose an end-to-end framework to explore the enhancement of reasoning capability for video anomaly tasks.
Furthermore, the existing VAD datasets primarily provide coarse-grained category labels [53, 71, 39, 1] or abnormal event description [13, 12, 54, 82], lacking annotation of reasoning process. To address this gap, we propose structured Verception-oo-Coniition Cognition Chain-of- Chain-of-Thought and a dataset specially designed for video anomaly reasoning, providing byep-by-tep CoT annotations.
Video Multimodal Large Language Model The video multimodal large models provide an interactive way to understand video content. Early works integrate visual encoders into large language models by aligning visual and textual tokens via mapping networks adapter layers [27, 32, 41, 87, 91]. Compared with static images, videos inherently contain rich temporal dynamics and redundant visual information. To address this, some studies explore token compression mechanism to handle longer contexts and reduce computational overhead [31, 76, 90, 25]. In addition, recent works have explored online video stream understanding [6, 10, 78, 74J. Nevertheless, these methods remain at the level of video understanding and lack exploration of reasoning capability.
Multimodal Large Language Model with Reasoning Capability Enhancing the reasoning capability of MLLMs has become a major research focus. Early studies have explored multi- stage reasoning frameworks and large-scale CoT datasets to enhance the reasoning capability of MLLMS [75, 63, 35]. Recently, DeepSeek-R1 [9] demonstrates the potential of reinforcement learn- ing in enhancing the reasoning capability, inspiring subsequent efforts to reproduce its success in multimodal domains [24, 84]. In the field of video understanding, several studies also utilize RL
3

---

P
Reasoning Stage 1: Perception
Reasoning Stage 2: Cognition
Step 1:
Holistic observation of whole video
Step 3: Shallow Cognitiin
Judgement explonation for anomaly
Global Perception
Scene
Objects
Cognize the phenomenio
Explanation
Anomaly Recognition
Perceive whole video
Environment
Video
Visual Cues
Spetial Location
Abnormal Event
Description
cateyory
Anomaly
Step 2:
Step 4:
Local Perception
Abnormal Event
Spatial Location
Deep Cognition
Violation of Norms
Perceive
Description
Cognize the essence
Consequences
suspicious video clips
Observation of suspicious clips
Further understanding for anomaly
(a) Illustration of the proposed structured Chain-of- Chain-of-Thought, including two stages: perception and cognition.
Vad- bad-Roasnnnig Dataset
Event Description
What
Answer
Potential Influence
Vad- Redd-Reaccnin--ie (for RL troining)
Vod Recsoning SFT (for SFT training) 1755 videos
Time Ronge
When
Anomaly
Where
Spatial Location
6448 videos
Category
Which
Why
Reason
Week
Video-level
Test
Reasoning Annotations
(b) Illustration of the answer after reasoning.
(c) The arrangement of Vad-Reasoning dataset.
Figure 2: Overview of the proposed Perception-to- Co-Connitinn CoT and Vad-R Vad-Reasoning dataset.
to improve spatial reasoning [30], temporal reasoning [68] and general causal reasoning [14, 92]. Building upon these advances, this paper focuses on the video anomaly reasoning task.
3 Method: Vad-R1
Overview In this section, we introduce Vad-R1, a novel end-to-end MLLM-based framework for VAR. The reasoning capability of Vad-R1 is derived from two-stage training strategy: SFT with high quality CoT annotated videos and RL based on AVA-GRPO algorithm. We begin by introducing the proposed P2C-CoT in Section 3.1. Based on the P2C-CoT, we construct Vad-Reasoning, new dataset as detailed in Section 3.2. Then, we introduce the improved RL algorithm AVA-GRPO in Section 3.3. Finally, we introduce the training pipeline of Vad-R1 in Section 3.4.
3.1 Perception-to-CConittoo Chain-of-Thought
When humans interpret a video, they typically first observe the events that occur in the video, and then develop a deeper understanding based visual observation. Motivated by this, we design a structured Perception-to-Cogniie
Chain-of-Thought (P2C-CoT) for video anomaly reasoning, which gradually transitions from Perception to Cognition consisting of 2 stages with 4 steps as shown in Figure 2(a), and concludes with a concise answer as shown in Figure 2(b).
Perception When watching video, humans typically begin with holistic observation of the scene and environment, and then shift attention to specific objects or events that appear abnormal. In line with this pattern. the perception stage of the proposed P2C-CoT reflects a transition from global observation to focused local observation. The model initially focuses on the whole environment, describes the scenes and recognizes the objects in the video. This step requires the model to have a comprehensive understanding of the normality in the video. Building upon this holistic understanding of the normality, the model then focuses on the events that deviate from the established normality, identifies what happens, when and where the event happens.
Cognition After observing the video content, humans typically identify abnormal events based on visual cues, and then proceed to reason about the potential consequences. Similarly, the cognitive stage of the proposed P2C-CoT reflects a progression from shallow cognition to deep cognition. The model first assesses the abnormality of the event and explains why it is considered anomalous with

---

Stage 1: SFT with Reasoning Annotations
Stage AVA-GRPO with Video-level Weak Labels
Step Generating Completions
Completion
Untrimmed Video
Completion
Policy Model
Group of Completions
MILMM UpdaTe
Untrimmed Video
Prompt
Prompt
Step 2: Computing Rewards
If satisfied
R ==
Farform Anamaly Reasicning
V
Completion
1
(3) Format Reward
c/orsarr
Ebel
Completion
dhitte video dtthiie grsworoThe anomaly occurred video Fighring.. physical altercation. slaraberr:
Prediction
Extract
(2) Accuracy Reward
Prediction v.s. Label
zome:
Else:
Anomaly Vinaication Reward
Original
New
Toker Prediction Loss
Prediction
Prompt
If- Alnorrmal
Nermo
Reasoning Annotation
shows </thinlo
Abnormal
Video with, Abrormal Segmant Discarded
prediction
right 0.5
The anomaly category Fighting..
If: Norndl
Aonormall
Normal
Video with Beginnin Ending
Segment Discorded
Model
Policy
francal
model just
== .0.2
Prompt
whether the video abnorncl
Step 3: Computing KL Divergence
Step 4: Computing Loss
gunurate mezzooning procesti and onaher encinsed -thinl... w/tthnio and wwnswer...oonseee.. toge...
normol, think beffors
Palicy Model
Sum
Complettoo 1
Legits
Maaan
Compiettoon
Normalizz:
D
Stc
Tranable
Rewards
D
KL Divergence
4 Advantage
Group of Completions
Palicy Model
Update
Reference
Frezen
(aatn
[Sia]
Meon A Standardd Deviation Rewordds
Model
Loss X
Figure 3: Illustration of the two-stage training pipeline for Vad-R1. Stage enables the model to acquire basic reasoning capability with CoT annotated video. Stage further enhances the model'ss reasoning capability through reinforcement learning.
relevant visual signals. It then engages in higher-level cogmition to reason the underlying causes, the violated social expectations, and the possible consequences of the abnormal event.
Answer As shown in Figure 2(b), after completing the reasoning process, the model is expected to provide a concise summary of its judgment about the given video. The final answer consists of several key dimensions related to the anomaly, including category (Which), description of the event (What), spatio-temporal localization (When & Where), the reason Why it is identified as an anomaly and the potential influence (How). Notably, for normal videos, the corresponding P2C-CoT is simplified into two steps. Please refer to Appendix B for more details.
3.2 Dataset: Bad-Reasnning
The cxisting VAD datasets generally lack the annotation of reasoning process. To construct a more suitable dataset for VAR, we take the following two aspects into consideration. On the one hand, we aim for the proposed dataset to cover a wide range of real-life scenarios. Similar to HAWK [54], we collect videos from current VAD datasets. The video scenarios include crimes under surveillance (UCF-Crime [53]), violent events under camera (XD- Violence [71]), traffic (TAD [39]), campus (ShanghaiTecch [34]) and city (UBnormal [1]). Besides, we also collect videos from ECVA [12), a multi-scene benchmark. On the other hand, we strive to broaden the coverage of anomaly categories. To this end, define taxonomy of anomalies comprising three main types: Human Activity Anomaly, Environments Anomaly, and Objects Anomaly. Each type is categorized into several main categories, which are further divided into fine-grained subcategories. Then, we collect additional videos from the internet based on the existing dataset to expand the categories of anomalies. In total, the proposed Rad-Reasoning dataset contains 8203 videos for training and 438 videos for test. As shown in Figure 2(c), the training set of Tad-Reasoning is split into two subsets: Vad-Ree Mid-Reasoning-a SFT which contains 1755 videos annotated with high- high-quality reasoning process, and Vad Vad-Reasoning- RL which contains 6448 videos with video-level weak labels.
Video Collection
To construct the proposed Vad-Reasoning dataset, we design a multi-stage annotation pipeline with two proprietary models Qwen-Max [59] and Qwen-VL-Max [61]. In order to ensure that the P2C-CoT annotation covers all key information in the video, we follow the principle of high frame information density [81]. Specifically, we first prompt Qwen- VL-Max to generate dense
Annotation
5

---

description of video frames. These frame-level descriptions are then fed into Qwen-Max to generate the CoT step-by-step with different prompts. Please refer to Appendix for more details.
3.3 AVA-GRPO
The original GRPO shows great effectiveness in text-based reasoning tasks. However, as mentioned above, the multimodal tasks like VAR are inherently more complex. In addition, there are only video-level weak labels available for RL stage due to high annotation costs, making it difficult to evaluate output quality based solely on accuracy and format reward. To address this challenge. we propose Anomaly Verification Augmented GRPO (AVA-GRPO) which introduces an additional reward through self-verification mechanism, as illustrated in the right part of Figure 3.
Overview of GRPO We begin by reviewing the original GRPO [50]. GRPO discards the value model and aims maximizing the relative advantages among a group of generated answers. Given a question g. the model will first generate a group of candidate completions O = {or jo Subsequently, a set of rewards R = {ra)a- are computed based on the predefined reward functions. The rewards
then normalized within the group to compute the relative advantages
r3 mean R
A
std (t)
where A, is the advantage score of oj, which provides more effective assessment of both individual answer quality and relative comparisons within the group. What's more, to prevent the current policy my from drifting excessively from the reference one Tref, GRPO introduces KL divergence regularization term. The final objective function of GRPO is formulated as
C
To(o; 9) Taaa (o, [g)
Tolo, 9)) e,, 1 1+ A1)
(G) = (g,0))
Σ
min
Ad, clip
Tew (o; g)
DKL mred) )
(2)
where the ratio selod]y] Tewa (o:1g) quantifies the relative change between the current policy and the old one, and the clip (., - 1 E) operation constrains the ratio within a range.
Anomaly Verification Reward GRPO replaces the value model with group relative scores, reducing the memory usage and training time. However, simple accuracy and format rewards are insufficient to evaluate the quality of answers for video anomaly reasoning task. To address this, we propose AVA-GRPO, an extension of GRPO that incorporates novel anomaly verification reward. As shown in the right part of Figure 3, for each completion og, the predicted category of the video is first extracted. The video is then temporally trimmed based on the extracted prediction, and the trimmed video is fed into the model to generate a new answer. Additional anomaly verification rewards are assigned by comparing the original and regenerated answers.
On the one hand, the video is initially classified as abnormal, the predicted temporal range of the abnormal event is extracted, and the corresponding segment is discarded from the original video to create a new trimmed video containing only normal segments. Then the trimmed video is re-fed into the model. If the trimmed video is subsequently predicted as normal, it suggests that the discarded segment is indeed abnormal and the model's initial prediction was correct. this situation, a positive reward will be assigned to reinforce the model's original prediction.
On the other hand, inspired by Video-UTR [81], we consider the phenomenon of temporal hacking for video-MLLMss where the models tend to generate predictions by relying only on a few frames, typically the beginning or ending of the video, instead of comprehensively processing the entire video sequence, which is detrimental to the recognition of anomaly events. As consequence, if the video initially predicted as normal, we randomly discard either the beginning or the ending segment of the video and feed the trimmed video into the model again, If the trimmed video is then

---

Table 1: Effectiveness of anomaly reasoning.
Answer
Detection
Method
Strategy
BLEU-2
METEOR
Recall
F1
Direct Answer
0.184
0.339
0.431
0.597
Qwen2.5-VL-77 [61]
Random Reasoning
0.179
0.328
0.377
0,540
Structured Reasoning
0.198
(+0.019)
0.352
(+0.013)
0.696
(+0.265)
0.730 (+0.133)
Direct Answer
0.038
0.184
0.368
0.534
Qwen3-8B [62]
Random Reasoning
0.040
0.191
0.554
0.655
Structured Reasoning
0.043 (+0.005)
0.193 (+0.009)
0.681
(+0.313) 0.686 (+0.153)
Direct Answer
0.268
0,441
0.838
0.861
Vad-R1
Structured Reasoning
0.293 (+0.025) 0.487 (+0.046)
0.843 (+0.005) 0.862 (+0.001)
predicted as abnormal, suggests the model made its original prediction only based on insufficient visual evidence, which is not expected. Therefore, negative reward is assigned in this case.
3.4 Training Pipeline
To address the challenge of lacking large-scale annotation, we design a two-phase training pipeline as shown in Figure 3. For the first stage, supervised fine-tuning is performed on the Vad-Reasoning-SVe dataset, in which videos are annotated with high-quality Chain-of- Thoughh (CoT) as described before. In this stage, the model's capability gradually shifted from general multimodal understanding to video anomaly understanding, and it is enabled to acquire basic anomaly reasoning capability. In the second stage, training is continued on the Vad-Reasone ood-Reasoning-PR RL dataset with the proposed AVA-GRPO reinforcement learning algorithm, which evaluates the quality of model responses in a self-verification manner with only video-level weak labels available. This stage aims at moving the model beyond pattern-man matching tendencies from SFT, enabling it to develop more flexible, transferable anomaly reasoning capability. Please refer to Appendix C for more details.
4 Experiments
Implementation Details Vad-R1 is trained with
Table 2: LLM-as-judge evaluations.
two stages based on Qwen-2.5-VL-77B [61]. For the first stage, SFT is performed with Vad-Reasoning- SFT dataset for four epochs. For the second stage, RL is performed with AVA-GRPO for one epoch, where there are only video-level weak labels avail- able for Vad-Reasoning-RR RLL dataset. All experiments are conducted with 4 NVIDIA A100 (80GB) GPUs. Please refer to Appendix C for more details.
Method
R
Open-Source video MLLMs
Intern InterViide22. [69]
0,580
0.517
0.487
Intern VL3 [96]
0.692
0.608
0.586
VidcoChat-FFae Flash [29]
0.367
0.292
0.356
VideoLLaMA3 [85]
0.549
0.449
0.497
VLaVV--NeT--VV [94]
0.541
0.452
0.491
Qwen2.5-VL [61]
0.638
0.555
0.542
Open-Source video reasoning MLLMs
Evaluation Metrics and Baselines We first eval- uate Vad-RI on the test set of Vad-Reasoning. focus- ing on two aspects: anomaly reasoning and anomaly detection. For anomaly reasoning, we assess the text quality of reasoning process with BLEU [46], METEOR [3] and ROUGE [33] metrics. Besides, we also adopt LLM-as-judge evaluations [54]. For anomaly detection, we report accuracy, precision, re-
Open-RI-Vidhe [67]
0.411
0.307
0.338
Vidco-R1 [14]
0.390
0.414
0.243
VideoChat-RR R1 [30]
0.634
0.559
0.528
MI.sM- VAD methods
Holmes-VAl [88]
0,388
0.275
0.343
Holmes-VAN VAU [89]
0.385
0.301
0.375
HAWK [54]
0.218
0.185
0.115
Proprietary MLLMs
Claude3.5-Haiku [2]
0.711
0.637
0.611
call and fl scores for anomaly classification, along with mloU and R@ K for anomaly temporal ground- ing. Besides, to further explore the capabilities of
QVQ-Max [60]
0.690
0.639
0.521
GPT-4o [43]
0.724
0.679
0.542
Vad-RI (Ours)
0.734
0.659
0.662
Vad-RI, we also conduct experiments on VANE [15], a video anomaly benchmark for MI.LMs, where the MILMs are asked to answer multi-choice
questions. In this case, we report the accuracy of every category. We compare Vad-R1 with general
video MLLMs [27, 32, 41, 87, 9 reasoning video MLLMs [30, 68, 14, 92] and some proprictary
7

---

Table 3: Performance comparison of anomaly reasoning and detection Vad- Rea-onngg dataset.
Anomaly Reasoning
Anomaly Detection
Method
Params.
BLEU-2 METEOR ROUGE-2
Acc
F1
mloU R@0.3 R@0.5
Open-Source video MLLMs
Intern 5ideo2.5 [69]
8B
0.110
0.264
0,109
0.715
0.730
0.417
0,458
0.424
VL3 [96]
8B
0.124
0.286
0.116
0.779
0.756 0.550
0.613
0.540
VideoChat-Finn Flash [29]
7B
0.012
0.084
0.047
0.683 0.487 0.536
0.538
0.358
VideoL.LaMA3 [85]
7B
0.066
0.200
0.092
0.665 0.624 0.425
0.451
0.419
LLaVA-NeXT-Vine [94]
7B
0.094
0.238
0.104
0.651
0,423
0.576
0.601
0,585
Qwen2.5-VL [61]
7B
0.113
0.264
0.116
0.761
0.730 0.567
0.610
0.563
Open- video reasoning MLLMs
Open-R1-Wa Video [67]
7B
0.060
0.179
0.084
0.793 0.790
0.559
0.642
0.540
Video-RI [14]
7B
0.135
0.317
0.132
0.624
0.694
0.334
0.392
0.328
VideoChat-BB R1 [30]
7B
0.128
0.287
0.123
0.793 0.790
0.559
0.642
0,540
MLLM-based VAD methods
Holmes-VAn VAD [88]
7B
0.003
0.074
0.027
0.565 0.120
Holmes VAU [89]
2B
0.077
0.182
0,075
0,490 0.371
HAWK [54]
7B
0.042
0.156
0.042
0.513 0.648
Proprietary MILLMs
Claude3.5-Haiku [2]
0.097
0.253
0.098
0.580 0.354
0.518
0.543
0.524
GPT-4o [43]
0.154
0.341
0.133
0.711
0.760
0.472
0.565
0.476
Gemini2.5-Flan [55]
0.133
0.308
0.120
0.624
0.707
0.370
0.437
0.358
Proprietary reasoning MLLMs
Gemini2.5-pra [56]
0.145
0.356
0.137
0.829
0.836
0.636
0.722
0.638
QVQ-Max [60]
0.142
0.318
0.121
0.702
0.747
0.430
0.503
0.412
04-mini [45]
0.106
0.263
0.109
0.884
0.875
0.644
0.736
0.631
Vad-RI (Ours)
7B
0.233
0.406
0.194
0.875
0.862 0.713
0.770
0.706
models [60, 43, 56, 55]. Furthermore, we also consider MLLLM-based VAD methods [54, 89, 88). Due to space limitations, please refer to the Appendix D) for more experimental results.
4.1 Main Results
Does reasoning improve anomaly detection? Table demonstrates the effectiveness of anomaly reasoning. On the one hand, we evaluate the performance of Qwen2.5-VL. [61] and Qwen3 [62]. As shown the first two rows of Table I. compared with directly answering, prompting models to reason chain-of-thouuht will gain greater performance. In
according to the proposed perception-to-cognitii the meanwhile, we evaluate the effect of random reasoning. this case, the performance improvement is minimal, even inferior to direct answering. Notably, Qwen3 is hybrid reasoning model that supports both reasoning and non-reasoning modes for the same task. The consistent performance gap across different settings further highlights the effectiveness of the proposed P2C-CoT for anomaly reasoning and detection, On the other hand, We compare the performance of Vad-RI trained with the full P2C-CoT versus training with only the final answer portion of the P2C-CoT shown in the third row of Table 1. When Vad-RI RI is trained with only the final answer, it exhibits a performance drop.
How well does Vad-R1 perform in anomaly reasoning and detection? Table 2 and Table shows the performance comparison on the test set of Vad-Reasoning, On the one hand, Table 2 shows the results of LLM-as-judge evaluations. Following HAWK [54], we evaluate the models' outputs based on Reasonability(R), Detail(D) and Consistency(C)) We observe that Vad-R1 achieves the best performance among all open-soure methods and even surpasses proprietary MLLMS such as GPT-4o, particularly in terms of Reasonability and Consistency. On the other hand, Table shows the results of anomaly reasoning and detection tasks. Vad-R1 achieves great performance on both text quality of anomaly reasoning process and the accuracy of anomaly detection. It is worth noting that Vad-R1 significantly outperforms existing proprietary reasoning MILMMs Gemini2.5-Pro QVQ-Max and o4-mini anomaly reasoning capability, with BLEU score improvements of 0.088, 0.091, and
8

---

Table 4: Performance comparison VANE.
Method
SORA
OpenSORA
RG2
VideoLCM
MS-T2
Avenue
Ped1
Ped2
Open Soer-S MLLMS
Video-LLaMA [87]
11.59
18.00
16.00
10.57
10.41
30.00
16.66
5.55
VideoChat [27]
10.74
28.00
4.00
17.64
20.83
32.25
13.33
13.88
Video-ChatGPT [41]
26.47
22.00
12.00
18.26
16.66
39.39
40.00
19.44
Vidco-I.LaVA [32]
10.86
18.00
16.00
19.23
16.66
3.03
2.77
6.06
MovieChat [51]
8.69
10,00
16.00
14.42
6.25
18.18
6.66
11.11
LLaMA-VID [31]
7.97
14.00
20.00
19.23
14.58
27.27
6.66
19.44
TimeChat [47]
21.73
26.00
28.00
22.11
20.83
24.20
27.58
11.11
MLLM- bassed VAD methods
Holmes- VAU [89]
2.17
34,00
24.00
29.81
25.00
6.06
3.33
5.56
Holmes-Van VAD [88]
6.52
34,00
32.00
33.56
22.92
12.12
20.00
5.56
HAWK [54]
24.64
52.00
44.00
36.54
50.00
36.36
36.67
38.89
Vad-RI (ours)
41.30
78.00
56.00
63.46
60.42
75.76
60.00
63.89
Table 5: Comparison of different training strategies for Vad-R1.
Anomaly Reasoning
Anomaly Detection
Strategy
BLEU-2 ROUGE-1 ROUGE-2 ROUGE-L
Prec.
mloU
R@0.3
R@0.5
R@0.7
Qwen2.5-VL VL
0.113
0.505
0.199
0.477
0.768
0.567
0.610
0.563
0.526
+SFT
0,219
0,456
0.196
0,429
0,712
0.612
0.677
0.599
0,535
+AVA-GRPO
0.143
0.513
0.207
0.486
0.810
0.675
0.736
0.661
0.606
-SPT+AVA-GRPO
0.233
0.530
0.238
0.501
0.882
0.713
0.770
0.706
0.651
0. 1.7 respectively. Besides, compared with existing MI.L.M-based VAD methods, Vad-R1 also exhibits greater advantages in anomaly reasoning and detection.
Table shows the performance comparison VANE benchmark. Vad-RI achieves significant improvements and outperforms all baselines, including both general video MLLMs and MLLM- based VAD methods. Although the VANE benchmark formulated as a multiple-choie question- answering task, its abnormal- relatted options inherently require the models to perform abnormal behavior recognition, semantic understanding, and reasoning. To better align with our anomaly reasoning setting, we also require the models to think before answering. Therefore, the evaluation on VANE serves as a complementary validation of Vad-R1 reasoning capability, demonstrating its superior reasoning ability under complex abnormal event understanding tasks.
4.2
Ablation Studies
How to obtain the capability of reasoning? Table shows the effectiveness of different training strategies. When directly performing RL to the base model without prior SFT, the performance improvement is limited. This suggests that, without fundamental reasoning capability, the model struggles to benefit from RL training with video-level weak labels. In contrast, applying SFT leads more significant performance improvement, indicating that the structured Chain-of-Thouuht annotations effectively equip the model with basic anomaly reasoning capability. Notably, the combination of SFT and RL gains the best performance. The results align with the conclusion of DeepSeek-RII R1 [9], which suggests that SFT stage provides fundamental reasoning capability for the model, while RL stage further enhances its reasoning capability. Table 6 shows the effectiveness of the proposed AVA-GRPO. Compared with original GRPO, AVA-GRPA includes an additional anomaly verification reward, which incentivizes the anomaly reasoning capability of MLLMM with only video-level weak labels. In addition, we add length reward to control the length of output. For both 16-frame and 32-frame settings, AVA-GRPO outperforms reasoning and detection tasks. In contrast, using only one reward leads to limited or unstable improvement. These results demonstrate that the combination length and anomaly rewards essential for improving the overall reasoning and detection performance.
original GRPO across video

---

Table 6: Ablation results of different reward strategies.
Reasoning ROUGE-L
Detection
Frames
Strategy
ROUGE-2
Precision
mIoU
R@0.3
R@0.5
R@0.7
GRPO
0,502
0.475
0.861
0.712
0.770
0.699
0.640
GRPO+ten_Lrwwae
0,529
0,501
0,856
0.710
0.770
0.697
0.642
GRPO+ano reward
0.496
0.467
0.866
0.707
0.765
0.695
0.638
AVA-GRPO
0.530
0.501
0,882
0.713
0.770
0.706
0.651
GRPO
0.495
0.468
0.831
0.695
0.761
0.692
0.624
GRPO+len reward
0.528
0.499
0.849
0.701
0.770
0.695
0.631
32
GRPO+ano reward
0.494
0.467
0,842
0.699
0.763
0.686
0.629
AVA-GRPO
0.533
0.504
0.900
0.726
0.786
0.715
0.661
Key of reasoning
ur erban street
wet or roiny weather
discorded plostic bags and trosh
pedestrions
disrupts expected clear street settings
Anomaly: Obstocles on Road
Vad-R1 (Ours)
Cornect reasoning
A1 Cloude3, 5- Haiku
Hallucination: moving litter
<stopls A rainy urbon environment is shown, with wet roads, .. Vehicles move alory streets, and pedestrions
<stepl> The video depicts on urbon street scene with multiple lanes, overpass/bridge parked vehicles, wet read surface with crosswalk markings </stepl>
appear near sidewalks... s/stepl>
<step2> However, abnornal event occurs in lower-left
<step2> A white object, which appears to be piece of poper plastic, moving across the s/step2>
portion of the frame. white plastic bay lies on the wet road, partially blocking the crosswalk sasswalk..///tes <//ttep>>
<step3r 3r The movement of the white object is abnormal because it to randomly drifting
<step.3> The presence of plastic bag on road identified as abnormal. It disrupts the normal flow of traffic and poses a
</step3>
patential hazard to pedestrians and vehicles </step3>
<step4> Such unpredictoble movement of debris in busy street
enviranment could potentially traffic disruption... </step4>
<step4 As result, this action could lead to accidents or injuries due reduced visibility and traction wet surfoces <ttep4
SF o4-mini
Missed anomaly
QVQ-Max
Missed anomaly
<stopl> The video shows an urban intersection on roiny day, with several discorded foom food contoiners ond plastic debris scattered across the readway and cresswalk. A person then walks into the middle of the read and collects these items. </stepl>
<stepl> depicts on urbon street soene with vehicles, a pedestrian cressing, and scottered trash on the read. A person is
collecting litter from the middle of the street and disposing of it a nearby trush bin, r/stepl>
<step2> individual carnies gathered trash over to nearby sidewalk where public garbage birs are located and deposits the waste appropriately. demonstrating routine cleaning behevior, </step2>
<step2> Based the described scenes, video showcases collection disposal urban
routine actions environment. s/step2:
Figure 4: Qualitative result for an abnormal video.
4.3 Qualitative Analyses
As shown in Figure 4, Vad-R1 correctly performs anomaly reasoning and identifies the white plastic bag as an anomaly. In contrast, although Claude identifies the plastic bag as abnormal, it defines the cause of the abnormality as moving plastic bag, rather than the plastic bag acting Besides, QVQ-Max and 04-mini also identify the white plastic bag, they do not treat it as an anomaly.
obstacle.
5 Conclusion
In this paper, we present Vad-RI, novel end-to-end MLLM-based framework for video anomaly reasoning which aims to enable deep analysis and understanding of anomalies in videos. Vad-R1 performs structured anomaly reasoning process through structured gradually from perception to cognition. The anomaly reasoning capability of Vad-R1 is derived from a two-stage training strategy, combining supervised fine-tuning on CoT-annotated videos and reinforcement learning with an anomaly verification mechanism. Experimental results demonstrate that Vad-RI I achieves superior performance on anomaly detection and reasoning tasks.
that progresses
10

---

Acknowledgments
This work was supported in part by the National Key R&D Program of China (No. (No.2022ZD01 19200), the National Natural Science Foundation of China (No. Noo.6330121,, No.62306343), Shenzhen Science 2023AA08), Shenzhen General Research Project and Technology Program (No. Guangdong Basic and Applied Basic Research Foundation (No. (No. (CY120041200125994000 151501 1398, No. 2025A151501 1322), the Smartcht research fund (No.2024-08), and China Postdoctoral Science Foundation (No. 2025T180435, No. 2024M753741).
References
[1] Andra Acsintoae, Andrei Florescu, Mariana- Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor lonescu, Fahad Shahbaz Khan, and Mubarak Shah. Ubnormal: New benchmark for supervised open-set video anomaly detection. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pages 20143-20153, 2022. 3,5,
[2] Anthropic. Claude 3.5 haiku, 2024. URL https: http://wwwwwww..i....//m com/ Slaude/haikie 7,
8, 32
[3] Satanjeev Banerjee and Alon Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the act workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65-72, 2005. 7
[4] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, et al. Video generation models as world simulators. OpenAl Blog, 1:8, 2024.7 28
[5] Congqi Cao, Yue Lu, Peng Wang, and Yanning Zhang. A comprehensive benchmark for semi-supervised video anomaly detection and anticipation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pages 20392 202221111,, June 2023. 26
[6] Joya Chen, Zhaoyang Lv, Shiwei Wu, Kevin Qinghong Lin, Chenan Song, Difei Gao, Jia- Wei Liu, Ziteng Gao, Dongxing Mao, and Mike Zheng Shou. Videollm-online: Online video large language model for streaming video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18407-18418, 2024.
[7] Junxi Chen, Liang Li, Li Su, Zheng-Jun Zha, and Qingming Huang. Prompt-enhanced multiple instance learning for weakly supervised video anomaly detection. In Proceedings of the JEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18319-18322 18329, 2024. 1,3
[8] Weiling Chen, Keng Teck Ma, Zi Jian Yew, Minhoe Hur, and David Aik-Aun Khoo. Tevad: Improved video anomaly detection with captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5549-5559, 2023.
[9] DeepSeek-AI. DeepSeek-Be R1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501. 2025. 1,
[10] Shangzhe Di, Zhelun Yu, Guanghao Zhang, Haoyuan Li, Tao Zhong, Hao Cheng, Bolin Li, Wanggui He, Fangxun Shu, and Hao Jiang. Streaming video aanswirnnn with in-context video kv-cache retrieval. arXiv preprint vrXiv:2503.005.. 2025. 3
[11] Zongcan Ding, Haodong Zhang, Peng Wu, Guansong Pang. Zhiwei Yang, Peng Wang, and Yanning Zhang. Slowfastvad: Video anomaly detection via integrating simple detector and rag-enhanced vision-language model. arXiv preprint arXiv:2504.1033 2025. 2.
[12] Hang Du, Guoshun Nan, Jiawen Qian, Wangehenhui Wu, Wendi Deng, Hanqing Mu, Zhenyan Chen, Pengxuan Mao, Xiaofeng Tao, and Jun Liu. Exploring what why and how: A multifaceted benchmark for causation understanding of video anomaly. arXiv preprint urXiv:2412.0718.
2024. 2, 3, 5, 25, 26
11

---

[13] Hang Du, Sicheng Zhang, Binzhu Xie, Guoshun Nan, Jiayang Zhang, Junrui Xu, Hangyu Liu, Sicong Leng, Jiangming Liu, Hehe Fan, al. Uncovering what why and how: comprehensive benchmark for causation understanding of video anomaly. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 18793-18803, 2024. 2, 3, 25, 26
[14] Kaituo Feng. Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Benyou Wang, and Xiangyu Yue. Video-rl: Reinforcing video reasoning in milms. arXiv preprint arXiv:2503.21776, 2025. 4, 7, 8, 31,
[15] Hanan Gani, Rohit Bharadwaj, Muzammal Naseer, Fahad Shahbaz Khan, and Salman Khan. Vane-bench: Video anomaly evaluation benchmark for conversational Imms. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 3123-3144 2025. 7, 28
[16] HPCAI Open-sora: Democratizing efficient video production for all. https:/ /ttp//ggtttb..t/// com httphhpcaaitechhppee-eeeee Sora, 2024, 28
[17] Chao Huang, Zhihao Wu, Jie Wen, Yong Xu, Qiuping Jiang, and Yaowei Wang. Abnormal event detection using deep contrastive learning for intelligent video surveillance system. IEEE Transactions on Industrial Informatics, 18(8):5171-5179, 2021. 1,3
[18] Chao Huang, Chengliang Liu, Jie Wen, Lian Wu, Yong Xu, Qiuping Jiang, and Yaowei Wang. Weakly supervised video anomaly detection via self- self-guided temporal discriminative transformer. IEEE Transactions on Cybernetics, 54(5))3117-3)
2022. 1,3
[19] Chao Huang, Jie Wen, Yong Xu, Qiuping Jiang, Jian Yang. Yaowei Wang, and David Zhang. Self supervuie attentive generative adversarial networks for video anomaly detection. IEEE transactions on neural networks and learning systems, 34(1 1):9389-9403, 2022. 1,3
[20] Chao Huang, Jie Wen, Chengliang Liu, and Yabo Liu. Long short-term dynamic prototype alignment learning for video anomaly detection. In Proceedings of the Thirty-Thrrd International Joint Conference on Artificial Intelligence, pages 866-874, 2024. 1,3
[21] Chao Huang, Weiliang Huang, Qiuping Jiang, Wei Wang, Jie Wen, and Bob Zhang. Multi- modal evidential learning for open-world weakly-supervised video anomaly detection. IEEE Transactions on Multimedia, 2025. 1,3
[22] Chao Huang, Qianyi Li, and Bob Zhang. Prototype-guice and dynamic-aware video anomaly detection. Neural Networks, page 107583, 2025. 1
[23] Chao Huang, Yushu Shi, Jie Wen, Wei Wang, Yong Xu, and Xiaochun Cao. Ex-vad: Explainable fine-grained video anomaly detection based on visual-language models. In Forly-second International Conference on Machine Learning. 2025.
[24] Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, and Shaohui Lin. Vision-rl: Incentivizing reasoning capability in multimodal large language models, arXiv preprint arXiv:2503.06744 2025. 3
[25] Peng Jin, Ryuichi Takanobu, Wancai Zhang, Xiaochun Cao, and Li Yuan. Chat-univi: Unified visual representation empowers large language models with image and video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13700-13710, 2024. 3
[26] Hyekang Kevin Joo, Khoa Vo, Kashu Yamazaki, and Ngan Le. Clip-tsa: Clippassisted temporal self-attention for weakly-supervised video anomaly detection. In 2023 IEEE International Conference on Image Processing (ICIP), pages 3230-3233 3234. IEEE, 2023. 1,3
[27] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang. and Yu Qiao. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06333 06355 2023. 3, 7,9
[28] Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos. Anomaly detection and localization in crowded scenes. IEEE transactions on pattern analysis and machine intelligence, 36(1):18-32, 2013. 26, 28
12

---

[29] Xinhao Li, Yi Wang. Jiashuo Yu, Xiangyu Zeng. Yuhan Zhu. Haian Huang, Jianfei Gao, Kunchang Li, Yinan He, Chenting Wang, al. Videochat-flash Hierarchical compression for long-context video modeling. arXiv preprint arXiv:2501.00577 2024. 7, 31,
[30] Xinhao Li, Ziang Yan, Desen Meng. Lu Dong, Xiangyu Zeng, Yinan He, Yali Wang, Yu Qiao, Yi Wang, and Limin Wang. Videochat-rl: Enhancing spatio-temporal perception via reinforce- ment fine-tuning, arXiv preprint arXiv:2504.0690 06958, 2025. 4, 7, 8, 31, 32
[31] Yanwei Li, Chengyao Wang, and Jiaya Jia. Llama-vid: An image is worth 2 tokens in large language models. In European Conference on Computer Vision, pages 323-340. Springer, 2024.
3,9
[32] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-Ilava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122. 2023. 3, 7,9
[33] Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81, 2004.
[34] Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao. Future frame prediction for anomaly detection-a a new baseline. In Proceedings of the IEEE conference on computer vision and pattem recognition, pages 3536-6545 2018. 1, 5,
[35] Ye Liu, Kevin Qinghong Lin, Chang Wen Chen, and Mike Zheng Shou. Videomind: A chain-of-lora agent for long video reasoning. arXiv preprint arXiv25503.1344, 2025. 3
[36] Zhian Liu, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li. A hybrid video anomaly detection framework via memory augmentdd flow reconstruction and flow-guide' frame prediction. Proceedings of the IEEE/CVF international conference on computer vision,
pages 13588- 13597, 2021. 1,3
[37] Cewu Lu, Jianping Shi, and Jiaya Jia. Abnormal event detection at 150 fps in matlab. In Proceedings of the IEEE international conference on computer vision, pages 2720-2727, 2013.
26, 28
[38] Hui Lv and Qianru Sun. Video anomaly detection and explanation via large language models. arXiv preprint wrXiv:2401.05700 2024. 2,
[39] Hui Lv, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yong Li, and Jian Yang. Localizing anomalies from weakly-labele videos. IEEE transactions on image processing, 30:4405-4511 2021. 1,
3, 5, 26
[40] Junxiao Ma, Jingjing Wang, Jiamin Luo, Peiying Yu, and Guodong Zhou. Sherlock: Towards multi-scene video abnormal event extraction and localization via global-local spatial-sensitive Ilm. In Proceedings of the ACM on Web Conference 2025, pages
2025. 2.
[41] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424 2023. 3, 7,9
[42] Chengzhi Mao, Revant Teotia, Amrutha Sundar, Sachit Menon, Junfeng Yang, Xin Wang, and Carl Vondrick. Doubly right object recognition: A why prompt for visual rationales. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2722-2732, 2023. 29
[43] OpenAl. Gpt-4o system card. arXiv preprint arXi::2410.21276, 2024. 7, 29,
[44] OpenAl. Openai ol system card. arXiv preprint arXiv:2412.16722 2024. 2024.
[45] OpenAl. Openai o3 and o4-mini system card, 2025, URL attps://oppntti www//ondo//////ccoco
63-o4-mint mit--mmiiisssseem---eee system-card/.... 8.
[46] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: method for automatic evaluation of machine translation. Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318, 2002. 7
13

---

[47] Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, and Lu Hou. Timechat: A time-sensitive multimodal large language model for long video understanding. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 14313-14323, 2024. 9
[48] Nicolae-C C Ristea, Florinel-Alin Croitoru, Radu Tudor lonescu, Marius Popescu, Fahad Shahbaz Khan, Mubarak Shah, et al. Self-distilled masked auto-encoders are efficient video anomaly detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15984-15995, 2024. 3
[49] Runway Research. Gen-2: The next step forward for generative ai. https://research. runwayml. com/ gen2, 2024. 28
[50] Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. 3, 6
[51] Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang. et al. Moviechat: From dense token to sparse memory for long video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18221-18232, 2024.
[52] Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, Michael Günther, Bo Wang, Markus Krimmel, Feng Wang, Georgios Mastrapas, Andreas Koukounas, Andreas Koukounas, Nan Wang, and Han Xiao. jiiaa-ebbeedddngs-s.... Multilingual embeddings with task lora, 2024. URL https: / aaaaaaivvvv..o http//axiv.ora/aaaa/a /tabss//4000 10173. 30
[53] Waqas Sultani, Chen Chen, and Mubarak Shah. Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 6479-6488, 2018. 1, 3, 5, 25, 26, 28
[54] Jiaqi Tang, Hao Lu, Ruizheng Wu, Xiaogang Xu, Ke Ma, Cheng Fang. Bin Guo, Jiangbo Lu, Qifeng Chen, and Yingcong Chen. Hawk: Learning understand open video anomalies. Advances Neural Information Processing Systems, 37:139751-139785 2024. 2, 3, 5, 8, 9,
26, 31, 32
[55] Gemini Team. Gemini 2.5 flash preview model card, 2025. URL https://storage.
googleapis com/model http///del-ccarrdddcooooaeo -ccdda//ccmmeett/ggemeee -2.5- flafflahhhpppp seech-ppeviee..dd. 8, 32
[56] Gemini Team. Gemini 2.5 pro preview model card, 2025. URL https://storag:: googleapis www.coooooco./eeeeoeeeooooooe..e hwtpr//aaarddddcccnnnee//e/ee
2. peeppro-pppee preview. pdf. 8,
[57] Kimi Team. Kimi k1.5: Scaling reinforcement learning with llms.
arXiv preprint
1XXi:25511112999 2025.
[58] Qwen Team. QwQ: Reflect deeply on the boundaries of the unknown, 2024. URL https: //qwenlm. gi wetttih/bbbllooooooooeeeeeo htcbllg///////oo//ooo 3w-.-bbbeeeee/.111
[59] Qwen Team. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024. 5
[60] Qwen Team. QVQ-Max: Think with evidence, 2025. URL htllhttps///qeee ceitigiithubio.
blog/qu pt///qq-maaat sesq-max-previeee preview/. 7, 8, 32
[61] Qwen Team. Qwen2.5-vl technical report. arXiv preprint arXh::2502.13923. 2025, 5, 7, 8, 31,
32
[62] Qwen Team. Qwen3: Think deeper, act faster, 2025. URL https://qwennnn https//////ennniiiihh.bbbb io/ blog/ blog/qwen3/. //wp////w/.///////////e.e/.. 7,8
[63] Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, et al. Llamav-ol: Rethinking step-by-step visual reasoning in 1lms. arXiv preprint arXiv:2501.06186 2025. 3
[64] Benfeng Wang, Chao Huang, Jie Wen, Wei Wang, Yabo Liu, and Yong Xu. Federated weakly supervised video anomaly detection with multimodal prompt. In Proceedings of the AAA/ Conference Artificial Intelligence, volume 39, pages 21017-21025, 2025. 1,3
14

---

[65] Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, and Shiwei Zhang.
Modelscope text-to-video technical report, 2023. 28
[66] Xiang Wang, Shiwei Zhang, Han Zhang, Yu Liu, Yingya Zhang, Changxin Gao, and Nong
Sang. Videolem: Video latent consistency model, 2023. 28
[67] Xiaodong Wang and Peixi Peng. Open-rl-video, 2025. URL https://github. com/
Wang- Xiaodong Caaee//pen//eeeeeeeeeee -idii. 7, 8, 31,32
[68] Ye Wang, Boshen Xu, Zihao Yue, Zihan Xiao, Ziheng Wang, Liang Zhang, Dingyi Yang, Wenxuan Wang, and Qin Jin. Timezero: Temporal video grounding with reasoning-guine guiiiigggggdedddd Ivim. arXiv preprint arXiv:2503. 2027, 4,7
[69] Yi Wang, Xinhao Li, Ziang Yan, Yinan He, Jiashuo Yu, Xiangyu Zeng, Chenting Wang, Changlian Ma, Haian Huang, Jianfei Gao, al. Internvide02. 5: Empowering video milms with long and rich context modeling. arXiv preprint arXiv:2501.12386, 2025. 7, 8, 31, 32
[70] Lian Wu, Chao Huang, Shuping Zhao, Jinkai Li, Jianchuan Zhao, Zhongwei Cui, Zhen Yu, Yong Xu, and Min Zhang. Robust fall detection in video surveillance based on weakly supervised learning. Neural networks, 163:286-299 297, 2023. 1
[71] Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang. Not only look, but also listen: Learning multimodal violence detection under weak supervision. In Computer Vision-ECCV 2020: 16th European Conference, Glaxgow, UK, August 23-28, 2020, Proceedings, Purt XXX 16, pages 322-339. Springer, 2020. 1, 3, 5, 25, 26
[72] Peng Wu, Xuerong Zhou, Guansong Pang. Yujia Sun, Jing Liu, Peng Wang, and Yanning Zhang. Open-vocabulary video anomaly detection. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 19397-18307, 2024. 1,3
[73] Peng Wu, Xuerong Zhou, Guansong Pang, Lingru Zhou, Qingsen Yan, Peng Wang, and Yanning Zhang. Vadclip: Adapting vision-language models for weakly supervised video anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 6074-6082, 2024. 3,
[74] Haomiao Xiong, Zongxin Yang, Jiazuo Yu, Yunzhi Zhuge, Lu Zhang, Jiawen Zhu, and Huchuan Lu. Streaming video understanding and multi-round interaction with memory-enhanced knowl- edge. arXiv preprint arXiv:2501.
2025. 3
[75] Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, and Li Yuan. Llava-ol: Let vision language models reason step-by-step arXiv preprint arXiv:2411. 1..:22411..0440, 2024. 3
[76] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. Pllava: Parameter-free llava extension from images to videos for video dense captioning. arXiv preprini a404v:2404. /6994, 2024. 3
[77] Yuchen Yang, Kwonjoon Lee, Behzad Dariush, Yinzhi Cao, and Shao- Yualo Lo. Follow the rules: reasoning for video anomaly detection with large language models. In European Conference on Computer Vision, pages 304-333 322. Springer, 2024. 2, 3
[78] Zhenyu Yang. Yuhang Hu, Zemin Du, Dizhan Xue, Shengsheng Qian, Jiahong Wu, Fan Yang, Weiming Dong, and Changsheng Xu. Sybench: A benchmark with temporal multi-turn dialogues for streaming video understanding. arXiv preprini arXiv:2502. 10810, 2025. 3
[79] Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Yuchen Wang, Ella Atkins, and David Crandall. Dota: Unsupervised detection of traffic anomaly in driving videos. IEEE transactions on pattern analysis and machine intelligence, 45(1):444449, 2022. 1,3
[80] Muchao Ye, Weiyang Liu, and Pan He. Vera: Explainable video anomaly detection via verbalized learning of vasion-language models. arXiv preprint arXiv:2412.01095 2024. 1,
[81] En Yu, Kangheng Lin, Liang Zhao, Yana Wei, Zining Zhu, Haoran Wei, Jianjian Sun, Zheng Ge, Xiangyu Zhang, Jingyu Wang, et al. Unhackable temporal rewarding for scalable video milms. arXiv preprint 1208,, 2025. 5, 6, 24
15

---

[82] Tongtong Yuan, Xuange Zhang. Kun Liu, Bo Liu, Chen Chen, Jian Jin, and Zhenzhen Jiao. Towards surveillance video-and and- and-language understanding: New dataset baselines and challenges. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22052-22066 2024. 3, 26
[83] Luca Zanella, Willi Menapace, Massimiliano Mancini, Yiming Wang. and Elisa Ricci. Har- nessing large language models for freei video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18527-18536,
2024. 2.3
[84] Yufei Zhan, Yousong Zhu, Shurong Zheng, Hongyin Zhao, Fan Yang, Ming Tang, and Jinqiao Wang. Vision-rl: Evolving human-free alignment in large vision-language models via vision- guided reinforcement learning. arXiv preprini arXiv:2503. 18013, 2025. 3
[85] Boqiang Zhang, Kehan Li, Zesen Cheng, Zhiqiang Hu, Yuqian Yuan, Guanzheng Chen, Sicong Leng, Yuming Jiang, Hang Zhang. Xin Li, et al. Videollama 3: Frontier multimodal foundation models for image and video understanding. arXiv preprint arXiv:2501.13100 2025. 7, 8, 31, 32
[86] Dasheng Zhang, Chao Huang, Chengliang Liu, and Yong Xu. Weakly supervised video anomaly detection via transformer-enabled temporal relation learning. IEEE Signal Processing Letters, 19:1197-1201, 2022.
[87] Hang Zhang, Xin Li, and Lidong Bing. Video-Ilama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiiv:330.0028855 2023. 3, 7,9
[88] Huaxin Zhang. Xiaohao Xu, Xiang Wang. Jialong Zuo, Chuchu Han, Xiaonan Huang, Changxin Gao, Yuehuan Wang, and Nong Sang. Holmes-vad: Towards unbiased and explainable video anomaly detection via multi-modal 11m. arXiv preprint arXiv:2406. 12235, 2024. 3, 7, 8,
26, 31,32
[89] Huaxin Zhang, Xiaohao Xu, Xiang Wang, Jialong Zuo, Xiaonan Huang, Changxin Gao, Shanjun Zhang, Li Yu, and Nong Sang. Holmes-vau: Towards long-term video anomaly understanding any granularity. arXiv preprint arXiv:2412.067777 2024. 2, 3, 7, 8, 9, 26, 31, 32
[90] Peiyuan Zhang, Kaichen Zhang. Bo Li, Guangtao Zeng, Jingkang Yang, Yuanhan Zhang, Ziyue Wang, Haoran Tan, Chunyuan Li, and Ziwei Liu. Long context transfer from language to vision. arXiv preprint arXiv:2406. /6852, 2024. 3
[91] Renrui Zhang, Jiaming Han, Chris Liu, Peng Gao, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, and Yu Qiao. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. arXiv preprint v:Xi::2303.16199 2023. 3,7 7
[92] Xingjian Zhang. Siwei Wen, Wenjun Wu, and Lei Huang. Tinyllava-video-rl Towards smaller Imms for video reasoning. arXiv preprini urXiv:2504.0964. 2025. 4,7
[93] Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, et al. Qwen3 embedding: Advancing text embedding and reranking through foundation models. arXiv preprint arXiv:2506.05176, 2025. 30
[94] Yuanhan Zhang, Jinming WU, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint 1rXiv:2410.02713, 2024. 7, 8, 31, 32
[95] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-asaa-judge with mt-bench and chatbot arena. Advances in neural information processing systems, 36:46595-46623, 2023. 29
[96] Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Yuchen Duan, Hao Tian, Weijie Su, Jie Shao, et al. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504. 10479, 2025. 7, 8, 31, 32
16

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The abstract and introduction accurately state our contributions.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results be expected to generalize other settings.
is fine include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
The limitations are discussed in Appendix.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low images are taken in low lighting. Or speech-to-text system might be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach address problems of privacy and faimess.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
17

---

Justification:
The paper does not include theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: YYs]
The instructions for replicating the results are provided Section 4. and Appendix.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments. If the paper includes experiments, No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily
model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome deseribe the particular way they provide for reproducibility, In the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
18

---

Answer: [Yes]
Justification: The dataset construction process is provided in Section 3.2 and Appendix. The code and dataset will be publicly released.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (uttps://nips.. ec/
for more details.
While encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https: / //nips. cc/cc///// sec/public/gee wtwgggididccssssa/
ssisssolicyy for more details.
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
The experimental settings provided in Section 4.1 and Appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [NA]
Justification:
The paper does not include the results significance statistics.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall
with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (c.g., Normally distributed errors).
19

---

It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
Details of computing resource are provided in Section 4.1 and Appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't 't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the csGuidel NeurIPS Code of Ethics https: http///eeerrpp.eeeee http://ps..ccpppciiiiiiiccecee ee ines?
Answer: [Yes]
Justification:
The research adheres to the NeurlPS Code of Ethics
Guidelines:
The answer NA means that the authors have reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification:
The potential impacts are discussed in Appendix.
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no socictal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
20

---

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [Yes]
Justification: The
required to follow specific usage guidelines, and the dataset and code can only be used for academic research.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recogmize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: All original sources have been properly credited, and the licenses and terms of use associated with the external assets utilized in this paper have been strictly respected.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URI.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g.. website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms use in package should be provided. For popular datasets, paperswi twwode.hco....c. com datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
21

---

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
this information is not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: All new assets presented in this paper will be released with appropriate metadata and usage documentation.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moodl as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
This paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: This paper does involve crowdsourcing research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere the NeurIPS Code of Ethics and the guidelines for their institution.
22

---

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [Yss]
Justification: The training pipeline, dataset and parameters of MLLM detailed in Section 3.4, Section 4.1 and Appendix.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to LLM policy (https: /ttp///eeeippeeeeeeee/eee/e cc/Conferencce///220 /LLM) for what should or should not be described.
23

---

A Summary of Appendix
This appendix provides supplementary information for the main paper. Firstly, we provide detailed information about the proposed Vad- Bad-Reasoning dataset, including the construction process, statistical analysis, and some examples. Then, we provide more experimental details covering prompts, settings, parameters, and computing resources, Furthermore, we provide more experimental results as well as visualizations. Finally, we discuss the potential impact and limitation.
B
The proposed Vad- Dataset
B.1 Annotation Pipeline
The training set of Vad- Realonins consists of two subsets: Vad- Sat-RRasoning-S-P and Vad-Reasoning- RL. For Lod-RRasoning-RL. we retain the original dataset annotations and collapse them into video-level weak labels (Abnormal or Normal). For Vad-Reasonnng-SPF we design a multi-stage annotation process based on the proposed P2C-CoTT, as shown in Figure 5.
Dense Frame Descriptions
Global Perception
Local Penception
Scene Description
Abnormal Event Description
QwenVL-
Qwen-
Max
Quenn Max
Mox
Naraal
Abnormal
Video
Quenn- Max
Basic Armotations
Anomaly
Shallow Cognitiin
Deep Cognition
Fine grained Category
Anomaly Identification
Cousol Reasoning
Manual annotate
Explanation
Qwen- Max
Social Norms
Spetial Location
Time
Jange
Qwen- Max
Manual Check
MILLI
Answer
Summary of Anomaly with Several Key Words
Check & Verify
Verify
Figure 5: Illustration of multi- stage annotation process of Vad- Mld-Reasonnng-SEE SFT dataset.
Firstly, we manually annotate the fine-grained anomaly categories of the video, the duration of the anomaly events, and the approximate spatial location where the anomaly events occurred. Then, in order to ensure that the P2C-CoT annotation covers all key information in the video. we follow the principle of high frame information density [81]. Specifically, the video is decomposed into separate frames with a frame interval of 16. The extracted frames are then fed into Qwen-VL-Max to generate detailed dense frame descriptions. Considering the redundancy and high density of video information, directly prompting the model to generate annotations for the entire video would result in high annotation costs and information loss. In comparison, we first perform dense sampling at certain intervals, and then require Qwen-VL-Max VL-Max to describe the video frames in detail, which can greatly preserve the key information the video frames and reduce information loss.
After obtaining detailed dense video frame descriptions, we employ Qwen-Max to summarize and generalize the proposed P2C-CoT from these descriptions. Specifically, to ensure the structure of the reasoning process, we generate each step of P2C-CoT separately with different prompts, instead od generating them all at once. This design allows each step to focus on a specific task (e.g. step I focuses on scene description while step 2 focuses on abnormal event description). Each step will receive the output of previous step as input to ensure logical coherence. After generating all the reasoning steps, the model then generate short summary about the anomaly in the video as final answer. In addition, we explicitly define anomaly categories to align model outputs with human- defined semantics, while enforcing constraints on relevance, objectivity, and neutrality. To further ensure the correctness of the generated CoT, all annotations are firstly verified by large language models and subsequently checked by human experts.
24

---

individual
vehicle norm
time rtrafi
fic
Individual
FoUTine
behaviors
anomaly
aG
Tes
vehicle
environment
ef
lower
owerresumne
resuu near
Via
Hlaction
bacause
with
envir
thauh
Conment
OUT
like
ar ee
event
monmer
elossifie
categor
align
could Canmata
expected
Mabnoo
center social action
Foad Pallin
setting typical wnmoortt urban
welkknng
stenesseeeeene indoor
considered
(a) Word cloud visualization for abnormal videos.
(h) Word cloud visualization for normal vidcos.
Figure 6: Word cloud visualizations of Vad ood-RRasonnng-SSS SFT dataset.
B.2 Statistical Analysis and Comparison
We compare Vad-Reasoning with existing video anomaly detection and understanding datasets in Table 7 and Table 8. Vad-Reasoning consists of a total of 8641 videos, covering 34 million frames and over 360 hours of duration, making one of the largest datasets among video anomaly understanding benchmarks. Besides, Fad-Reesoning-a SFT provides fine-grained Chain of-n-oo--n of-Thought (CoT) annotations, explicitly simulating human reasoning over abnormal events, with an average annotation length of 260 words. For the annotations, the recent video anomaly understanding datasets like CUVA [13] and ECVA [12] contain the description about the cause and effect of the anomaly. However, their corresponding annotations are isolated and disjointed, lacking systematic structure and logical progression. In contrast, the proposed Bad-Reasoning-t SFT datset provides structured and coherent anomaly reasoning annotation.
Figure illustrates word cloud visualizations generated from the annotations of our Bad-Reasoning- SFT dataset. For abnormal videos, terms such as anomaly, event, and safety frequently appear, emphasizing that the CoT annotations of abnormal videos focus on irregular situations and potential risks. In contrast, normal videos are characterized by high-frequency words like activities, normal, and environment, which highlight everyday scenarios and typical human behaviors. Our fud-Reasoning- SFT dataset not only captures descriptive content but also encourages models to engage in deeper causal interpretation of abnormal events. These observations indicate that our dataset purposefully constructed to promote both recognition and reasoning in complex real-world contexts.
Figure presents a comprehensive statistical overview of the proposed Vad-Reasoning dataset. The overall distribution of video length is relatively even as shown in Figure 7(a) and (b). Most of the videos in the Vad- Read-Resonnng dataset are collected from UCF-Crimi [53] and XD-Violence [71] as shown in Figure 7(c) and (d). And we collect additional 10 percent of videos from the internet. The proportion of normal and abnormal videos in the two subsets is basically balanced shown in Figure 7(c). Finally, the fine- tne-grained anomaly distributions are shown in Figure 7(f)-(h).
B.3 Examples
We provide two examples of the proposed Vad-Reasoning dataset in Figure and Figure Notably, the CoT of normal videos will be simplified into two steps, the simple perception and cognition.
C
Implementation Details
C.1 Prompt
The prompt used for performing video anomaly reasoning is shown in Figure 10. The prompt is com- posed three parts, Task Definition, Output Specification and Format Requirements Firstly, the Task Definition outlines the overall goal of video anomaly reasoning and explicitly require the model to think before answering. Secondly, the Output Specification provides detailed guidelines on the reasoning process and the expected answer. Finally, the Format Requirements presents concrete output examples with explicitly defined tags (c.g., <think></think> and Canswer>/ansswr>>
25

---

Table 7: Basic metadata comparison of datasets, Here "Mixture" indicates that the dataset is composed by integrating videos from multiple existing datasets.
Dataset
Source
Videos
Frames
Duration
Resolution
FPS
Traditional Video Anomaly Detection
Datasets
UCF-Crime [53]
Surveillance
1900
13,741,393
128h
320 240
Multiple
XD-Violence [71]
Multiple
4754
18,714,328
217h
Multiple
24
ShanghaiTech [34]
Campus
437
317,398
856 480
UCSD Ped1 [28]
Campus
70
14,000
238 158
UCSD Ped2 [28]
Campus
4,560
360 240
CUHK Avenue [37]
Campus
37
30,652
0.3h
640 360
25
TAD [39]
Traffic
518
540,212
Multiple
UBnormal [1]
Generation
543
236,902
2.2h
Multiple Multiple
30
NWPU Campus [5]
Campus
547
1,466,073
16.3h
25
Video Anomaly Understanding Datascts
UCA [82]
Surveillance
1854
13,163,270
121.9h
Multiple
CUVA [13]
Multiple
986
3,345,097
32.5h
Multiple
Multiple
BCVA [12]
Multiple
2127
19,042.560
88.2h
Multiple
Multiple
VAD-Instruct50 [88]
Mixture
6654
32,455,721
345h
Multiple
Multiple
HIVAU-7OK [89]
Mixture
6654
32,455,721
345h
Multiple
Multiple
HAWK [54]
Mixture
7898
14,878,233
142.5h
Multiple
Multiple
Vad- RReasnnnnn-SS
Mixture
2193
8,680.615
88.3h
Multiple
Multiple
Vad-Rease ood-Reasoning--e
Mixture
6448
25,495,729
272.2h
Multiple
Multiple
Vad-Rean Vad-Reasoning
Mixture
8641
34,173,344
360.5h
Multiple
Multiple
Table The annotation type comparison of datasets. denotes that the videos in Maa--Reasonnn---R only labeled with video-level labels (Abnormal or Normal).
Dataset
Anomalies
Text Annotation
Reasoning
Traditional Video Anomaly Detection Datasets
UCF-Crime [53]
13
Anomaly class Anomaly class
XD- Violleen [71]
6
ShanghaiTech [34]
13
UCSD Ped1 [28]
5
UCSD Ped2 [28]
5
CUHK Avenue [37]
5
TAD [39]
7
UBnormal [1]
22
NWPU Campus [5]
28
Video Anomaly Understanding Datasets
UCA [82]
13
Event descriptions
CUVA [13]
42
Anomaly description, cause, effect
Isolated Isolated
ECVA [12]
100
Anomaly description, cause, effect
VADDInstrnct50k [88]
13
Clip caption QA
HIVAU-70K [89]
13
Clip/Event/ ViieeeeeeeVieeleeelll Caption & QA
Isolated
HAWK [54]
Anomaly description & QA
Had-Reasoning-SFT
37
Chain-of-Thought Thought Video-level label
Structured & coherent
Vad- Bod-Reasoning-Rl
1"
Vad- Red-Reasoning
37
Hybrid annotation
C.2 Training Process of AVA-GRPO
The core of the proposed AVA-GRPO is the additional anomaly verification reward as shown in Algorithm 1. Besides, we additionally consider length reward. We first separately calculate the length of the reasoning text abnormal videos and normal videos Aad-Reasoning-SFi During RL training, if the length of output satisfies the corresponding range, length reward will be assigned. Notably, for each completion, the model will be only updated once. Consequently, the objective function of AVA-GRPO is simplified as
26

---

242
renu Hele
mrese peese
Dasso Seaes
P00
600
Ued pm0
12.
1anm
(a) Video length distribution Ree-RRaaoonnnn-SSe SFT.
(b) Video length distribution Vad- aad-Reasoning- RL.
ECWA
UB
SFT-Abmermal
3.3%
RL-Normal
SFi-hormal
11.
32.7%
XD
UCF
17.7%
34.3%
13.6%
91
5.5%
8.4%
TAD
64.0%
29.5%
RL-ABrormal
UCF
(c) Video source distribution of Vad Reasoning SFT.
(d) Video source distribution of Normal Abnormal vidcos Ree-RResonnnn---- RLL
distribution.
Jwalincs or Landrica
DergeoLetare
6.
Ser Broore
4.r
2.7%
4%%%
ACCEFN
17.94
924
3.%
7.2
10
10:4%
Bogil
11.6
Weigg May Orving
Fad Vilicon
Tafe
(f) Human anomalies distribution.
(g) Environment anomalics distribution.
(h) Object anomalies distribution.
Figure 7: Statistical analyses of the proposed Vad-Reasoning dataset.
To(o; 1 A, DKL mes) )] Tom gnd (0: 9)
GAV = B(q
where Temara is equivalent to na. Finally, the training process of AVA-GRPO is shown in Algorithm 2.
C.3 More Experimental Details
All experiments are conducted on 4 NVIDIA A100 (80GB) GPUs. For supervised fine-tuning stage, we train the base MILM on Vad-Reasoning-SSS hours. For reinforcement learning stage, we continue to train the model on the aad-Reasoning-Pe dataset for one epoch, taking about hours. For efficiency, we uniformly normalize the video to 16 frames, and the maximum number of pixels per frame is limited to 128 x 28 28 during training. The learning rates for both stages are set to 1 x 10-6 The number of completions generated in group is set to 4. The hyperparameter 3 in Equation is set as 0.04. AVA-GRPO includes five types of rewards. The specific values and meanings are shown in Table For normal videos, the length range of reasoning process is set [140, 261], while it is set [233, 456] for abnormal videos.
dataset for four epochs, taking approximately
27

---

Step 1:
Global Perception
Step 2: Local Perception
young bay interacts with a large white cut in colm indoor dining area. The cat sits on the bey s lop or nearby on chair, and both face window letting natural light. The setting includes wooden furniture, plants, and frumed pictures, creating a cozy atmesphere. The boy engages in routine behaviors like petting zitting beside the cat
However, focusing on the abnormal event, we abserve that the
lower-left portion of the frome. the bey leans forword toward the cet if to kiss or nuzzle The remains seated his lop during interaction. This specific behavior introduces element of unpredictability in their interaction.
chair, weoden furniture
plants, pictures
kiss or ruzzle
lower-left left portion frame
suffer physical harm emational distress
scratching or biting the boy feel threatened
def fensive reactions
Step 3:
Shallow Cognition
Step 4:
Deep Cognition
This scenario violates social norms and safety practices, approaching animals
Given this setup, the anomaly could invalve the cat reacting sudderly. such as scratching bittng the boy when he leons cleser. a reaction aligns with AtAnimols category, as the cat may feel threatened by the boy's s forward- leaning action.
closely without considering their body larguage can lead to defersive reactions, If the cat scratches on bites, the boy might suffer physical horm emotional distress, emphosizing the importance of respecting animals' boundories prevent such incidents.
Humon'"
Answer
The anomaly category of the video is Arimlls Hurt Humonc/shicho. In this video, sahatho lorge white cot suddenly scratches its/what?, occurring during time range swhem/0.337. 0.5643/whemn. This place in the swhereslower-lee left partion of the framer/whares, where the boy and the cat are interacting in on indoor dining area. This behaviar is considered obnormal becouse swhythhe cat perceive the bey's action as threat and react defersively, which deviates from expected interactiona /why///hy Consequeritly showesuch
bites boy who forward
takes
could result physical injury emotional distress boy. highlighting the violation of safety norms regording animal handling and respect for their boundories/hon
Figure 8: An abnormal example of Vad Reasonng..
Step 1: Perception
Step 2: Cognition
The scene shows well-maintained outdoor with paved walkwny made of light colored tiles Grass, small trees, strubs line. both sides of the path, creating a serene and organized envirommntt People. are casually walking along the pathway, dressed everyday oftire, indicating routine activities such as commuting or leisurely strolls. The setting appears calm and functional, with features like. trash bins and utility poles contributing its design.
Based the observations in the scene, the video is normal and anomalies. The behaviors shown, such as people welking at relexed poce, align with typical actions in a public space, Additionaaly, the clean and orderly environment suggests a standard, well-mointained
for pedestrion use. unusual or disruptive activi activities are present, the events depicted are consistent with everyday life in such setting.
poved walkway
grass, small trees, shrubs line
people walking
walking at a relexed pace
clean and arderly environment
Answer
The video clossified /whicheoormok//hhihb. In this video, whatipeople are cassally walking along poved pathway serene outdoor area grass, trees, shrubs lining both sides, while utility poles trash bins contribute to organized environment//whatt This is considered normal because swhyethe actions displayed. such as walking at a relaxed pace in everyday attire. align with typicol behoviers expected well maintoredd public spoces without ony signs of disruption or unisual activity
Figure 9: A normal example of Vad- hud-Reasoning.
C.4 Evaluation on VANE Benchmark
VANE [15] is a benchmark designed for evaluate the ability of video-MLLMs detect anomalies in the video. It consists of 325 video clips and 559 question-answer pairs, covering both real-world surveillance and Al- Al-generated video clips, and are categorized into nine anomaly types. For real- world anomalies, VANE collect 128 videos clips from existing video anomaly detection datasets (e.g..
CUHK Avenue [37], UCSD-PedI/Pedd Ped1/Ped2 [28], and UCF-Crime [53]). For Al-generated anomalies, VANE includes 197 clips videos generated with SORA [4], OpenSora [16]. Runway Gen2 [49],
SV [65] and VideoL.CM [66]. We report the performance of Vad-R1 I and other MI.LM- based VAD methods on different categories. Notably, since Vad-RI is trained with the proposed
28

---

Your task is to andlyze whether the given video abnormal or rarnal Think before ondwering, and generate 1. structured reasoring process enclosed in -thiklo/thirke Toge
2. fina explonation enclosed <ctwer=s/rreep toge
abnornd videos, the reasoning should based on structured 4-step process: <thino must include following four steps enclosed corresponding fage
ishept> Scene Description
objective overvieu of environmnnt behoviers, without mentioning any activity speculction. Description bescribe
<shep2>: Abnormal
approximate spotial (e.g botton frans), without exploining
why abnormal
<step3:: Abnormal Recogrition Explain why
compored potherss expectations. vislates expectations.
ostep4s: Roosoning
Anolyze potentio negetive consequences exploi
ceherent paragraph owhiche: Define the A'brermal'
longunge, includes exectly the following
What hospored (describe
When hoppered, rernalized frome Where
naherofO 0-4D)///hhem))
qporokmate lal descriptions) corsidered abnormal How behovor could
violcte
For normal videas, the resicning should be simpiiiid sed to just two steps <thinko must include only the following two staps:
<stepix Scene and Object Description - Previde concise and objective everview of the enviremment and typical behoviers, without mentioning anendies. <step2>: Nornal Event Explonction Explon why the video corsidered narmal.
Arower nust be o single, coherent parograph natural longuage, which includes the following three togs
owhichs: Define the video os ('Normal''
owhats: A concise description of the event in the video.
owhyy: Why it is considened normal
Exumple Output for Abrermal Videos: <thino
<stept- vide shows .. c/stepp>
2step2-Nextt we observe abnormal event ./sttsp>> 3nteped those obserwatioss x/stepá
satepdias result, behavior... ./sttep4o
vanswerr of t/inio
neticlo whhiich videa, rehatra pedestrion ..w/hhat,, occurring during the range rahamit B266//whem. place appreximately the oeherexover- of frame//where. This corsidered abnermal because rehyppedcstriard expected w.y/y result, chovosuch behovior could ..shoo..
Output
<shap3> The vides
.,//stap>>
2thep-BBsee described .s/stop2>
</thinke
The video classifted wwhchhoerrnk///tcch... In this vide,, nahatspeople c/whot/whots. This considered normal becouse oahp.../laty
Vansswer>
Figure 10: Prompt template for performing video anomaly reasoning.
Table 9: Reward types and the corresponding values.
Type
Meaning
Value
Accuracy
Evaluate classification result
Format
Evaluate format of output
1
Anomaly verification: Abnormal
Evaluate correctness of videos predicted as abnormal
0.5
Anomaly verification: Normal
Evaluate correctness of videos predicted as normal
-0.2
Length
Evaluate length of output
0.2
Vad-Rea Bad-Reasoning dataset, which incorporates videos from UCF-Crime, we exclude the corresponding UCF-Crime subset from VANE benchmark.
D More Experimental Results
D.1 LLM-Guided Evaluation
In this section, we provide additional LLM-guided evaluations [42, 95] to further assess the reasoning quality of Vad-R1. Table 10 demonstrates the results of pair-wise comparison [95]. Given the output of the our Vad-R1 and other models, we require GPT-4o [43] to compare the two answers and choose the better one. We also consider the impact of the order of answers on the judgment. The results show that GPT-4o overwhelmingly trends to choose Vad-RI in both comparison orders, confirming the superior reasoning quality, contextual alignment, and interpretability its responses. This consistent preference demonstrates that Vad-R1 not only produces semantically accurate descriptions but also exhibits more human-like reasoning process. Table 11 shows the results of double-right [42], which jointly evaluates whether the model can provide a reasonable thinking process and correctly
29

---

Algorithm Anomaly verification reward
Input: Current video v, policy model Re. generated completions = {o:1:-1-
Output: Anomaly verification reward Rano
Init anomaly verification reward: Rano {ri)i=1> where ri 0
2: for each E O do
3: Extract prediction p of u from completion o,
4: if =
Normal then
5:
Randomly discard either the beginning or the ending segment of
6: else
7:
Discard the predicted abnormal segment
8:
end if
9:
Obtain trimmed video
10:
Generate
completion Ô Te p, 8)
11:
Extract new prediction ş of i from new completion ő
12:
if = Abnormal and
Normal then
13:
Assign positive reward r; + 0.5
14:
else if Normal and p Abnormal then
15: Assign negative reward r; < -0.2
16: end if
17:
end for
18: return Rano {r:)
Algorithm 2 AVA-GRPO
Input: Saattaad-essiiiinn RL dataset D = {(e,, ) initial policy model Te...
Output: Updated policy model iT..
1: Init policy model: ny f- TBa
2: Init reference model: Tel f- TT
3: for e (1, (1.... }} do
4: for (J) E D do
5: Generate group of completions 0 fo. TT(: p, p.)))
6:
Compute accuracy reward Race [riji=l
[ri]
7:
Compute format reward R {rii
8:
Compute anomaly verification reward Rano < Algorithm Compute length reward Rien {r: Compute sum Race + Rg + Rano + Rien
9:
Compute advantages A Rt std(R)
11:
12:
Update To with Equation 3
13:
end for
14:
end for
15:
return m
identify the anomaly, thereby offering more comprehensive assessment of the anomaly reasoning capability. In this setting, we adopt semantic similarity to evaluate the correctness of the reasoning process. We adopt Qwenõ-embedding [93] and Jina-embedding- V3 [52] to calculate semantic similarity separately. We observe that Vad-RI achieves the highest RR score 66.29% and 70. 16% under different embedding models, indicating that its reasoning outputs are most aligned with the predictions.
D.2 Experiments More Input Tokens
During both training and inference, the video is uniformly sampled into 16 frames as input, with a maximum pixel count 128 x 28 x 28 per frame. In this section, we increase the number of frames to 32 and 64 per video, and the maximum pixel to 256 x 28 x 28 per frame. The results are shown in Table 64 yields improvement across both anomaly reasoning and detection, showing that the extra frames
the one hand, We observe that increasing the number of frame from to
30

---

Table 10: Pair-wise comparison between Vad-RI and other models.
Model
Rate
Model
Rate
B
A_ win B_ win Tie JA
B
[A_win B_winn Tie
Open-Source video MLLMS
Vad-RI Intern Video2.5 [69]
83,56
12.79
3.65
Intern Video2.5 [69]
Vad-R1
11.42
70.09
18.49
Vad-RI In3ernLL3 [96]
78.82
17.31
3.87
Ln3 [96]
Vad-R1
24.15
50.11
25.74
Vad-RI VideoChat- [29]
90.64
7.76
1.60
VideoChat- Flash [29]
Vad-RI
11.64
74.89
13.47
Vad-R1 Vide [85]
85.93
10.09
3.98
VidcoI.LaMA3 [85]
Vad-R1
13.50
81.90
4.60
Vad-R1 LLaVA-NcXT--Vddee [94]
84.97
11.16
3.87
ALaVA-NcXT-Videe [94]
Vad-R1
14.35
81.55
4.10
Vad-R1 Vw [61]
83.83
11.16
5.01
QWwn2.5-VL [61]
Vad-R1
17.54
70.39
12.07
Open-Source video reasoming MLLMy
Vad-R1 Open-RI-Videe [67]
90.21
7.74
2.05
Openn-RI Video [67]
Vad-R1
5.24
93.62
1.14
Vad-RI Video-RI [14]
82.23
12.07
5.69
Video-R1 [14]
Vad-R1
8.88
86,10
5.01
Vad-RI VideoChat-R R1 [30]
79.95
14.81
5.24
VidcoChat- RI [30]
Vad-R1
18.68
70.62
10.71
MLLM-basedd VAD methods
Vad-R1 Holmes-VAI [88]
80.28
5.57
Holmes-V [88]
Vad-R1
5.92
80.87
13.21
Vad-RI Holmes-VAR [89]
78.54
8.68
12.79
Holmes VAU [89]
Vad-RI
8.45
79.45
12.10
Vad-RI HAWK [54]
77.45
4.33
18.22
HAWK [54]
Vad-R1
4.10
77.90
18.00
MN-GPD
0.5
32
0.3
1.4
Iniuggoo Stp
200
1.k
50C
12c
(a) Total reward.
(b) Std of total reward.
GERO NN-GPP
GPFD AMVERRO
300
491
400
390
250
cwtnildcbll.oo Jalp
12 Abal 569
500
16
1.5k
500
L5k
(c) Comparison on completion length.
(d) Comparison on error rate.
Figure 11: RL training curves of Vad-R1.
provide more useful visual evidence. On the other hand, the benefit of a higher resolution depends on the number of input frames. When increasing the max number of pixels to 256 x 28 x 28 with 16 frames, the model gains small but consistent performance improvement, suggesting that high resolution details compensate for the short clip. In contrast, the performance will drop if we increase the max pixels for 32 frames, possibly due to token redundancy. Consequently, increasing frames is more useful, whereas higher resolution might lead to information overload.
D.3 Training Curves
Figure 11 demonstrates the key training curves of Vad-R1 during RL stage. Figure 11(a) shows the total reward of AVA-GRPO, which increases steadily and converges after approximately 1000 steps, indicating consistent improvement in the degree of matching policy for the output of Vad-R1. Figure 1 (bb) illustrates the standard deviation of total reward, which decreases rapidly in the early stage and stabilizes below 0.1, suggesting that the output quality of Vad-R1 gradually improves as the training progresses. Figure 1 (C(c) compares the completion length between GRPO and AVA-GRPO.
31

---

Table 11: Results of double-right metries with different embedding models.
Qwen3-embedding
Jina-embedding- V3
Model
RRT
RW4
WR1
WW4
RR1
RW
WR1
WW1
Open-Source video
MLLMS
Intern Video2.5 [69]
37.59
25.51
2.05
34.85
41.91
21.18
7.97
28.94
In3 [96]
50.57
27.33
0.23
21.87
56.72
21.18
1.14
20.96
VideoChat- -lash [29]
4.56
63.78
31.66
30.07
38.27
0.00
31.66
Video-LLaMA3 [85]
49.54
21.10
0.00
29.36
55.35
15.29
0.00
29.36
LLaVA-NeXT-Vine [94]
41.23
23.92
0.23
34.62
52.62
12.53
0.91
33.94
Qwen2.5-VL [61]
50.34
25.74
0.00
23.92
56.49
19.59
1.37
22.55
Source video reasoning MLLMs
Open-R1 -i--dRo [67]
26.42
37.59
0.00
35,99
38.95
25,06
0.68
35.31
Video-RI [14]
11.85
50.57
0.23
37.35
30.75
31.66
6.83
30.76
VideoChat-R R1 [30]
48.52
30.75
0.00
20.73
53.99
25.28
1.82
18.91
MLLM-baxed
methods
Holmes VAD [88]
10.93
45.56
0.91
42.60
23.05
33.94
1.14
42.37
Holmes VAU [89]
7.76
27.63
1.14
63.47
20.78
14.61
2.97
61.64
HAWK [54]
5.47
25.97
0.46
68.10
6.15
25.28
1.82
66.75
Proprietary MIL.Ms
Claude3.5-Haiku [2]
44.65
36.90
0.00
18.45
51.94
29.61
1.37
17.08
GPT-4o [43]
48.97
22.10
28.93
56.49
14.58
1.14
27.79
Gemini2.5- [55]
34.02
28.54
0.00
37.44
39.73
22.83
2.05
35.39
Proprietary reasoning MLLMs
Gemini2.5-PP [56]
32.65
50.46
0.00
16.89
60.27
22.83
0.00
16,90
QvQ-Max [60]
42.11
28.38
0.00
29.51
50.11
20.37
0.69
28.83
04-mini [45]
57.37
31.34
0.00
11.29
66.36
22.35
0.23
11.06
Vad-R1 (Ours)
66.29
21.18
0.00
12.53
70.16
17.31
1.14
11.39
Table 12: Performance comparison of different numbers of input frames and spatial resolutions.
Anomaly Reasoning
Anomaly Detection
Frames
Max Pixels
BLEU-2
METEOR ROUGE-2
Acc
mloU
R@0.3
R@0.5
128 x x 28
0.233
0.406
0.194
0.875
0.862
0.713
0.770
0.706
16
256 x 28 x 28
0.238
0,412
0.198
0.886
0.878
0.713
0.772
0,702
128 x 28 x 28
0.242
0.416
0.201
0.900
0.891
0.726
0.786
0.715
32
256 x 28 x 28
0.238
0.413
0.198
0.888
0.883
0.708
0.772
0.695
64
128 x 28 x 28
0.244
0.420
0.203
0.895
0.892
0.709
0.777
0.695
We observe that the reasoning process generated by AVA-GRPO during the training process is shorter in length, presenting more concise and focused responses. Figure 11(d) presents the eтrоr rate comparison, where AVA-GRPO shows a clear downward trend and stabilizes at a lower level than vanilla GRPO. This indicates that AVA-GRPO effectively reduces incorrect reasoning and leads to more accurate and reliable responses during training.
D.4 More Qualitative Results
We provide more qualitative results in Figure 13 and Figure 12. Vad-RI demonstrates stable anomaly reasoning and detection capabilities. On the one hand, Figure 12 shows normal example in Vad- Reasoning dataset. The video shows normal scene campus. Vad-R1 accurately describes the content in the video and identifies its normality. In comparison. GPT-4o and QVQ-Max demonstrate hallucination, pointing out anomalies that do not exist. And although Gemini2.5- reverse walking as a potential abnormal event, it does not take into account the specific scenario of pedestrian walkways. the other hand, Figure shows results on VANE benchmark. When facing unseen videos, Vad-R1 still demonstrates great reasoning capability in complex environments and
considers
32

---

Key of
urbon outdoor area
poved walkway, greenery
modern buildings
people walking
typical pedestrian activinies
Normal video without anomaly
reasoning
Vod-R1 (Ours)
Correct reasoning
Gemini2 5-Flash
Scene - agnostic anomaly:
reverse walking
<stepl> The video
modern urban environment
<stepl> The video shows walkway path in outdoor public
pedestrion pathways, greenery, and architectural fectures like
space. Several people are seen walking various directions
columns and roilings. People are walking, standing.... ./stepl>
the poved path and a gressy area... s/stepi>
<step2: Based on the described scene, the video normal because it depicts typical urban activities in a well-maintained setting. Individuals engage in routine behaviors such walking using their phones, which olign with expected actions in public spaces s/step2-
estep2> Throughout the video, one person observed walking the opposite direction of the general flow of pedestrian traffic on the walkway- </step2
<step3> In public walkway designed for pedestriars, it is expected that people would walk in the same direction... </step3>
GPT-40
Hallucination: theft
of pedestrian traffic could lead to collisions with oncoming people.... 1/step4>
<step4> Walking against
estepl= The video shows a pedestrian area with people walking
casunlly along the poved walkowyy The environment includes trees,
open spaces with grass, and buildings the background </stepl>
QVQ-Max Max
Hallucination: falling
<stepl> The video depicts an outdoor urban setting with poved
<step2: Next, observe someone abruptly rushing to grab the
walkway flonked by gressy
and Several individuals seen walking along the path... .. s/stepl>
backpack of onother pedestrian The event appears to disrupt normal pedestrion behavior. </step2>
<stepZvA person wearing red shirt trips and falls abruptly the poved walkwny </step2>
<step3> Besed
these observations, grabbing someone's backpack is considered abnormal individucls are expected to respect each other's 's personol boundories and property s/step3>
< event deviates from normal pedestrion behaviar, individuals typically maintain balance while </step3>
<step4 As result, this behavior violates social norms of respect and sufety. It could lead to negative consequences such as distress for the victim physical aitercations s/step4>
<stepd The foll poses risks of physical injury to the individual and may disrupt bystanders' movements </step4)
Figure 12: Qualitative result for a normal video.
Question
Which of the fallowing octions digns with the anomaly descr ibsd in the vidko?
Answer: 8) throwing bockpock
Video
Options
sleeping middle the road
tying their shoeloces using callphore
throwing their backpock
HolmesVAU Partially cornect ressoning and wrong answert <thirlo ven the context of the sideo, where indiviluals are walking and interacting with Their surnouddngg the anomaly describe is likely to movoe or unusual or
(OurR) Cornect!
anomaly specifically Fighting momely. chaotic
incluting people which devatis from nereel pesceful himes behnwer ththirk umnowern///ennieer
dieruptive oction Among the options provided "A person throwing their backpack
col altercations, throwing ebjects,
stonds out ns on oct that cauld be considered deruphive or unusual in a public
setting. This behosier controsts with the rout ins oc activitis ies of soling and using a callphone, making a plosiible onondy c/thirlo cantaern /arrowerrs
Wrong reasoning and answer <tinno Bared on the previded video deserigtion. the anomdl described person slooping the middls the Thersfore. the cornect ansver option A: sleaping the middle the rood </thirke ansaers///msseerr
HAWK
HAlmesVAD Connect answer but wrong reasoning!
<thinks The video shows
standing the middle of road holding
backpock, deeningly lost in thought, with behovier </hano cmstaerr Be/nneer///
of cistress unuscal
Figure 13: Qualitative performance on VANE benchmark.
correctly identifies anomalies in the video. In comparison, the reasoning process of Holmes is partially correct, resulting in incorrect judgment, while Holmes VAD makes correct judgment but incorrect reasoning process.
E
Impact and Limitation
In this paper, we propose new task: Video Anomaly Reasoning, which enables MLLM to perform deep analysis and further understanding of the anomalies in the video. We hope our work can contribute to the video anomaly researches.
However, the inference speed of Vad-RI remains limitation, as the multi-step reasoning process introduces additional computational overhead.
33


Reviews:
[{"review_id": "h7w4yfpPvB", "reviewer": "Reviewer_VuPk", "summary": "This paper proposes an MLLM-based method for Video Anomaly Reasoning (VAR) called Vad-R1 and a VAR dataset called Vad-Reasoning. Vad-Reasoning consists of videos from existing datasets and is annotated by the proposed Perception-to-Cognition Chain-of-Thought (P2C-CoT). Vad-R1 is trained with two stages: Supervised Fine-Tuning (SFT) with the P2C-CoT annotated videos and Reinforcement Fine-Tuning (RFT) with video-level weak labels. An improved RL algorithm called Anomaly Verification Augmented Group Relative Policy Optimization (AVA-GRPO) is proposed. Experiments show that Vad-R1 achieves superior VAR performance.", "questions": "1. What is the dataset for Table 1?\n2. What is the loss function used for the SFT step?", "limitations": "See weaknesses", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. This paper applies the novel RFT technology to the VAR task and proposes a task-specific variant, which advances the research progress of the long-established VAD problem.\n2. A new dataset, Vad-Reasoning is constructed, which is useful for training and benchmarking VAR models.\n3. The experiments compare with multiple state-of-the-art video MLLMs, reasoning MLLM, and MLLM-based VAD approaches, which is comprehensive.\n4. This paper is well-written, well-organized, and easy to follow. Its narrative reflects a deep understanding of the field and direction. Figures 2 & 3 help readers understand the proposed method.\n\nWeaknesses:\n1. There are several concerns regarding AVA-GRPO. The reward function considers only two cases: (1) a positive reward for an anomaly video (ground truth) where the prediction is anomaly (original) → normal (trimmed); and (2) a negative reward for an anomaly video (ground truth) where the prediction is normal (original) → anomaly (trimmed). In the first case, what if the prediction for the original video is correct, but the temporal localization is inaccurate? In this situation, the trimmed video may still contain abnormal frames. If the model incorrectly classifies the trimmed video as normal, it would still receive a positive reward. Furthermore, both cases assume the original video is abnormal, and there is no consideration for normal videos. This may lead to an imbalance in training signals.\n2. It seems the training set includes well-annotated anomaly samples, another major concern. According to the standard formulation of anomaly detection, training should be either one-class (only normal data) or weakly supervised (normal data plus weakly labeled anomalies). If the model is trained on fully annotated anomaly samples, the task becomes closer to generic video understanding rather than true anomaly detection.\n3. The metrics used for evaluating reasoning are insufficient. All of them rely on lexical comparisons. It is necessary to include semantic evaluation metrics, such as Doubly-Right [r1, 65] and LLM-as-a-Judge [r2].\n4. According to Figure 4, the evaluation setting involves first performing detection, followed by answering a multiple-choice question (i.e., reasoning). This setup appears to match the one described in Line 34, which contradicts the stated motivation of this work.\n5. According to Figure 4, the evaluation approach appears more aligned with action recognition than with VAD. This makes the claimed VAD capabilities less convincing.\n6. (minor) The taxonomy of anomalies stated in L155 is similar to [65].\n7. (minor) In L233, single choice questions -> multi-choice questions. In L98 and L108, Language Model -> Language Models.\n\nJustification:\n\nThis paper applies the most advanced technology to the VAD task, which is a good contribution. However, there are concerns about the proposed method, problem formulation, and evaluation. I suggest Borderline Reject at this stage.\n\n[r1] Mao, Chengzhi, et al. \"Doubly right object recognition: A why prompt for visual rationales.\" CVPR, 2023.\n\n[r2] Zheng, Lianmin, et al. \"Judging llm-as-a-judge with mt-bench and chatbot arena.\" NeurIPS, 2023.", "was_revised": true, "final_justification": "The authors’ rebuttal has addressed some of my concerns; however, one of my primary concerns remains (Weakness 2): if well-annotated anomaly samples are used for training, Video Anomaly Reasoning becomes just a sub-problem of general video understanding reasoning. Therefore, I am raising my rating to 4: Borderline Accept (but not higher).", "rebuttal": "We are deeply grateful for your thoughtful feedback and valuable suggestions. We appreciate your recognition of our framework, dataset, experiments, and writing. In the following, we carefully respond to each comment and provide additional explanations.\n***\n# Response to weakness 1: Concerns of AVA-GRPO's implementation details\n**Reward bias caused by inaccurate temporal localization**: We acknowledge that inaccurate predictions of the temporal boundaries of anomalies may potentially affect the reward calculation. However, we emphasize that AVA-GRPO is essentially a self-supervised verification mechanism, which is designed to approximately evaluate the correctness of temporal localization under weak supervision. In practice, we first perform uniform sampling on the video due to the contextual limitation. When computing the reward, we perform the trimming operation on the raw untrimmed video, followed by resampling, rather than trimming on the already sampled frame set. This ensures that even with inaccurate localization, the trimmed video can still contain enough abnormal frames to support detection, preserving the robustness of the reward signal.\n\n**The absence of explicit rewards for normal videos**: The AVA-GRPO is biased toward reinforcing anomaly understanding, which aligns with the nature of task. The MLLM has acquired general video understanding capabilities during the **pre-training** stage. And the anomalies are often human-defined, thus we hope that the model can learn the pre-defined anomaly patterns during **post-training** (SFT+RL) stage. Additionally, our training set also includes normal videos, ensuring the model retains an understanding of normal patterns and avoids catastrophic forgetting.\n***\n# Response to weakness 2: Clarification with additional annotations\n**Differences in Tasks**: We do not aim to solve traditional anomaly detection, but rather address a novel and more challenging task—Video Anomaly Reasoning (VAR), which aims to not only determine whether a video is abnormal, but also to gradually infer complex semantic information. In this task setting, the model needs to possess causal reasoning capabilities similar to those of humans, which cannot be learned merely through traditional VAD Settings (such as only normal data or video-level labels). Therefore, we simulate the process of human thinking and introduce the Perception-to-Cognition Chain-of-Thought. Based on this, we construct a structured annotated dataset (Vad-Reasoning-SFT) for the first stage of supervised fine-tuning (SFT) to help the model master the basic reasoning logic.\n\n**Our training strategy**: The training pipeline of Vad-R1 is divided into two stages: (1) The first stage (SFT) uses **a small number** of videos (1755) with detailed reasoning annotations, (2) The second stage (RL) involves a **large-scale** dataset (6448 videos) that contains only video-level weak labels. Therefore, the overall training strategy remains consistent with the real-world challenge conditions of anomaly detection, where fully annotated anomaly samples are scarce, and models must learn from weak supervision. \n***\n# Response to weakness 3: Additional evaluation metrics\nWe agree the limitations of using only lexical-level metrics (e.g., BLEU, METEOR, ROUGE) for evaluating reasoning quality. Therefore, referring to HAWK[r1], we evaluated the reasoning results with the proprietary model in three aspects: Reasonability, Detail, and Consistency. Due to the length limitation of the main text, the complete results are presented in Table 4 of the supplementary materials. Some of the results are shown below:\n|Method|Reasonability|Detail|Consistency|\n|-|-|-|-|\n| Holmes-VAD |0.388|0.275| 0.343 |\n| Holmes-VAU |0.385 | 0.301| 0.375 |\n| HAWK | 0.218 | 0.185 | 0.115 |\n| Claude3.5-Haiku  | 0.711 | 0.637 | 0.611 |\n| QvQ-Max | 0.690 | 0.639 | 0.521 |\n| GPT-4o | 0.724 | **0.679** | 0.542 |\n| **Vad-R1 (Ours)**| **0.734** | 0.659 | **0.662** |\n\nIt can be observed that Vad-R1 demonstrates superior performance, particularly in terms of Reasonability and Consistency. In addition, based on the reference paper you provided, we supplement the following experiments:\n\n**Doubly-Right**: In this setting, we adopt similarity to evaluate the correctness of the reasoning process. We use qwen3-embedding[r2] to encode text to calculate similarity. The results are as follows\n| Model | RR ↑ | RW ↓ | WR ↓ | WW ↓ |\n|-|-:|-:|-:|-:|\n| Holmes-VAD|10.93 |45.56 |0.91 |42.60 |\n| Holmes-VAU |7.76 |27.63 |1.14 |63.47 |\n| Hawk | 5.47 | 25.97 |0.46 |68.10 | \n| Claude-3-5-haiku | 44.65 | 36.90 | 0.00 |18.45 |\n| GPT-4o |48.97 |22.10 |0.00 |28.93 |\n| Gemini-2.5-flash |34.02 |28.54 |    0.00 |   37.44 |\n| Gemini-2.5-pro |32.65 |50.46 |    0.00 |   16.89 |\n| QVQ-max|42.11 |   28.38 |0.00 |   29.51 |\n| o4-mini| 57.37 |   31.34 |0.00 |   11.29 |\n| **Vad-R1 (Ours)**| **66.29** | 21.18 | 0.00 | 12.53 |\n\nWe observe that Vad-R1 achieves the highest RR score of 66.29%, indicating that its reasoning outputs are most aligned with the predictions.\n\n**LLM as Judge**: In this setting, we consider pairwise comparison. We choose GPT-4o as the judge, providing it with the outputs of Vad-R1 and other models, and requiring it to choose a better answer. We also consider the impact of the order of answers on the judgment. The results are as follows\n\n| Model_A  | Model_B  | A_win_rate | B_win_rate | Tie_rate |\n|-|-|-|-|-|\n| **Vad-R1**  | Hawk | **77.45**  | 4.33 | 18.22 |\n| **Vad-R1** | Holmes-VAD | **80.28**  | 5.57 | 14.15 |\n| **Vad-R1**  | Holmes-VAU | **78.54**  | 8.68| 12.79|\n| Hawk | **Vad-R1** | 4.10 | **77.90**  | 18.00 |\n| Holmes-VAD  | **Vad-R1** | 5.92 | **80.87**  | 13.21 |\n| Holmes-VAU  | **Vad-R1** | 8.45 | **79.45**  | 12.10 |\n\nWe observe that Vad-R1 is significantly superior to other methods, and GPT-4o tends to choose the generated results of Vad-R1 under two conditions.\n\n***\n# Response to weakness 4 & 5: Clarification of the evaluation setting\n**Evaluation setting**: Our evaluation consists of two aspects: on our test set (Table 1, 2, and 4) and the VANE benchmark (Table 3 and Figure 4). On the test set, the model performs standard anomaly reasoning (thinking before answering).  Figure 4 illustrates an example from the VANE benchmark, which is designed in the form of \"video + multiple-choice question.\" In this setting, the model directly receives the entire video, the question and the options. The model first thinks (enclosed with \\<think> tags) and then answers the given question (enclosed with \\<answer> tags), which is essentially different from the one described in Line 34. Our model does not rely on any external modules but completes reasoning and decision-making **end-to-end**. Besides, due to the length limitation of main text, we provide the qualitative results of evaluation on our test set in supplementary materials.\n\n**Evalutaion method on VANE**: The purpose of the VANE benchmark is to evaluate the understanding ability of MLLMs for abnormal events. Although it is a \"multi-choice question\" in form, the abnormal content involved in its options requires the model to have the ability of abnormal behavior recognition, semantic understanding and reasoning. So we regard the evaluation on the VANE benchmark as a supplementary way to demonstrate the performance of Vad-R1 under complex reasoning tasks.\n***\n# Response to weakness 6: Difference with AnomalyRuler on taxonomy of anomalies\n\nBoth our Vad-R1 and AnomalyRuler[r3] indeed categorize anomalies from the perspectives of human activities and environmental/contextual elements. But there are several key distinctions:\n\n**Purpose of Taxonomy**: AnomalyRuler uses the taxonomy primarily for rule induction from few-shot normal frames.  In contrast, our taxonomy aims to cover possible anomalies in real life. Based on the taxonomy, we can build our dataset and further construct the reasoning annotation.\n\n**Granularity and application**: We define fine-grained subcategories across multiple sources, supporting high-quality reasoning annotations and enabling both detection and open-ended causal reasoning. AnomalyRuler's rules are relatively abstract.\n***\n# Response to weakness 7: Inappropriate expression\nThank you for pointing out the inappropriate expression. We will correct the issues in the revised version.\n***\n# Response to question 1 & 2: Unclear descriptions\n**Dataset for Table 1**: The experiments in Table 1 are conducted on the test set of Vad-Reasoning we proposed (the same as those in Table 2 and 4).\n\n**Loss function for SFT**: The SFT stage employs the standard Token-level Cross-Entropy Loss, which maximizes the likelihood probability of the generated reasoning process. It can be formulated as: \n$$\nL=-\\sum_{t=1}^T \\log P(y_t \\mid y_{<t}, x)\n$$\nwhere $y_t$ is the ground truth token at time step $t$, $y_{<t}$ is the sequence of previous tokens, and $x$ denotes the multimodal input.  \n***\nWe sincerely appreciate your comments once again and will revise the paper accordingly. We are looking forward to further discussion during next phase.\n\n> [r1] HAWK: Learning to Understand Open-World Video Anomalies, NeurIPS 2024  \n> [r2] Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models, arXiv 2506  \n> [r3] Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models, ECCV 2024"}, {"review_id": "5M507HYBoX", "reviewer": "Reviewer_PJyt", "summary": "This paper introduces Vad-R1, an end-to-end MLLM-based framework for Video Anomaly Reasoning (VAR). The authors propose a new task that goes beyond traditional video anomaly detection by requiring models to perform structured reasoning about anomalous events. The framework consists of three main components: (1) a Perception-to-Cognition Chain-of-Thought (P2C-CoT) that guides step-by-step reasoning from global perception to deep cognition, (2) the Vad-Reasoning dataset with fine-grained anomaly categories and reasoning annotations, and (3) a two-stage training pipeline combining supervised fine-tuning with a novel reinforcement learning algorithm called Anomaly Verification Augmented GRPO (AVA-GRPO).", "questions": "-\tDataset Annotation Methodology: While the use of proprietary models for annotation generation is understandable given the scale requirements, the paper would benefit from more detailed analysis of annotation quality. Some human validation samples or inter-model consistency analysis would strengthen confidence in the dataset quality.\n-\tAVA-GRPO Analysis Depth: Although the ablation study shows the effectiveness of the complete pipeline, a more detailed analysis of how the anomaly verification mechanism specifically contributes to performance would be valuable.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "***Strengths***\n\n-\tThe paper is well written.\n-\tThe authors conduct extensive comprehensive comparisons against various baselines including general video MLLMs, reasoning-enhanced models, and proprietary systems, demonstrating consistent improvements.\n\n***Weaknesses***\n\n-\tDataset Quality Concerns: The paper's reliance on proprietary models (Qwen-Max and Qwen-VL-Max) for generating reasoning annotations is problematic. The authors provide insufficient analysis of annotation quality, consistency, or validation. There is no human evaluation of the generated reasoning chains, no inter-annotator agreement metrics, and no analysis of potential biases inherited from the proprietary models. This raises serious questions about the reliability of both the training data and evaluation benchmarks.\n-\tLimited AVA-GRPO Analysis: While the paper introduces AVA-GRPO as a key contribution, the ablation study in Table 4 only compares the full pipeline against individual components. There is no direct comparison between AVA-GRPO and standard GRPO on the same dataset, making it impossible to assess the specific contribution of the anomaly verification mechanism. The paper lacks analysis of how the verification rewards affect training dynamics or convergence.", "was_revised": true, "final_justification": "After carefully reading the authors response and comments by other reviewers, I decided to keep my original rating with an increased level of confidence. The authors have provided a comprehensive rebuttal, addressing key concerns of my reviews. While I agree on reviewer #VuPk on the limitation regarding the anomaly sample annotations, I believe the paper is valuable for the research community on the topic.", "rebuttal": "We are truly grateful for your insightful comments and your recognition of our experimental results and writing. In response to your concerns, we provide the following clarifications and explanations.\n***\n# Response to weakness 1 & question 1:  Clarification on annotation quality\n\n**Data generation strategy**: To mitigate noise, inconsistency, and bias in LLM-generated data, we carefully designed a multi-stage annotation pipeline with the following components:\n\n* **Step-by-step generation**: The reasoning process is decomposed into four structured steps. Instead of generating the entire Chain-of-Thought (CoT) in one pass, each step is generated independently using tailored prompts. This design helps reduce error propagation and enhances the clarity and logical consistency of each reasoning step.\n* **Explicit anomaly definition**: To guide the generation process and ensure alignment with the anomaly definition, we provide explicit, pre-defined anomaly categories as input to the proprietary model. This reduces ambiguity and ensures relevance to the intended anomaly pattern.\n* **Output constraints**: We enforce constraints on model outputs, including relevance, objectivity, and neutrality, to minimize hallucinations and ensure the generated reasoning is grounded in video content.\n* **Summary and compression**: Given the high information density of videos, intermediate steps may result in verbose outputs. To address this, we introduce a refinement stage, where the model summarizes each reasoning step into concise 3–5 sentence paragraphs, improving readability and preserving essential content.\n* **Human verification**: To validate the quality of generated annotations, we conduct manual verification by volunteers with expertise in video anomaly detection. The volunteers check the outputs based on accuracy, coherence, and completeness.\n\n**Consistency analysis**: We conduct an inter-model consistency study. Specifically, we randomly select a subset of videos and apply the same prompt to generate reasoning process with different proprietary models. We then measure the semantic similarity between the generated annotations. The results are as follows\n\n| Similarity | Qwen-Max| GPT-4o | Gemini2.5 | Claude3.5 |\n|-|-|-|-|-|\n| **Qwen-Max** | 1 | 0.9059 | 0.8963 | 0.8948 |\n| **GPT-4o** | 0.9059 | 1 | 0.9068 | 0.8948 |\n| **Gemini2.5**| 0.8963 | 0.9068 | 1 | 0.8948 |\n| **Claude3.5**| 0.8948 | 0.8948 | 0.8948 | 1 | \n\nThe results show that the outputs from different models exhibit high semantic alignment, suggesting that the generated annotations are not overly dependent on a specific model.\n***\n# Response to weakness 2 & question 2: Further analyses of AVA-GRPO\n\n**Ablation study on rewards**: The proposed AVA-GRPO contains additional reward for anomaly verification and the reward to constrain output length. We conducted ablation experiments on these two rewards. Due to the length limit of the main text, we present the results in the Table 6 of supplementary materials. Some of the results are as follows\n\n|Strategy| ROUGE-L | ROUGE-2 | Precision | mIoU  | R\\@0.3 | R\\@0.5 | R\\@0.7 |\n|-|-|-|-|-|-|-|-|\n| GRPO  | 0.502   | 0.475   | 0.861     | 0.712 | 0.770 | 0.699 | 0.640 |\n| GRPO+len_reward | 0.529   | 0.501   | 0.856| 0.710 | 0.770 | 0.697 | 0.642 |\n| GRPO+ano_reward | 0.496   | 0.467| 0.866 | 0.707 | 0.765 | 0.695 | 0.638 |\n| **AVA-GRPO**| **0.530** | **0.501** | **0.882** | **0.713** | **0.770** | **0.706** | **0.651** |\n\nIt can be seen from the results that after introducing additional rewards, the performance of AVA-GRPO in anomaly reasoning and detection has been enhanced. Additionally, we can also observe that the performance improvement of adding only a single reward is unstable, which proves the effectiveness of the combination of two rewards.\n\n**Training dynamics**: We monitored and visualized the evolution trend of rewards and output lenght during the training process as shown in Figure 6 of supplementary materials. Besides, we compare the differences in training curves between the original GRPO and our proposed AVA-GRPO, and the specific results can be summarized as follows\n\n* On the **accuracy_reward** curve, we observe that the accuracy reward obtained by the AVA-GRPO model is significantly higher than that of the original GRPO.\n* On the **output_length** curve, we observe that the reasoning process generated by AVA-GRPO during the training process is shorter in length, presenting more concise and focused responses.\n\nIn conclusion, AVA-GRPO not only optimizes the final performance but also demonstrates superior stability and efficiency during training.\n\n***\n\nWe sincerely appreciate your valuable feedback once again. We will revise the paper accordingly and we are looking forward to further discussion during the next phase."}, {"review_id": "r9EIP85zQh", "reviewer": "Reviewer_KKNF", "summary": "This paper focuses on a new form of task based on video anomaly detection called video anomaly reasoning. This paper includes a new pipeline named P2C-CoT and a new dataset named Vad-Reasoning. Based on that, the paper conducts a training based on reinforcement learning called AVA-GRPO. The model is tested on anomaly reasoning and anomaly detection setting in this Vad-Reasoning as a text generation task. It also tests on the VANE benchmark.", "questions": "1. Are the used metrics objective and able to reflect the truthfulness of the reasoning in video anomaly reasoning? Why?\n2. Performing R1-style training is interesting and would be the main technical contribution in this paper. However, is the training easy to run on Vad-Reasoning? What's the biggest obstacle in running R1 style training in the proposed dataset with MLLMs? What are the tricks we need to use to overcome the training difficulty? What's the additional cost for training on Vad-Reasoning like?\n3. How's the ratio of training/test videos in Vad-Reasoning decided? The test video has a relatively small amount which looks unreasonable.", "limitations": "In addition to the additional computational overhead, the training cost can be limited and is not detailed in the paper.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-motivated by improving the traditional video anomaly detection into a video anomaly reasoning task. This will help better improve MLLM's reasoning ability. \n2. The proposed pipeline based on R1-style training is reasonable. This has been proved successful in training a MLLM with good reasoning and now it moves to the application of video anomaly reasoning. \n3. The proposed dataset Vad-Reasoning is a good contribution to this field.\n\nWeaknesses: \n1. The used metrics like BLEU-2, METEOR, and ROUGE-2 are reasonable for text generation task. However, it lacks an explanation of whether they are good metrics for reflecting the true reasoning ability of MLLMs. This metric can be biased or inaccurate and the paper didn't explain whether they are suitable. Or in other wods, common ones used in video anomaly detection task is AUC, now we move to video anomaly reasoning and use those text generation metrics, are they really suitable? This is a question not answered in the paper.\n2. Despite the introduction of Vad-Reasoning, whether it will be made open is not discussed in the paper or supplementary material. The number of training/test videos in the datasets look unreasonable, i.e., the number of test video is small compared to training videos.\n3. Besides the Vad-Reasoning dataset, the proposed method is more like a direct application of DeepSeek-R1 training. The paper does have some customization for the task, but the novelty is limited.", "was_revised": true, "final_justification": "The authors have provided new results and a better explanation. The final score is adjusted based on that.", "rebuttal": "We greatly appreciate your valuable feedback. We also appreciate your recognition of our framework, training strategy, and dataset. We will respond to your questions in the following sections.\n***\n# Response to weakness 1 & question 1:  Clarification on the effectiveness of evaluation metrics\n**Difference between tasks**: Traditional video anomaly detection focuses on identifying whether an anomaly occurs, typically measured via frame-level metrics like AUC or AP. In contrast, our proposed video anomaly reasoning aims to further analyze and understand the anomalies in the video, explaining the cause and consequence of anomalous events in natural language. This shift from simple detection to structured reasoning naturally calls for text quality evaluation. \n\n**Justification for text-based metrics**: We adopt BLEU, METEOR, and ROUGE to measure the lexical similarity between the generated and reference reasoning process, which are common in open-ended generation tasks and have also been used in prior MLLM-based video anomaly understanding studies such as HAWK[r1] and Holmes-VAU[r2].\n\n***\n# Response to weakness 2 and question 3: Training/test split design and release plan of dataset\n\n**Design of training/test ratio**: To simulate real-world application scenarios, our training set is divided into two parts: Vad-Reasoning-SFT with a small number of detailed annotated videos, and Vad-Reasoning-RL with a large number of videos with only video level labels. The test set follows the same annotation format as the Vad-Reasoning-SFT subset. If we consider only the SFT portion of the training set, the ratio of test to training set is approximately 1:4, which is a standard and reasonable setting. The large-scale Vad-Reasoning-RL subset requires only a small amount of annotation cost and can be cheaply scaled in practical applications. The detailed statistical analysis of the dataset is provided in supplementary materials. We also conduct ablation experiments on different data scale of Vad-Reasoning-RL (30%, 60% and 100%). The results are as follows\n\n|Ratio | BLEU| ROUGE| Precision | R\\@0.5 | R\\@0.7 |\n|-|-|-|-|-|-|\n| 30% | 0.507 | 0.427 | 0.734 | 0.638 | 0.546 |\n| 60% | 0.510 | 0.465 | 0.773 | 0.645 | 0.585 |\n| **100%** | **0.510** | **0.468** | **0.882** | **0.706** | **0.651** |\n\nWe observe that increasing the amount of RL data consistently improves model performance, demonstrating the effectiveness of our data structure design in enabling practical scalability.\n\n**Release plan**: We confirm that the Vad-Reasoning dataset, including the full reasoning annotations, will be released publicly in compliance with NeurIPS 2025 policies.  \n\n***\n# Response to weakness 3: Novelty of our work\nIn this work, although the training paradigm of Vad-R1 draws on the idea of Deepseek-R1, Deepseek-R1 is a plain text model. In response to the unique requirements of the multimodal video anomaly detection tasks, we have made the following key customizations and innovations:\n\n**A challenging new Task**: Unlike traditional video anomaly detection, the proposed video anomaly reasoning aims to generate a causal and complete natural language reasoning chain from the video. This is an important expansion of the video anomaly task, and it also poses new challenges to the model structure and training paradigm.\n\n**Structured Chain of Thought**: Abnormal events are usually defined by humans. Therefore, we simulate the process of human thinking and propose the Perception-to-Cognition Chain-of-Thought, dividing the video anomaly reasoning process into four explicit stages. This design can guide the model to understand the anomalies step by step.\n\n**Customized reward design**: The proposed AVA-GRPO does not simply follow the existing RL method, but specifically constructs an **Anomaly Verification Reward** mechanism for the annotation limitation of the video anomaly reasoning task. It aims to enhance the model's abnormal reasoning ability without introducing additional annotations.\n\nTherefore, we believe that although the design of Vad-R1 is based on the general training paradigm, it has made targeted customizations, constituting a systematic innovation for the video anomaly task.\n\n***\n# Response to question 2 and limitations: Feasibility of R1-style training\nOur main technical contribution lies in adapting and validating the R1-style training paradigm in the context of video anomaly task, which to our knowledge has not been explored in prior works. In the following, we explain the key obstacles and our solutions for R1-style training.\n\n**Key obstacles in training Vad-R1**: \n\n* **SFT cold start with structured reasoning data**: Unlike generic reasoning tasks, video anomaly reasoning requires the model to perform multi-step, structured reasoning. However, existing video anomaly detection datasets lack such annotations. Besides, starting RL directly from a base model leads to unstable training and suboptimal reasoning quality.\n\n* **Reward design under video level labels**: Given the high annotation cost, large-scale fine-grained annotation is unavailable. Thus, designing stable and effective reward functions is essential for enabling RL training under video level weak supervision.  \n\n**Our solutions**:  \n\n* **Perception-to-Cognition Chain-of-Thought**: We propose a novel structured anomaly reasoning chain, which simulates the human reasoning flow from perception to cognition. This provides a structured and interpretable format for supervision and evaluation.\n\n* **Vad-Reasoning dataset**: Based on the proposed CoT, we construct Vad-Reasoning-SFT dataset to support SFT training, where the videos are annotated with detailed reasoning process. In addition, we also construct a larger dataset Vad-Reasoning-RL for RL training, where videos only have video level weak labels.\n\n* **AVA-GRPO**: We extend original GRPO with an anomaly verification reward, which evaluates model outputs through self-consistency, promoting the anomaly reasoning ability of the model without additional annotations. Besides, a length reward is added to constrain the output length. \n\n**Training cost**: We keep the training cost practical. All training are conducted with four A100 GPUs. It takes about 6 hours and 30 hours for SFT and RL training, respectively. Considering the complexity of the video anomaly reasoning task and high cost of annotation, we believe the training cost is justified and acceptable.\n\n***\nWe extend our deepest gratitude to your comments again. All suggested revisions will be incorporated into the paper. We are looking forward to further discussion during the next phase.\n\n> [r1] HAWK: Learning to Understand Open-World Video Anomalies, NeurIPS 2024  \n> [r2] Holmes-VAU: Towards long-term video anomaly understanding at any granularity, CVPR 2025"}, {"review_id": "PYBQ5QmHhX", "reviewer": "Reviewer_1Ake", "summary": "The paper addresses the problem of video anomaly detection by leveraging the reasoning ability of multi-modal large language models. The idea is quite straightforward. The paper first curates a structured CoT dataset, then finetune a MLLM with this dataset using SFT, and finally train the MLLM using RL with verifiable reward. The results show the effectiveness of the proposed method on a few standard VAD benchmarks.", "questions": "See the weakness section.", "limitations": "n/a", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n- The idea is simple and the presentation is clear.\n\n- The training pipeline is pretty standard, consisting supervised finetuning and reinforcement learning with verifiable reward.\n\n- The results look good.\n\nWeakness\n\n- The novelty is low. The paper essentially applies a standard reasoning elicitation approach (SFT, RLVR on long CoT data) to video anomaly detection. I fail to see any significant contribution beyong this simple application.\n\n- A few baseline results are missing. For example, the author mentioned VERA (arXiv:2412.01095), but did not compare it in the experiment. There are quite a few MLLM-based VAD methods, and the paper didn't compare to them.\n\n- The contribution of the long CoT data can be problematic. The annotation is fully dependent on the model output, without any refinement or debiasing strategies. Moreover, no ablation study of the dataset size and distribution is conducted.", "was_revised": true, "final_justification": "The authors' rebuttal didn't fully convince me. I still think VAR and explainable VAD are fundamentally similar task. \n\n- They are just of different interpretation format. \n\n- I think the overall novelty is the application of RLVR to VAD. The contributions are rather limited, which is the major reason that I maintain my negative score.", "rebuttal": "We sincerely appreciate your rigorous comments. While some critiques reflect fundamental concerns, we recognize their intent to enhance the paper's robustness. Below, we address each comment with analyses and clarifications.\n***\n# Response to weakness 1: Clarification on contribution and novelty\nOur work is not just a simple application of the existing SFT+RL paradigm, it contains deep customization for video anomaly task, which is reflected in the following key aspects:\n\n* **Introduction of new task**: We propose a new task formulation, video anomaly reasoning (VAR), which moves beyond traditional video anomaly detection (VAD). Unlike VAD which focuses solely on whether an anomaly occurred, VAR requires models to generate causally complete natural language reasoning chains that explain what happened, why it happened, and which social norms were violated. This represents a substantial shift from perception to cognition in video understanding, and introduces new challenges for model design, supervision, and evaluation.\n\n* **Structured reasoning process**: Rather than prompting the model to simply \"explain the video,\" we introduce a structured Chain-of-Thought in order to guide the model to perform structured anomaly reasoning. This explicit design enables the model to learn a stable and interpretable reasoning structure, and lays the foundation for both reward engineering and structured evaluation.\n\n* **Vad-Reasoning dataset**: The existing VAD datasets only provide video level labels or brief descriptions, lacking detailed reasoning annotations. The proposed Vad-Reasoning dataset is composed of two subsets. Vad-Reasoning-SFT (1755 videos with CoT annotations) for SFT cold start and Vad-Reasoning-RL (6448 videos with video level weak labels) for RL training. This design has scalability while saving annotation costs.\n\n* **Anomaly verification reward**: We propose AVA-GRPO, a novel variant of GRPO that introduces an anomaly verification reward, which requires the model to justify its predictions via video trimming and re-generation. It aims to enhance the model's anomaly reasoning ability without introducing additional annotations.\n\nIn conclusion, our work is not a direct application of existing methods into the video anomaly domain. Instead, it presents substantive contributions centered around the novel video anomaly reasoning task, including innovations in task formulation, reasoning process design, dataset design and reinforcement learning algorithms.\n***\n# Response to weakness 2: Clarification on missing baselines\n\n**The reasons for not choosing VERA**: VERA is essentially a traditional anomaly detection method while our Vad-R1 focuses on end-to-end video anomaly reasoning. VERA guides **frozen** MLLM to explain anomaly events in videos by learning a set of \"trainable guiding questions\". However, since the MLLM is frozen and no additional modules are introduced, if VERA is used as the baseline, it is equivalent to directly running its base model (InternVL2[r1] is used in its official code). In the main experiment, we have already compared the more advanced models of the same series (InternVideo2.5[r2] and InternVL3[r3]), so we did not take VERA as the baseline. Nevertheless, in response to the reviewer's suggestion, we add experiments comparing with VERA, and the results are as follows\n\n| Method | BLEU | METEOR | ROUGE | Acc | F1 | mIoU| R\\@0.3 | R\\@0.5 |\n|-|-|-|-|-|-|-|-|-|\n| VERA| 0.131 | 0.294 | 0.117  | 0.469 | 0.633 | 0.150 | 0.194  | 0.100 |\n| **Vad-R1** | **0.233** | **0.406**  | **0.194**  | **0.875** | **0.862** | **0.713** | **0.770**  | **0.706** |\n\nWe observe that Vad-R1 achieves significantly better performance across all aspects, confirming that our end-to-end reasoning framework is more effective than detection-oriented pipelines like VERA.\n\n**Regarding other MLLM-based VAD methods**: On the one hand, although some methods employ MLLM, they still focus on traditional video anomaly detection. The MLLMs are only used as auxiliary modules. After the classifier gives the anomaly score, MLLM provides auxiliary explanations, such as AnomalyRuler[r4], VERA[r5] and LAVAD[r6]. In comparison, our Vad-R1 is an **end-to-end** anomaly reasoning model and does not require additional detection steps. On the other hand, some methods use MLLM for anomaly understanding. However, not all methods have open-sourced their code or model weights, such as Sherlock[r7] and AnomShield[r8]. So in the experiment, we only compare HAWK[r9], Holmes-VAD[r10] and Holmes-VAU[r11].\n\nIn conclusion, within the scope of task type and reproducibility, we select the most representative and comparable MLLM-based method as baseline.\n***\n# Response to weakness 3: Clarification on annotation quality  \n**Data generation strategy**: We carefully design our annotation pipeline to mitigate noise and bias typically associated with LLM-generated data, which contains several key components:\n\n* **Step-wise generation**: The reasoning process is composed of four explicit steps. Rather than generating the entire reasoning chain in one shot, we generate each step independently with different dedicated prompts. This decomposition reduces error propagation and improves clarity and correctness.  \n\n* **Explicit anomaly definition**: During generation, we explicitly provide pre-defined anomaly types to the proprietary model, ensuring that outputs align with our task design.\n\n* **Output constraints**: We add several output constraints into the prompts, including relevance, objectivity, and neutrality, to prevent hallucinations and ensure high-quality reasoning chains.  \n\n* **Summary and calibration**: Given the information density in video content, each step may produce verbose outputs. To ensure clarity and conciseness, we introduce a final summary and calibration stage, where the proprietary model condenses each step into a refined 3–5 sentence paragraph. This guarantees that the final CoT remains focused and refined.\n\n* **Human verification**: After the data generation process, to ensure the quality of the data, we invite volunteers with backgrounds in video anomaly detection to manually check the generated annotations. \n\n**Data distribution and size ablation**: We perform ablation experiments on the data size of two training subsets.\n\n* **SFT**: The Vad-Reasoning-SFT dataset contains three major categories of anomaly: human anomaly, environment anomaly and object anomaly. We conduct ablation experiments on the categories of anomaly, and the results are as follows\n\n| Environment | Object | Human | BLEU  | ROUGE | METEOR | Recall |\n|-|-|-|-|-|-|-|\n| √|  | | 0.446  | 0.379  | 0.357  | 0.814  |\n|  | √ | | 0.426  | 0.359  | 0.344  | 0.485  |\n| | | √ | 0.456  | 0.376  | 0.362  | 0.701  |\n| √| √ | | 0.503  | 0.414  | 0.396  | 0.809  |\n| √|  | √ | 0.501  | 0.416  | 0.399  | 0.853  |\n|  | √ | √| 0.507  | 0.414  | 0.400  | 0.828  |\n| √| √ | √ | **0.513** | **0.455** | **0.411** | **0.971** |\n\nIt can be observed that model trained with all categories achieves the best overall performance. These results confirm that diverse anomaly categories contribute positively to reasoning quality, and highlight the importance of balanced and comprehensive training data.\n\n* **RL**: We conduct ablation experiments on different data scales of Vad-Reasoning-RL dataset (30%, 60% and 100%). The results are as follows\n\n|Ratio | BLEU  | ROUGE | Precision | R\\@0.5 | R\\@0.7 |\n|-|-|-|-|-|-|\n| 30% | 0.507 | 0.427 | 0.734 | 0.638 | 0.546 |\n| 60% | 0.510 | 0.465 | 0.773 | 0.645 | 0.585 |\n| **100%** | **0.510** | **0.468** | **0.882** | **0.706** | **0.651** | \n\nThe results show consistent performance improvement as the amount of RL data increases, highlighting that our dataset design supports practical expansion with minimal annotation cost.\n\n***\nWe deeply appreciate your rigorous comments, which has driven significant refinements to our methodology and interpretations. We will revise the paper accordingly and we are looking forward to further discussion during next phase.\n\n> [r1] Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks, CVPR 2024  \n> [r2] Internvideo2. 5: Empowering video mllms with long and rich context modeling, arXiv:2501  \n> [r3] InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models, arXiv:2504  \n> [r4] Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models, ECCV 2024  \n> [r5] VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models, arXiv:2412  \n> [r6] Harnessing Large Language Models for Training-free Video Anomaly Detection, CVPR 2024  \n> [r7] Sherlock: Towards Multi-scene Video Abnormal Event Extraction and Localization via a Global-local Spatial-sensitive LLM, ACM WWW 2025  \n> [r8] Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomaly, arXiv:2412  \n> [r9] Hawk: Learning to understand open-world video anomalies, NeurIPS 2024  \n> [r10] Holmes-vad: Towards unbiased and explainable video anomaly detection via multi-modal llm, arXiv:2406.12235  \n> [r11] Holmes-vau: Towards long-term video anomaly understanding at any granularity, CVPR 2025"}]

Author rebuttal:

Meta-review: This paper has received 3 weak accept ratings and 1 weak reject rating. The authors’ rebuttal has addressed most of the issues raised by the reviewers. Video Anomaly Reasoning is a highly interesting innovation, and the proposed dataset also contributes positively to research in this direction. Therefore, I recommend accepting the paper.
Decision: Accept (poster)