A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis
Authors: Dongheng Lin
Mengxue Qu
Kunyang Han
Jianbo Jiao
Xiaojie Jin
Yunchao Wei
Venue: NeurIPS 2025 poster ()
Keywords: Video Understanding
Vision-Language Model
Test-time Reasoning
Anomaly Detection
Primary area: applications

Abstract: Most video-anomaly research stops at frame-wise detection, offering little insight into why an event is abnormal, typically outputting only frame-wise anomaly scores without spatial or semantic context. Recent video anomaly localization and video anomaly understanding methods improve explainability but remain data-dependent and task-specific. We propose a unified reasoning framework that bridges the gap between temporal detection, spatial localization, and textual explanation. Our approach is built upon a chained test-time reasoning process that sequentially connects these tasks, enabling holistic zero-shot anomaly analysis without any additional training. Specifically, our approach leverages intra-task reasoning to refine temporal detections and inter-task chaining for spatial and semantic understanding, yielding improved interpretability and generalization in a fully zero-shot manner. Without any additional data or gradients, our method achieves state-of-the-art zero-shot performance across multiple video anomaly detection, localization, and explanation benchmarks. The results demonstrate that careful prompt design with task-wise chaining can unlock the reasoning power of foundation models, enabling practical, interpretable video anomaly analysis in a fully zero-shot manner. Project Page: https://rathgrith.github.io/Unified_Frame_VAA/.

A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis
Dongheng Lin 1.2 Mengxue Qu'
Kunyang Han
Jianbo Jiao" Xiaojic Jin
Yunchao Wei
1 Institute of Information Science, Beijing Jiaotong University
2 'he MIx Group, University of Birmingham (d.lin.2 2, j. jiao) @bham..bammmmmm..mm fqumengxue, kunyanghan, xiaojie. jin, yunchao.. weil@bjtu. edu. cn
accham.c.... ac uk
Abstract
Most video-anomaly research stops at frame-wise detection, offering little insight into why event is abnormal, typically outputting only frame-wise anomaly scores without spatial or semantic context. Recent video anomaly localization and video anomaly understanding methods improve explainability but remain data-dependent and task-specific. We propose unified reasoning framework that bridges the gap between temporal detection, spatial localization, and textual explanation. Our approach is built upon a chained test-time reasoning process that sequentially connects these tasks, enabling holistic zero-shot anomaly analysis without any additional training. Specifically, our approach leverages intra-task reasoning refine temporal detections and inter-task chaining for spatial and semantic understanding, yielding improved interpretability and generalization in fully zero-shot manner. Without any additional data gradients, our method achieves sate-offtteear zero-shot performance across multiple video anomaly detection, localization, and explanation benchmarks. The results demonstrate that careful prompt design with task-wise chaining can unlock the reasoning power foundation models, enabling practical, interpretable video anomaly analysis in fully zero-shot manner. Project Page: https:: htttps//rratggrch github io/ Unified, Frame VAA/
Introduction
Video anomaly analysis key application of computer vision for public security. Most early works formulate the task as temporal Video Anomaly Detection (VAD): mark the segments whose behavior deviates from learned normal patterns. Traditional detectors have reached high performance benchmarks, yet they output only frame-wise scores and provide no insight into why the segment abnormal. These limitations interpretability motivate broader shift from temporal detection to more downstream anomaly analysis tasks with user-friendly and explainable outputs, including spatial Video Anomaly Localization (VAL) [Liu and Ma 2019 Weng al.) 2022 and textual Video Anomaly Understanding (VAU) tasks [Du et al. 20244] Tang et al. [ Zhang et al. 2024b) utilizing fine-tuned MLLMs. While these works provide either spatial or textual cues for better explainability video anomalies separately, the previous works were mostly focused on a certain type of downstream tasks, which not provide holistic analysis to video anomalies, resulting in "incompleteness" from existing video anomaly analysis methods.
further challenge is the heavy reliance on dataset-specific supervision. Traditional VAD and VAL models require temporal masks or spatial bounding boxes, yet anomaly definitions vary widely across datasets (Wu et al. 2020 Lu al. 2013 Mahadevan al. 2010), so a model tuned on domain
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Table Comparison of scopes and requirements recent VLM-based methods. / = supported
tasks, X = not supported. Our framework is the only strictly zero-shot approach that handles all three.
Method
Supervision
Fine-tuning
Temporal
Spatial
Textual
LAVAD Zamella el al.
2024
None
Nome
CUVA [Du 2024
Text
Prompt-tuning
STPrompi Wü et al. 2024b
Weak class (closed-set)
Prompt-tuning
/
x
Hawk [lang cl ul A124
Instr. tuning
Projection
X
x
Holmes VAU Zhang cl al 2024b
Instr. tuning
LoRA
/
VERA Ye ct
Weak class
Verbalized prompt leurning
/
Ours
Nome
None
/
often fails on another [Wu et al. 2024a]. Also, in real-world applications, due to privacy and security concerns, the training data could be unavailable for some sensitive scenes. As partial remedies, recent work has explored zero- and few-shot approaches using frozen vision-language backbones We observed that most of the VLM-based works have limited task scope and still rely on annotated datasets. The only strictly zero-shot method is solely focusing on temporal VAD which makes less user-frienan eser-friendly [Zanella et al.] 2024]. For prompt- based methods Yang et al., 2024 Ye et al. 2025 Wu et al.) 2024b), they inevitably require induction on an annotated training set, which comes the cost of generality as prompts are often learned to be foak/domain-specifee
or MLLMs as we summarized in Table [
This generality problem even exacerbates for instruct-tuned MLLMs [Tang]
ctt al. 2024 Zhang et al. 2024b) which are optimized to return answers from seen QA pairs focused on describing closed set of anomaly types (Ding and Wang 2024].
In recognition of these problems, given that multimodal LLMS already encode rich priors for commonsense reasoning (Zhao et al. 2023 Zhang et al. 2025b] Ren et al. 2025 J. fine- tuning may be unnecessary for certain tasks, as long as we can effectively reason about task contexts at test time [Minaee et al. [225 Ma et al.) [024]. Specifically for video anomaly analysis, we may consider each of the previous benchmark tasks as answering specific questions (When, where, what, and why?) about visual anomalies, among which each can be seen as sub-problen contributing to holistic analysis. Therefore, solving these tasks represents naturally stratified reasoning contexts contributing towards holistic anomaly analysis. Inspired by this, we propose a unified reasoning- driven chain framework that conditionally connects different MLLM-based task solvers during test time.
Specifically, our framework operates systematically across three clearly defined stages rather than merely concatenating separate tasks. First, an initial Video Anomaly Detection (VAD) computes a surrogate anomaly probability at the video level and extracts a contextual tag list corresponding the most suspicious segments, thereby providing individualized context cues for each sample. Following this, score-gated refinement utilizes both the contextual tag list and preliminary anomaly scores to perform conditional score adjustments, refining the VAD task based on the inferred contexts. Lastly, the final anomaly scores and contextual tag lists jointly guide the downstream spatial Video Anomaly Localization (VAL) and further textual Video Anomaly Understanding (VAU) tasks, where textual and visual prompts are dynamically refined based on the VAD scores. In summary, each stage of our framework employs frozen Vision-Language Models (VLMs), with dynamic prompts iteratively inferred from preceding stages.
We conduct extensive experiments on UCF-Crima XD- Violence, UBnormal and MSAD (Sultani ctt al.) 2018 Wu et al., 2020, Acsintoae et al. 2022 Zhu et al. 2024 The proposed framework achieves state-of-the--e performance on three separate tasks under a zero-shot setting, achieving an overall 4-6% AUC improvement on VAD, and consistent improvements over diverse metrics for VAL and VAU tasks. These results show that our training- training-free, unified video anomaly analysis framework is interpretable, extensible, and robust across various domains and tasks.
2 Related Works
Traditional video anomaly analysis. Early Video Anomaly Detection (VAD) works typically fall into three major supervision regimes: one-class models trained only on normal clips and used compact embeddings or memory banks to detect outliers [Sohrab et al., 2018 Wang and Cherian 2019 Micorek et al. 2024]: fully unsupervised methods rely reconstruction future-frame
2

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Inidial Suspicious inserval
LLM
VLM
Temporal Detection
Refinrd Ancenally Interval
When the anamaly? Suspicious behavioes are
m
Shoplifting? Stedling? Fighting?
Roason Gate
[0.1. 0.8.
0.1] soore
Spatial Localization
anomaly lag list
Where ancmaaly?
The anomaalees could be
Multimodal LLMs
Visual prompt refine
Reason
Galle
Anomaly Understanding
caption prompt& score prompt
What the ancetaly? rough screening suggeses
The video contoins mon committing shoplifting, becouse
video ciipe
instruction
Figure 1: Overview of the unified holistic anomaly analysis framework. Left: A preliminary step extracting the most suspicious intervals of video and extracts anomaly tag lists reflecting possible anomaly contexts. Right: Illustraction of how the priors are used to refine each of the tasks. Low-confidence samples in Temporal VAD refined by selective intra-Task Reasoning step. The Inter-Task Chaining further connects it to downstream, including spatial VAL and textual VAU into a cascaded chain for a unified holistic anomaly analysis.
prediction losses (Hasan al. 2016 Thakare et al. 2022 and weakly- MIL frameworks used video-level tags to rank anomalous snippets (uulanni et al. 2018 |eng et al. 2021 Joo et al.
2023) All of them need to be re-trained for unseen domains
anomaly types and provide
semantic rationale for their decisions |Ramachandra et al. 2020 Wu et al. 2024b). To address this, open-ser detectors emerged: OVVAD fuses LLM semanties the system can both detect and classify novel anomalies [Wu et al., 2024a However, such open-set detectors still require task-specific training and provide very limited textual insight into why frames may be abnormal, motivating the move toward vision-language solutions with task formulations beyond temporal detection.
-LM-based video anomaly analysis. LAVAD Zanella et al. 2024 introduces fully training-free pipeline for temporal detection: frozen VLM captions each Irame; prompted LLM converts the caption stream into frame-wise anomaly scores that are further refined by ensembles of foundation models. While effective when anomalies are clearly distinguishable from normality, it occasionally fails to distinguish more complex anomaly types [Ding and Wang 2024), and lacks direct semantic explanations, providing only default VLM captions alongside computed anomaly scores. Prompt- tuning variants (Du et al. 2024 Wu et al. 2024b Yang et al. 2024 Ye et al. 2025) optimize textual prompts guide frozen MILMS for certain tasks. While they reveal strong performance, they remain dependent on annotated data and deal with limited task scopes /Zhang et al. 2024b
Video anomaly understanding and multimodal LLMs. With the need for deeper semantic reasoning, instruction-tunin methods such as Hawk (Tang al.) [0224] and Vlolmes-VAU (Zhang al. 2024b] fine-tune VL.Ms on detailed, anomaly-captione explanations. These works have achieved more accurate descriptions but require extensive annotation and computational resources, and remain tied to seen anomaly types [Liu al. 2025).
video clips to produce narrative
To sum up, we observe: strictly zero-shot methods such as Zanella et al. [2024) support temporal detection but lack spatial grounding and textual insights. Prompt-tuning variants [Du al. 2024 Wu
et al.] 2024b Ye et al. 2025) are mostly focused on only subset of tasks/domains as the prompts are often task/domain specific. Instruction-tur -uuned models [Tang al., 2024 Zhang et al.) 20246) produce rich narrative explanations. yet lack either temporal spatial coverage and incur high annotation costs. These gaps motivate our effort to unify these tasks under a zero-shot setting.
3 Methodology
We show an overview of this framework in Figure T The video anomaly analysis task is decomposed into three major sub-tasks, formulated in previous works, and our framework exploits the inherent

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connection among them. Our unified framework can be summarized in two major components: 1) An Intra-Tape Task Reasoning (IntraTR) extracts anomaly priors through the temporal video anomaly detection (VAD) task and then refines the temporal detection through gated additional reasoning step. 2) Building on the reasoning process IntraTR, an additional Inter-Task Chaining (InterTC) connects the extracted tag list and temporal score results from the initial VAD results to enable subsequent localization and understanding tasks in a cascaded manner. Detailed explanations for each component are provided in Section 3.1 and Section 3.2 respectivel...
3.1 Intra-Task Reasoning (IntraTR) for temporal anomaly detection
Problem formulation. VAD can be formulated as binary (0-1) classification at frame level. Ideally, for each input frame fi, the objective to predict an anomaly probability 8,. For baseline methods utilizing LLM and VLM [anella et al. 2024], it can be formulated as:
OLLM(PVAD @ OVLM (ct, Pcaption)).
Sy [s1....,TT]
(I)
where T is the number of frames in video V, is short video clip representing events around frame Si and PVAD, Peaption represents prompts used respectively for video anomaly detection and clip captioning. Vector Sy therefore provides first-pass anomaly estimate for every frame, obtained without fine-tuning. However, beyond this baseline, can we further leverage Sy for improved reasoning?
Trying to answer this question, our VAD pipeline treats Sy not only as the final answer but also as a starting point for structured intra-task reasoning step performed at test time. Figure provided an overview the proposed IntraTR pipeline.
Score-guided anomaly extraction. To iden- tify the potential anomalies present in the video, we first conduct one forward pass producing frame-wise anomaly scores Sy , 8T for a video V with T frames, where each anomalous event
Ioyus video
Reject
In Tioi Scorer
Reaponing
8, E [0, 1]. Intuitively. e should occupy a contiguous window We = (.....,++E l 1). < |Svl reflects local seg- ments. Denote the mean score inside any win- dow W by v(W)= [W] Ejew Sj. Following the intuition that anomaly events should main- tain consistently high scores, in an anomalous video, we expect to find:
Anunndo lid
KLU
Shoplitnng? Fyhhing!
Extroctor
Refine Pronp!
Figure 2: Intra-Task Reasoning pipeline: (1) the Initial Scorer produces a score curve; (2) peak detection truncates a suspicious window and the Tag Extractor generates anomaly tags ly; (3)
JW: : [Wel= = e, such that (uW))) 2 T, (2)
reasoning gate refines low-confidence predictions via the Score Updater.
where T is a natural decision boundary (e.g. 0.5).
To find whether such a window W. exists the video, at inference time, we slide window of admissible length and select:
W max arg
WC(1 |W|= ((W),
(3)
šy Mu W ).
(4)
where Wmax is the most suspicious segment and šv [0, 1] is the surrogate video-level anomaly probability. After identifying the most suspicious part of the video Vaus indicated by W Wmax. we extract text contexts related to anomalies by querying VLM to generate a list of concise phrases ly summarizing the possibly related anomaly activities in the video clip Vaus as follows:
Veus [fg].
j W max:
(5)
ly = 0yLM (Vaus, Pextract).
(6)
We then pass W Wmax By and ty to later stages for further processing.

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Score-based reasoning gate. Recent studies reveal non non- monotonic trade-off between reasoning depth and accuracy in large language models: while short chain of thought can boost performance. excessive steps often induce "over-thinking" and hallucinations [Huang and Chang 2023 Chen et al.) 2025]. Inspired by this observation, we trigger an additional reasoning pass only when the first-pass score is ambiguous via bosre gate component with motivation explained below.
Starting from the raw frame scores Sv, we obtain the surrogate video-level probability sv. If sy & 00.5-m, 0,5+ml, the model is considered confident about its first round predictions as the prediction is positioned far from the decision boundary Yaniv and Wiener 2010]. Therefore, gating mechanism with width 2m allows borderline/ambiguout videos with šy E [0.5 m| to proceed to a second reasoning stage. With the tag list ty extracted from frames in W Wmxx the task prompt is refined to PvAD = tv @ PVAD-
Intuitively, m quantifies the degree of "suspicion", ", which can be either fixed value or adaptive w.r.t. each sample. For the setting of m specifically, we offer two options. It could be either 1) a fixed heuristic constant over all samples that allowing user control the degree of suspicion, or as an adaptive sample-specific variable estimated from current V by my = Var(Sv) reflecting the diversion of normal/abnormal frame scores may exist in current video. We compare and discuss the impact of m in Section 4.2 and Appendix B. correspondingly.
Based the above, querying the scorer LLM once more with refined prompts when šy m0.5=m]:
Sy = BLLM(PVAD + 8VLM (CPppiiiio)))
= I....,,
The refinement yields updated frame scores Si, replacing the initial Sy for the final decision. Following established practices in prior works Ye et al. 2025 Tran et al. 2022]. we run a standard gaussian smoothing to post-process the refined Sv, resulting the final A By allocating the costly reasoning step only when the score near the margin indicates uncertainty, the method inherits the computational efficiency and robustness of selective prediction while mitigating "over-thinking" hallucinations observed in unrestricted chain-of- -f-thughht generation.
Beyond the IntrasR-assisted VAD above, we further explore leveraging the reasoning steps from the VAD task to assist downstream tasks through InterTC component in Section 3.2 and Section 3.2
3.2 Inter- Chaining (InterTC) for holistic anomaly analysis
In this section, we cover the design of InterTC for two key sub-tasks in anomaly analysis, namely 1) spatial Video Anomaly Localization (VAL) and 2) textual Video Anomaly Understanding (VAU).
Algorithm 1: Inter-Tast Chaining prompt re-
InterTC from temporal detection to spa- tial localization. Video Anomaly Localization (VAL) aims to predict spatial bounding boxes for regions in the frame containing the anomalous activities. The InterTC connects VAD with VAU using straightforward method. Specifically, we utilized a frozen VLM (Loc(PLOC @ f). guided by base localization task prompt Ploc for frame f. And then inject tv to the PLOC. producing refined prompt Pioo using a pre- defined template. Therefore, PLoc is expected to a more sample-specifir and clearer guid- ing prompt for spatial localization and thereby improving its performance. Detailed prompt templates are included in Appendix C.T
finement for VAU
Input: video V = [fi, frli tag list tv;
base prompt pvAu:
localization prompt PLoCi surrogate anomaly score šv; most suspicious window W Wmax
Output: final description d'
VAD-prior Prompt Refinement:
PVAU + tv PVAU;
Score-gated Localization Overlay (optional):
if sy > 0.5 then
Feel + sample frames( (V, W Wmax);
bboxes + f1o00 (Feel, tv + pLoc); Vquery draw boxes V, bboxes);
else Vquezy +V;
Final description:
Cascaded InterTC for video anomaly under- standing. Given an untrimmed surveillance video V= =='in JT). video-level anomaly understanding (VAU) aims to 1) decide whether V containing abnormal event and 2) output human-readable description d' that explains anoma-
d' + BVLM (Vquery, peAu); return d'
5

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lies from the visual inputs. Formally,
OvAU : V - (5v, d²), Dv E {0.1).
(8)
Unlike earlier works that train task-specific models via instruction tuning [Tang et al. 2024 Zhang ct al., 2024b), our approach vAu operates in a fully zero-shot manner. It reuses the frame-level scores Sv, the tag list tv, and the suspicious window W Wmax obtained during the earlier temporal detection and spatial localization steps refine the anomaly understanding prompt at inference time.
Algorithm D provides an overview of the full prompt refinement step for downstream VAU task leveraging the reasoning steps from the preceding VAD and VAL tasks. Specifically, we begin with VAD-prior Prompt Refinement which incorporates the tag list ty from the VAD task into the anomaly description prompts, forming more context-aware textual query PvAU ly 4 pVAU-
Next, we apply a visual prompt enhancement called Score- gated Localization Overlay. Specifically, the surrogate probability šy gates visual-prompp enhancement stage: only when sy > 0.5. i.e. the VAD detector already believes an anomaly is present, allowing us to trust that object-level cues are meaningful and beneficial to include. For such videos we 1) sample frames inside Wmax. 2) invoke detection-capable VLM with ty + PLOC to obtain bounding boxes, and 3) overlay those boxes onto the corresponding frames in original video V, producing annotated Vquery- If šy I 0.5 we skip the bounding box overlay and retain the original, unmodified video.
Finally, the VLM receives Vquery (either annotated or not) together with PVAU and outputs the description d², Since localization performed only when the detector is confident that an anomaly exists, the inserted boxes act as reliable visual prompts rather than noisy clutters.
4 Experiments
4.1 Experimental setup
Datasets & evaluation metrics.
We evaluate the official test splits of three benchmarks: 1)
UCF-Crime (Sultani et al., 2018) (real-world CCTV and crowd-sourced, 13 anomaly types); 2) XD-Violence [Wu et al. [020] (800 test videos from movies, sports clips, CCTV, dashcam, cartoons); 3) UBnormal [Acsintoae et al. 2022 (211 1 fully synthetic surveillance videos across 29 virtual environments); 4) a more recent MSAD (Zhu et al. 20244 (14 distinct scenarios captured from various camera views, containing 360 test videos) which is less likely to overlap with pre-train data.
According to previous works, we primarily evaluate Area Under the Curve (AUC) score for the Receiver Operating Characteristic (ROC) Curve on all the datasets. Since several studies also report Average Precision (AP) on XD- ID-Violence [Wu et al.) 2020), we include AP results for reference.
Finally, for Video Anomaly Understanding (VAU) task, fairly evaluate the quality of the generated d', we adopted all the video-level annotations from HIVAU-70k [Zhang et al.) [022b)] Spanning 1051 video descriptions, with 251 test videos from JCr-Crime, and 800 videos from XID-Violence, which is larger than the original video-level test set in Zhhag et al. (2024b
(398 samples). In addition
to traditional NLP metrics (Papineni et al., 2002, Vedantam et al. 2015 Banerjee and Lavie 2005
Lin 2004), we also evaluate GPT-guided scores following recent works [Tang et al. 2024 Li et al.
2024a]. More details are available in Appendix C
Hyperparameters & experiment details. For VAD tasks, clip-level scoring operates on the full video with 16-frame stride (see details in Appendix C.2 The suspicious window size for the prior extraction step is set to & = max(300, T //10) and fixed m 0.05. We evenly subsample at most 180 frames from the window W Wixx due to the limited context capacity of the VLM model to get ty. As for the default VLM and LLM tested in the framework, we choose VideoLLaMA3-77 (Zhang et al. 2025a and Llama-3. 3. --B--Instruct [Grattafiori et al. [024]. To reduce computational cost, subsample all videos at frame sampling stride of 16. We run all experiments two NVIDIA GeForce RTX 3090 GPUs Further implementation details, prompts and hyperparameter stability tests are provided in Appendix Cadd Appendix
B.1
Additionally, we adopted Qwen2. 10nn2.5-VL-- 5-VL-7B (Bai et al. 2025 as the default localization VLM for VAL.
task, and varied different baseline VLMs including Zhang et al. 2025a Li ct al. (022bb]
Bai et al.

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Table 2: VAD Performance comparison
UCF-Crime, XD-Violence, UBNormal and
MSAD. indicate whether a method is zero-shot and training-free in terms of model parameters.
Method
Zero-shot
Training-free
UCF-Crime
XD-Violence
UBNormal
MSAD
AUC(%)
AUC(%)
AP(%)
AUC(%)
AUC(%)
AP(%)
2018
X
71.92
73.20
GODS
Waie 300 Cheran 2019
X
X
70.46
61,56
RIFM
X
K
83.31
77.81
60.94
86.7
66.3
VXD
X
CLIP-TSA
D00 R1 A125
X
X
87. 58
82.19
MGFN
Chen
X
K
86.98
80.11
85.0
63.5
STPrmmpr Wiler
231244
OVVAD
ef 20A4
X
K
36.40
66.53
62.94
Holmes-
20124a
K
96
87.68
MULDE
Mi A024]
X
78. 50
72.80
EGO
Ding ar RU24
X
81.71
65.77
87.3
64.4
AnomalyKuler
2024
71.90
VERA ef
86.55
88.26
70.54
HolmesVAL Zhhnn et
2024h
(2S)
X
58.54
AnomalyRule Yang ot 2024
(2S)
65.401
UR-DMU 2hou er 20233
(2.5)
74.3
53.4
CLIP Radlod c 2022
LLAVV-13 Liu al. 2014
(ZS)
72.84
79.62
50.26
Videol Ladeol.LaMM..... A-78-- Himin 1-88 (7S)
78.7
68.5
GL.M.4.IV.9B- T..i.iiii
CcT))
LAVAD [Zanella al. 2004
80128
85,
62.01
$1.06
Ours (fixed constant
84.28
91.34
68.07
68.98
85.9
76.4
Ours (adaptive myy)
$4.08
91.23
25.9
The resalt from direct evaluation of the method trained other non-overlapping datasets, reflecting zero-shot performance.
Zero-shot thain-of-thhughh (CoT) inference VAD performance using GLM--.11V99 Thii-kiinn Teaan al.
2025
Table 3: (a) Ablation of inference steps. showing the effectiveness of each reasoning component. (b) Ablation video-level anomaly priors. foracle ground-trute types, and ty actual local anomaly priors we extracted during the reasoning step.
(a) Inference component effectiveness ablations
(b) Reasoning prior ablations
0 LLM-Scoring @ Prior Reasoning
@ Score-gated Reasoning
(%)
Anomaly Priors
AUC (%)
77.67 (+0.00)
77.40 (-0.27)
PVAD
81.86
80.38 (+2.71)
1 foracle
83.91
84,28 (+6.61)
4 fy (Ours)
84.28
2025) for VAU task. Moreover, for both VAL and VAU tasks, we leverage the anomaly priors (c.g. tv, sv, Wmax) obtained under the default configuration of IntraTR in Section 3.1
For the Video Anomaly Localization (VAL) task, following previous works, we evaluate temporal loU (TloU) [Liu and Ma 2019 Wu et al. 2024b) for each anomalous frame f, (j 1,.... N), the localization head (J,ocJj.Picc
outputs confidence C₁ and box Bj. Then, the TloU computed as: & EE Area( (G,))) U[C' 2 r]. with G, the ground-truth bboxes, where the indicator I E (0, 1} judges whether the confidence Cy above the default threshold - 0.5.
A,
4.2 VAD results
Table summarizes the results across the three benchmarks. Across datasets, our zero-shot, training-fiie framework outperforms the previous best zero-shot detectors by 4 6% on UCF-Crime and XD-Vin Violence, 3% on UBnormal, and also generalises well on MSAD. Our method also showed competitive performance to those baselines requiring additional supervision, data CoT reasoning steps, further proving the benefit IntraTR component we proposed. Figure B qualitatively compares method with the baseline [Zanella et al. 2024 showing significantly reduced false positive predictions. More examples are provided in Appendix[D.. D.I
We also find that fixed margin m already performs well, although it introduces an unavoidable assumption on the test domain, while variance estimated my also provides similar performances without posing any assumption on the test domain. This sample-specific "suspicion" accounts for its superiority on a synthetic dataset (UBNormal) where samples are peculiar to natural videos. We further discuss the impact of values in Appendix B.]

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Table 4: Comparison with previous supervised works on temporal-loU (%) metric using zero- shot Qwen2 5-VV--B. ty comes from IntraTR, loaale from ground-truth class names.
1.0
Anvrnay
0.6
LAWAD IEVPR 2024]
Ours
0.4
Anomaly
0.2
Method
TloU
1000
2000 A.
VadCLIP [W et al 2024c
22.05
STPrompt Wa et at.
2024b
23.90
Qwen2.5-VL-7B (baseline)
24.09
tv
25.17
Figure 3: Anomaly scores video from UCF-Crime with an "Arrest" incident.
foracle
25.21
Table 5: Video anomaly understanding performance comparison on two benchmark datasets. The results are computed against ground-truth descriptions provided by (Zhang al. 2024b). Apart from the traditional NLP metrics (BLEU, CIDEr, ROUGE, METEOR), we also provide GPT-R, GPT-D, GPT-C metrics Reasonability, Detail and Consistency computed against against the ground-trutt using API calls to OpenAI-GPT4.1 [OpenAl 2025 correspondingly following previous works Tang al. 2024 Li et al. 2024a].
U13:-Crim Nuttoan at 201s
XI-Vidkee
Wrnetal.
2028
TrimWVdort 5-00
(wang sl 5335
0.359
D001
0188
0.003
0.380
0.355
0,305
11300
11003
0.119
01.30
0.455
11:47
VMlaCChe-Rlab-BB Lictal 2034
0.622
V Zbarg a poss
0,233 0.34
Do04
0.117
0.15
0461
0.289
11:86
1.300
1L055
0.381
0156
0.508
11487
IatrrUMA YAU
youry
0.183
0.411
0.444
0.386
0250
0.707
Tay
01877
0
0.176
0.556
0.365
Re evalumede on our new evaluation set strictly fallowing its default configurations.
Ablation test-time reasoning steps. Table 3a evaluates the individual contributions of the three components of our inference loop. The simplest baseline, single-round direct query to a frozen VLM achieves 77.67% (row 1). Introducing the ① LLM-based Scoring component and the @ Prior-Reasoning step without the subsequent score-gated reasoning yields only 77. 77.40% (row 2). In contrast, keeping the LLM scorer but dropping the prior reasoning module lifts performance to 80.38% (row 3), indicating that unrestricted "overthinking" across all samples without selective gating can conversely inject noise, causing hallucination, degrading performance. Activating all three stages, including the @ score-gated reasoning, further raises the result to 84.28% (row 4), a gain of 6.61 % over the raw VLM baseline. These results validate our hypothesis that confidence anomaly presence can act metric to evaluate the quality of first-round prediction and therefore effectively control a proper reasoning depth for test samples.
Ablation on ty. Table 3b isolates the effects of incorporating the textual video-level anomaly priors in the second-round reasoning for VAD. The baseline soooe-gated reasoning module under fixed small margin value 0.05 with an empty ty achieves a lower performance 81.86%. Replacing the tv with ground-truth oracle class names from annotations (e.g. "Arson, RoadAccident") (fomclee lifts performance to 83.91% %. confirming that accurate anomaly priors improve detection performance. Interestingly, our automatically extracted priors ly even surpassed the oracle class names, reaching 84.28% demonstrating that the local anomaly extraction step could effectively finalize the anomaly priors to clearer contexts than rough anomaly classes (e.g. class label "Arrest" is ambiguous, while extracted ly may include "physical altercation" which more informative) ground-truth, Exploiting clearer contexts leads to superior frame-wise anomaly detection performance.
4.3
VAL results
Table 4 shows that the zero-shot MLLM baseline already outperforms earlier supervised detectors, and that injecting anomaly tags, either from the automatically derived tag list ly in IntraTR or the ground-truth class name, yields an additional ~ 1% absolute gain in quantitative TloU metric. Also, the tiny gap between the results using /v and the ground-truth lomelle suggests our lv captures near-optimal semantic cues the oracle provides, yet without requiring any manual annotation. These
8

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Ground-truth
the mebile naticing He then discreatly the phone in pocket nothing sut the ardinery had eccurnett The basis for this enumaly is the unexpected and unouthorized rensva of item from store display, which desictes from the normal and expested behavice of custemer a retoil envrrommnt, where oustomere are typically expected to browse, osk for assistonce, and make purchoses through legitinaate meons.
"The momtly exists, specifically identified \'Shoplifthgg.. The onomaly invoives the bearded approsching the gloss cobinet,
photes display, and then corefully taking making
the phones out of the cabinet without
for it internct with the sales stuff, and molks
'The video oppears normal. The shows mon blue counter a mobile phone store. stonding looking the phones on display and occosionally picking one up to exomine it. There are other people in the background, but they are not interocting with the man. The man does not appear to be doing anything unusual anomalous."
"The anomaly shoplifting. A black picking phone
'The video depicts series of mundone events where a blue shirt glasses takes phone. opproaches counter, with unusual or suspicimus activities occurning throughout the video."
jocket ond from the display cade and putting pocket. He then walks away with the phone."
VideoLlama3-73 Baseline
Ours
Holmes
Figure 4: Qualitative results of video anomaly understanding. Descriptions for a video containing an incident of "SShplifting from different methods, where green text highlights correct descrip- tions/rationale about the anomaly, and red highlights statements inconsistent with the ground truth.
Table 6: Ablation study of InterTC prompt refinement steps on description quality.
Dataset
Method
Tag bv
bbores
BLEU
CIDEr
METEOR
ROUGE
ZS CoT Baseline"
0,3172
0.0193
0.1651
0.1820
InterTC (Ablated)
0.2147
0.0143
0.1167
0.1564
UCF-Crime
InterTC (Ablated) InterTC (Full)
0.3328
0.0183
0.1684
0.1920
0.3453
0.0232
0.1750
0.1878
ZS CoT Baseline"
0,3682
0.0381
0.1824
0.1876
InterTC (Ablated) InterTC (Ablated)
0.2897
0.0219
0.1410
0.1690
XD- Vi-Violence
0.3857
0.0270
0.1931
0.1993
InterTC (Full)
0.3993
0.0288
0.1980
0.1997
ZS CofI: The zero-shot VAU perfommance of reasoning VLM: GLM-4.1V.9B-Thinnnnng which is capablc of long chain-of-thought (CoT) inference.
Team al. 2025
observations confirm that even lightweight semantic priors effectively improve spatial localization without retraining. Additional qualitative examples of localization are included in Appendix D
4.4 VAU results
Experiment results. Table 5 compares our InterTC refinement to direct VLM inference baselines
and recent instructed VAU MLLMs (Tang et al., 2024 Zhang et al., 2024b) on two different test domains, against the ground-truth description provided by HIVAU-70K Zhang et al. 2024b). On both domains, Inter'CCreefied query prompts improve the base VI.M on both traditional NL.P metrics and all GPT-scores (Reasonability. Detail, Consistency) of the outputs, narrowing much of the gap to instruction-tuned methods [Tang et al. 2024] IZhang et al.) 2024b) and even surpassing instruct-tuned methods on several metrics. Qualitatively, we also demonstrate the descriptive capability of our framework in Figure 4 On shoplifting clip, the baseline VLM (Zhang et al. 2025aa] and Holmes VAU both fail to identify the abnormal act, whereas our method reports the key action ("puts the phone in his pocker") and labels the event shoplifting. More examples are provided in Appendix D.3 These findings confirm that 1) the tag- based prompt enrichment injects crucial context and 2) localization cues further enhance narrative detail without any additional training.
Ablation to prompt refinement steps. To isolate the improvement of VAU metrics to each compo- nent of -CerTs-ssseeee VAU process, we conduct corresponding ablations. As shown in Table 6 across both OCF-Crime and XD- Vi-Vieeen,,, simply enhancing the prompt with the tag list ty from VAD-priors to the base prompt accounts for the majority of the observed gains. In contrast, Inter-task chaining from the spatial localization overlay to VAU step yields smaller, incremental lift top of that strong improvement. We suspect primarily because the frozen VLMs have not been fine-tuned on large-scale data featuring overlaid bounding boxes, resulting in rather marginal improvements.

---

While the generic "thinking" VLM [Tam et al. 2025 underperforms on the more specialized VAD task (see Table 2). it performs better on VAU than zero-shot baselines. This indicates that chained inference idea adopted in Team et al. 2025 and our InterTTC can enrich textual anomaly under- standing by encouraging more detailed, stepwise deseriptions. However, general-purpont reasoning Team et [2025] may not generalise well on the niche and complex anomaly video understanding task (Shojace* et al. 2025), introducing content weakly related to the true anomalies. In contrast, our InterTC-guidee
evidence, yielding superior scores on most metrics across all video-anomaly tasks. Overall, VAD prior textual prompt refinement plays more major role in prompt refinement, while localization visual prompts could be optional enhancement when extra compute is available.
prompts focus the description
5 Conclusion
In this work, we introduced a unified, training-free framework for holistic video anomaly analysis by chaining temporal detection, spatial localization, and textual understanding in single inference pass. Our zero-shot system consistently outperforms prior training-free baselines and approaches supervised methods across all three sub-tasks.
By structuring our pipeline as a sequence of gated reasoning steps, each sub-task enriches the next with semantic or visual priors drawn from the model's own outputs, enabling self-correction and deeper interpretability without any additional training. In video anomaly analysis specifically. where events unfold over time and space, such multi- inference captures structure that single-past models miss or fail, yielding both accurate detection and user-friendly explanations without any additional training. Despite some possible limitations and potential societal impacts it may bring about a powerful yet bulky VI.M system for sensitive video analysis (see more discussion in Appendix and Appendix G we believe this framework of treating inference as an active, context-driven process can foster more robust video analytics and may generalize to other complex dision-languaa tasks.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. NN49937,,,,) Beijing Natural Science Foundation (N4.2242022), the Fundamental Research Funds for the Central Universities (2024XKRC082). Jianbo Jiao is supported by an Amazon Research Award.
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Technical Appendices and Supplementary Material
A Appendix Roadmap
In this appendix, we cover the following materials:
Additional ablation study (Appendix B
Additional implementation details (Appendix
Additional qualitative results (Appendix D
Running-time Analysis (Appendix
Limitations (Appendix F)
Broader impacts (Appendix
B Additional ablation study
B.1 Hyperparameter sensitivity tests
Senstitivity on m We study performances under different beeiiion-boundary-maane width values m E 0.5) and dynamic my Var(Sv) presented in Table Performance remains stable for m < 0.2 and my and drops significantly on UCF-Crime and XD- Violeeeee when m = 0.4, presumably because an overly wide margin labels many true positives as "uncertain", resulting in unnecessary hallucinations. In contrast, UBnormal [Acsintoae et al.) [022] benefits from larger m,; the synthetic clips are originally ambiguous for pretrained models such that additional skepticism beneficial Yang et al. 2024). As m [0.05, 0.2] yields near-optimal AUC all real-world datasets, we adopt the smallest value m 0.05 as the default setting for constant m..
To further investigate how the IntraTR step affect model behaviours, we further visualize the density of samples with respect to the 11 distance of their video-level scores to the decision boundary |Sy- -r] that measures the confidence of predictions in Figure Specifically, we observe that both smaller constant m and the dynamic my can effectively produce more high- high-confidence predictions, while larger m conversely results in more confusion and therefore less confident predictions overall.
Sensitivity on window length & We heuristically set our minimal suspicious window max(300, 1/10), in which 300 frames is a floor for the shortest window Wmax- Since a clip ca (the smallest scoring unit) also spans 300 frames & 10s, lowering this floor has little effect.
As result shown in Table 8 an overly large & (as result of smaller divisor video length 7) degrades the performance. We suspect that a large window size hides flecting anomalies as the
Margin values ryy Var(Sp)
0.6
0.4
m=0.05
mm0.1
0.2
A Density
m=0.4
-0.4
-0.6
-0.8
0.1
0.2
0.4
D.5
Is,
Figure 5: A of Score density with regards to distance to decision boundary. For all samples in UCF-Crime and Violence, it shown that high m value resulted more ambiguious predictions with 1Sy- - -| ++ while a small or local variance based m effectively pushes the predictions away from decision boundary as we expected.
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Table 7: Impact of several margin values (m E (0, (..).)) on VAD performance. All settings outperform the baseline, with stable results across different m values.
Margin values
UCF-Crime (AUC) XD-Violence (AP)
XD-Violence (AUC)
UBNormal (AUC)
0.05
84,28
68.07
91.34
68.97
0.10
83.10
68.16
91.40
69.52
0.20
83.57
68.36
91.60
70.33
TH 0.40
79.21
67.45
90.81
70.59
my Var(Sv)
84.08
68.03
91.23
69.02
Table 8: Impact of several window lengths (4) on VAD performance
Window length f max (000, T/5) E max(300, T/10) = max max(300, T/15)
UCF AUC (%)
81.07
84.28
83.66
window may have higher probability of containing benign frames with lower scores, resulting in lower estimate of the surrogate video-level anomaly probability šv. In addition to such heuristics we used, it is also possible to introduce an additional time series segmentation model [Lovrić et al. 2014] to identify abnormal event intervals from sequences of frame scores.
Impact of post-processing In addition to we also evaluate the impact of the Gaussian smoothing parameter used score post-processing. It's typical to conduct postprocessing (Gaussian, EMA) to the anomaly scores for VAD tasks [Zanella et al.) 2024 Ye et al. 2025]. We followed this typical practice and implemented a simple Gaussian filter on the final score. The following Figure 6
demonstrate the robustness of our method different values use for gaussian smoothing post-processing.
B.2 Impact of different VLM/LLM components
Ablation on Monolithic Multimodal LLMs In our work, by default, we followed modular architecture of VLM + LLM from previous baseline (Zanella et al. 2024]. There are also other experiments and claims supporting this design.
In Table 3a we have provided ablation to end-to-end VI.M performance when used for scoring on every 16 frames clips. As a result, our discrete VLM, LLM framework provide better performance
(84.28% against 77.67%), Which aligns with the trend reported in the baseline [Zanella et al. 2022) We also tested even simpler baseline of using VaM337L to conduct direct end-to-end QA
with complete video inputs and asking for timestamps of anomalous intervals. The Table that such simple design gives much poorer performances even poorer overall performance.
s shows
Besides these experimental support for the modular design. Another earlier work [Chen et al., 2023a also suggests such capability of LLMs to coordinate separate VL.M models for better reasoning. Especially for cases where the task domain is a niche one under-represennte in the massive pretraining data. These rationales justify modular VL.//LLM design over single model.
To validate the generality of our method across different MILMM components. Table 10 varies the checkpoints plugged into our pipeline. With the L.M fixed (0I..M Llama-3 3. 3.1........1 1-8B -ng--unst downgrading the vision backbone from VideoLLaMA3-7B to a 2B variant or to Qwen2.5-VL results only a marginal drop < 1% AUC, indicating that the reasoning loop compensates for weaker video features. Conversely, keeping the same VLM and swapping the LLM shows larger but still moderate drops: 3B instruct model loses ~% AUC, whereas older Llama-2-13B loses ~ 4%. Overall, every combination remains above 80% AUC, confirming the plug-an and-play nature of our framework: it can enhance a wide range of pre-trained VLM/LLM pairs with minimal performance degradation, and it benefits most from stronger language reasoning while being relatively insensitive to vision backbone capabilities, by reducing holistic understanding into chained process of solving simpler, modular tasks.
Modular Ablation on Different Multimodal LLMs
Furthermore, to show that our performance gain not solely from the stronger capability newer VLM and LLM, we modernised baseline method [Zanella et al., 2024 under newer VideoLLama3-
23

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0.95
91.34%
91.34%
91.13%
90.89%
Performance (AUC/AP)
0.85 83.50%
84.29%
84.48%
84.49%
UCF-Crime (AUC)
XD-Violence (AUC)
XD-Violence (AP)
UBNormal (AUC)
0.75
70.46%
69.58%
68.45%
69.04 99.04%
67.65%
68.07%
66.90%
0.65
65.85%
5
10
15
20
a
Figure 6: VAD performance stability w.r.t. Gaussian smoothing a. Performance remains stable across different values. We simply choose default value = 10 and SciPy's 's default truncate 4.00 O (which yields an effective radius of 4o) for all the VAD experiments.
Table 9: VideoLLaMA3-7B End-to-end VAD QA Results
UCF AUC (%)
XD AUC (%)
XD AP AP (%)
UBN AUC (%)
Direct QA Ours (Full)
58.68 84.28
62.52
33.76 68.03
53.73
91.23
69.02
7B [Zhang et al.) 2025al and Llama3. 1-8B GGrattaiiori et al. 2024 backbones. In Table II observed drop single VLM performance when using newer model under Zanella et al. 2024 UCF-Crime. This may be due to the limited capability of sentence encoding VLM Girrdaar et al. 2023). which may fail to recognise more nuanced frame caption from newer models. This problem is mitigated on XD- Violence, where more dramatic videos than mundane surveillance footage of UCF-Crime makes raw captions encoded more recognisable in the representation space.
we
C Additional implementation details
C.1 Detailed prompts
We provide all the used prompts in this part.
Prompts used in VAD Firstly, we used the same Peaption across all datasets. Specifically:
Prompts for Peaption
"role": "system", "content":
"You are an Al assistant analyzing this video segment.
"Summarize the main events actions in concise way.
As for PVAD. we mainly adopted base prompts from Zanella al. [024]. Following their design, we also applied dataset priors to the prompts, the definition of anomaly varied for each of them. Specifically, we have base definition of anomaly events denoted dataset_prion "suspicious activities", For UCF-Crima
change it to "suspicious potentially

---

Table 10: Ablation of pretrained VLM/LLM models used on UCF-Crime. We varied different checkpoints for the components in our framework.
8VLM
0LLM
AUC (%)
VideoLLaMA3-77
84.28%
VideoLLaMA3-25
Llama-33 18.1-88-Instten
83,35%
Qwen2. 5-V2.-
83,23%
Llama -3. 1e-1--8IInss Instructt
84.28%
VideoLLaMA3-7B
Llama 2-13B-Chat
80.70%
Llama-3. 2-3B-Instruee
81.09%
Table 11: Performance "modernised" baselines Zanella al. 2024
with newer backbone models
Zhang et al. 2025a and Grattafiori et al. [2024]
Method
UCF AUC (%)
XD AUC (%)
XD AP (%)
Zanclla
2024
(BLIP2 FLAN-T5-XI. 1lama2-18B-char
74.19 72.99
85.16 84.64
61.09
Zanella et
2124
(VideoLLama3-75 Llama3.
61.20
Ours (Videol Lamaa-mmma- 7B + Llama3.1 11-B)
84.28
91.34
68.07
criminal', and for XD-Violence, we opt to "suspicious or violent" subject to the clear
definition of anomalies within each of them (Sultami et al. 2018 Wu al. 2020). However, on UBNormal [Acsintoae et al. 2022), where the anomalies span a wide range of spontaneous activities that may not be considered malicious by commonsense, we simply keep the base dataset_prior.
Prompts for pVAD
"role": "system", "content":
f'How would you rate the scene described scale from 0 to 1, with 0 representing standard scene and denoting a scene with ctataset_prion activities?"
"Please provide the response in the form of Python list and respond with only number in the provided list below [0, 0.1, 1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] without any textual explanation. It should begin with 'I' and end with 'J.
"role": "user", "content":
(1O0IMM(Ci,Peaptio))
We also conducted an ablation study for the incorporation of dataset priors in Ta-
Table 12: Ablation of dataset-level anomaly priors.
ble 12 which has shown a similar trend
Dataset Priors
UCF-Crime (AUC)
XD- VD-leioence (AUC)
to previous works (Ye al. 2025 Zanella
82.94%
90.72%
ct al. 2024]. Specifically, the overall VAD performance benefited from injecting even
84.28%
91.34%
a small context prior. Providing even brief
contextual definitions of anomaly events improves baseline model performance, providing stronger motivation for the automated extraction and utilization of the sample-specific anomaly prior we have proposed in work.
As we described in Section 3.2 after we identified Wmaxx we got segment of video Vsus, we queried the PYLM with the Vaus and Pextract to get the tag list ty.
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Prompts for Pextract
"role": "system",
"content":
"You are an Al assistant analyzing suspicious segment of a video.
"role": "user",
"content":
["sVsss] )
"Analyze the video interval to identify any possible suspicious behaviors."
"Return your answer strictly as a Python-style list phrases that could briefly describe
"the suspicious scene split by commas.
"No additional commentary or text, return only the list." "
To produce PVAD during inference, we augment PVAD prompts with template sentence containing Iv. Specifically, we inject the following sentences: lemplate(tv) = f'In addition, we have identified certain {dataset, {dataset_prior) behaviors that may appear in the video. Please consider these carefully when deciding on the final anomaly rating. [Potentially reported suspicious activities: {tv}]" right after the first system prompt part of PVAD-
Prompts used in VAL During spatial localization of anomaly regions in video frames, we use the simplest default prompt provided by the official release document of Qwen2. 5L (Bai et al. 2025
Prompts for PLOC
"role": "user",
"content":
f'('fi'"
"Analyze this image and identify any suspicious anomalous region, if present."
"Return your answer in JSON format:
"[["bbox 2d": [x1, yl, x2, y2), "confidence": c)]."
To incorporate ground-truth or extracted anomaly priors lv, loractes we simply augment the PLoC by adding them the start of user prompts hints to the model. Specifically:
Prompts for Płoc
"role": "user",
"content"::
[']]
"The video could contain the following anomaly type: "(fv)""
"Localize the suspicious region or individual in this image."
"Return your answer in JSON format:
"[["bbox_2d" [x1, yl, x2, y2), "confidence": c)]."
Prompts used in VAU For VAU task, we fixed PVAU across different test domains (UCF-Crime, XD-violence), but varied them across different pretrained fyLM for the best baseline performance, which are:
26

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Prompts for pVAU (Videochat-Flash- 2B) [L et al. 2024b]
user prompt f" Pleaase unalyze the video for any anomaly activities, If there is any anomaly, describe the anomaly activities present in the video. After description, analyze why it is an anomaly without timestamps. If no anomalies are found, state that the video appears normal and then describe the scene in detail. (V))
Prompts for pvau (VideoLLaMA3-7B) [Zhang et al., 2025a
"role": "system",
"content":
"You are an Al assistant analyzing a video." "
"role": "user",
"content": (
("())"
"Please analyze the video for any anomaly activities in detail.
"If there any anomaly, describe the anomaly activities present in the video in detail.
After description, analyze why it is an anomaly without timestamps."
"If no anomalies are found, state that the video appears normal and then describe the scene
in detail."
As covered in the main text, producing PvAu is simply appending template prompts with ly to the end of the system prompt or before the user prompt if the model does not support cus- tomizing the system prompt) of PVAu- Specifically, templateyau(tv) = =Foor better anomaly detection and description in detail, a preliminary analysis suggests that the suspicious activity could be related to tv. Use these information to guide your anomaly detection analysis."
C.2 Detailed sampling strategies
Sampling clip c; around f, in VAD Recent VLMs gain capability to process multiple frames as videos [Bai et al. 2025 Li et al. 2024b Zhang et al. 2025a]. This a desirable functionality would like to exploit when dealing with frame-wiss VAD. As single frame may not be able to represent contiguous events. Therefore, following previous works sampling multiple frames to predict s; [Zanella et al. 2024 Ye 2025), we opt input series of frames o; around the target Si instead of taking Ji only. Specifically, we sample & by following steps.
Let the video run at [ps ry and denote by w [s] the dataset-specific temporal radius we keep on either side of f.. Empirically, we set w = 10 s for UCF-Crime and XioVinne, and w = s for UBnormal (in which most clips are only 10-15 long). The total window length in frames is 2w ry + and the half-width
L/2]. Bounding the window to the video limits,
a = max i ()
5),
b = min (T, i 6),
we draw 10 evenly spaced indices
I(i)= = linspaco(a, b, N)).
= = I()))
Thus, always contains 10 frames centered as much as possible on f. That means, for 30 fps videos in UCF-Crime, the sampling spans up to 150 frames (5 on either side, automatically shrinking near the video boundaries.
Sampling for downstream tasks For VAI. task, we sample the frames containing anomalies
following to practice in previous works LLu and Ma 2019 Wu et al. 2024b). For VAU task,
27

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adhere to the default configuration in Zhang et al. 2024b). which samples 16 frames per video for all the methods taking frame inputs.
Aromaly Score Vasalizttinn A050
Anomay Score Vouua bazen Nerra (75
ASC
04-
07
(a) lv =phyysical altercation, assault,
(b) lv
"crosswalk, traffic light",
fighting", loralle "Assault"
Londle
"Normal'
Avanaly Seave Weatratien surdayyya 1264
Jvrrely sore Veurbsteee veurbsiee-wsteeyl aabberyy22 1264
14
0.5
209
04-
754
1250
1750
(c) tv = "attempted break-in, in,, attempted
(d) tv = "kidnapping, assault", famdle "Robbery"
burglary", turalle "Burglary'
Aromas Score Youd odion 20.11 +03-05-51222000 29-9-112
Anomaly Sear Viustration Suug And Dangeuus 1x3557 #12-57-52 01-6445
Aromay
0.
1302
(c) tv "kidnapping, fighting, choking, kicking, punching", foracle "Fighting, Shooting"
(f) ty "fighting, hitting with sticks, throwing objects, running away", tomale "Fighting"
04
0.2
0.2
(g) tv tv)",, tancle " "
(b) tv "fighting, hitting, pushing", Lonale "Fighting"
Figure 7: Frame-wise anomaly score plots for eight representative clips. Our method exhibit consistent performance on various video/anomaly types. The comparison between ly and loratte (The original class annotated in /Sulanni et al. (2018) Wu et al.] (2020) is given for each sample, suggesting the qualitative performance of the anomaly prior extraction step.
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Figure 8: Qualitative examples of our localisation outputs. Each plot the compares detected anomaly window using baseline prompts and InterTC-refined prompts against the ground truth bounding boxes.
D Additional qualitative results
D.1 More results on frame-level video anomaly detection
We show additional qualitative temporal VAD results with the corresponding tv tags extracted in Figure 7 For most samples, there are clear and reasonable tags ty extracted. There are also ambiguous tags, e.g., Appendix C.2 while the performance of the VAD task re- mains stable. Another interesting observation here that the lv extracted, in most cases, are analytical tags for rough loaale categories. For example, in Appendix C.2 the elaborated Iv "fighting, hitting with sticks, throwing objects, running away" are more tractable then the rough laracle = "Fighting", which explained the observed gap of quantitative performances when using different anomaly priors for VAD task in Tbble
As is shown, despite our method exhibit decent performance in flagging various kinds of anomalies, failed occasionally on small event gaps (e.g. Figure 7e We suspect that this insensitivity may be due to the uniform sampling around fs we employed to obtain c;. This may result in the do not have the necessary granularity to represent extremely short video clips. While this this work, future works may consider dynamic sampling strategy improve the baseline VLM for VAD.
our focus in
D.2 More results on spatial video anomaly localization
The additional localization visualization in Figure & gives clear evidence proving the performance gain by incorporating Inter-Task Chaining of anomaly priors. The ly text prompts suggesting possible anomaly contexts allow for more accurate and reasonable groundings.
D.3 More qualitative results on video anomaly understanding
In addition to the results shown in Figure 4 we include extra qualitative comparisons in Figure and Figure 10 The results clearly show that MLLMs assisted with Inter-Task Chaining produced excellent VAU results, which accounted for the quantitative performance gains in terms of both traditional NLP metrics and preference on several dimensions of GPT-based evaluations [Tang et al. 2024). However, we also noticed that sometimes our method produced overly verbose answers compared with other
29

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counterparts. This actually aligns with trend of redundant outputs discovered in LLM reasoning (Sui et al., 2025]. Despite this drawback, the majority contents in our generated descriptions are still focused on the desired topic of anomaly analysis and providing additional details, further enhancing explainability.
Ground-truud
No onomaly exists video. video depicts mundine scene where a man wearing pink takes some money and places it a counter, puts the money of displays it, with no unusual out-of ardinsry events securring throughout the entire clpp
"The appears normal!"
"The video appcors normal. The man is seen stunding behind counter in store, handling money counting it. He then the money his pocket apporent anomalies
"The video depicts a mundone scene whene man in a pink shirt is seen handling small object, possibly and then proceeds from bax, which appears few items container a box places the items back into the box, and the video ends with the man looking ot the itens in the box."
of poper card,
owny.
man
lade3Llama3-78 Baseline
Ours
Holmes
Ground-truth
"There anomaly the video, specifically intentionall action, the flome sudienly and uncxpeettally spreads rapidly, engufing the tree and surrounding enviroement indicating intentiooall damags harm. The basis for forgingingg this unomaly that the man's is actions init itllylly appear intenfional and controlled, but the sudden and uncontrolled spread of the flame auggestt loss
The anomaly shows attemphing light Christmas Instead of controlled and
control, implyng a delibertte act of arsen."
"The vidko aspears normal. The scene hallway with christmes tree, shelf, and deor person enters the frome, walks towards the deor. opens it, and exits, The person then returne to the frons, walks lowerds the shelf, and bandi down to pick something up, The person then stands up and wilks out the frome. The video ends with the hallway empty".
The vides Constmes, Christnas decorated visble. setting
anamaly involves the black entering the
the deor, then luaving the which suspicious and unuuua behavier that deviates from narmal and expected td actions in typical housshhld setting.
and appears odjusting sonsthing then the room Shortly ofter. smoke to fill the roon, sesouring the view The smoke becomes dender ever time, eventually filling the entire frome. The source of the smoke not visble in the wide., This video contoins on anamaly as it showd smoke filling the room, which is not a normal occurrence in decorcted room. The smrke could be caused by fire or other hazardous situstion. which would require immediate attention and action to ensure the safety those the reom.
enters
shelf.
VideoL.amma3775 Bascline
Ours
VolmesVAU
Ground-truth
"The onomaly exists and itss specific nome is V'Explostonn.. The aromaly event is machine explesion that emits lange anount of sparkss and snoke, occurving sudderly and intersely, with a rapid release of energy and matends that deviates from nornel machine operation. The basis for judg ng this cnoncly the sucdon and intence releose energy and materiass indicoting a paternally hazordous and unexpacted event."
"The video begins with a view of on industrisl setting, showing a large piece of machinery and control ponel. A nan in hard hat is seen working on the control panel. manipulating suitches and buttons, The scene than morestonn to dark, alurry image of a buildigg night, followed series frames visible. There ne apporent anemalies content."
"The video begine with mon in on orange hard hat s standing in front of a large electrical panel. He is looking of the panel and appears to be inspecting it. The man then reaches out and touches are of the switches on the penel. Sudsenly. there bright flash light loud explesion. blast
"The onemaly event involves a mon in white shirt and black pants walking tewards the comers, and then a man in white shirt and block pants walking towards the comere, with fire burning in the bockground, suggesting a pottential explesion on
text \"Livelesk"
man is threwn by destroyed. The ends electrical ponel is damaged ponel
interiinaalyy anemaly surrounding shows pandl This and could potentially servous harm the and others the viontt.""
explosion electrical expected behavior
VideoLlama3-77 Bascline
Ours
Holmes VAU
Figure 9: Representative qualitative results for the video-anomaly understanding task (part-
2). Green parts represents correct description/reasoning about the anomaly and the red parts highlight the statements inconsistent with the ground-truth.
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Ground-truth
costure and the other gney grey welf costune, basherball court, where they perforn antics, dence, and engage with players and staff members the didelnes, ultimately curminating in series of donce moves and acrobatic stunts, and showcasing verious actiities such naking piayfull wresting, interacting, and performing dance routine, while players and coaches seen the bockground. shretching. atting on the sdelines watching the interaction.
There onomaly in the video. The video depicts series of nomnll playful interactione between mascots,
"The video appears normal. The scone takes place on basketball court where two mascots, one orarge and one gray, are performing dance routine. They interact with each other and the stretching and preporing
"The video is an analysis of a basketball court where two noscots, one in on orange bird costune and the other engaging in playful interactions. The moscots playfully jumping, appears halftime entertainment sayment. There are no visible anomalies video; it seems to be normal, lightheorted Interaction between the mascets"
"The antomaly events involve series physical altercations between mescots and players, including player in white jersey being knocked to the ground by a moscst, a player in white jersey being pushed to the ground by mascot, and plcyer jersey being knocked the ground moscot, with the mascots engaging aggressive behaviar and physical contact ployers"
gray welf costume,
players, who the game. The mascots' energetic entertaining adding gome attnesphere."
wrest running
white
pre-
part a pre-game
VideoL Baseline
Ours
Holmes
Ground-truth
identified VTightinn V. The orencly invales verouut marrial orte techraques, including blecking, striling, sonsie settings, incuuiing an outdoor oneo with loundry hanging is the backgrourd and feature different individuus,, auch as man in dark jocket and cop. man in black
anomaly exists and
of physical attercations between individuals, showcasing wach other, The svents unfoid dif
well attumpte
and a mon in slue. The onemcly events one characterized by viclent and aggnessive behawiar, incldding gropsling, holding, and chanesgraphed combot rourines, which deviste from nomnal and pooceful hunon interactions. The bass for judging this anamaly is the clear depiction of aggressive and violent behovier between individaals, which signfiiont deviation fram narmal humsn interactions
video appears or TV show. featuring a mon block outfit engaging vorious acticties, is seen talking to another mon, perforning nartial ants moves, and interacting with different people in the envirorment. The sotting includes outdoor lecations with, greenery, buildings, and loundry hanging on lines. There are no apparent anomalies in the video."
scone from movie
"The video begins with men uniform stonding front of green door. gesturing with hands, The scene, then shifts to non in a blue jocket and cap stonding in front of a building with the number V'1SOV" on it. He is seen pushing onether mon, who is wearing a black foiform. egainst a wall. The man in the blue jocket then punches the mon in the block uniform. cousing him to fall to the ground. The man in the blue jocket then, stands over the fallen mon. looking down at him, The anemaly in this video is the physical confrontation belwsen the two mes, This type of behavior is not typically seen in public spaces and could be consddered eggressive or violent. It possible that the two men were involved in dispute or aftercation that escalated into physical vialance. However, withour further context information. it is difficult to determine the exact circumstonces surrounding the confrontation"
ahomaly events invo of confrentational physical
two kicks, groppling, one indlvduall appeaning be defersive stance and the stonce, suggesting potential fight altercation"
including
more aggressive
VidooLlama3-7B Bascline
Ours
VolmesVAU
Figure 10: Representative qualitative results for the video-anomaly understanding task (part-
1). Green parts represents correct descriptiin/reasoning about the anomaly and the red parts highlight the statements inconsistent with the ground-truth.
Table 13: Amortised per-frame processing time (sec/frame) for a full UCF-Crime test run, model loading excluded, smaller value means faster.
Method
VLM Captioning
Caption Cleaning
LLM Summary
LLM Scoring
Score Refinement
VAD Overall
Zamella et al. 2024 Ours
0.06736
0:01490
0.01684
100 0.00314
0.00673 0.00026
0.11691 0.02927
0.02587
E Running-time analysis
As mentioned earlier in Section 3.1 method has relatively efficient inference process due to the selective prediction nature saving unnecessary thinking on samples where the first round scores show enough confidence. Beyond this, we also find that our method is faster than previous baseline zero-shot LLM work [Zanella et al. 2024] by design. In the following, we provide a complexity analysis of our inference steps and compare it with that of the prior work.
Our test-time IntraTR pipeline for VAD requires! 1 VLM captioning query and 1 LLM scoring query per 16 frames, along with a single VIM query per suspicious video to extract tags. For videos flagged as "uncertain", we perform an additional L.LM scoring query per 16 frames. In total, our method performs at most 1 VLM and 2 LLM queries per 16 frames, plus fewer than I VLM query per vidco
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Score Visaliztioon
X064
Aromaly Soow Vausheallen www443M0A #0D-00-d0 0043-40
Suee
"thul"
Groed disprrall poice presence, thantic simaaphere' 'Hoo""
64
Sande 'dealing'
Srack
VAU Results
Revveee couoee foner "oroge welt undon of hortgg shorp nor vikee
peggl velicia Rable la forgge yoalti oicaely soko au' supioou bohenur Cas siught Tax and mide boce allan suggery have
doomler and the conral ceveeperee ower proumme securned alliuo snfery,
Figure II: Failure video anomaly analysis cases. Both contains nuanced anomaly events that may be hard to determine. We find that for both cases, the model can still reasonable anomaly tags lv despite unsatisfactory VAD scores, therefore still yielding partially correct (Green/Red fonts represents Correct/Wrong statements) textual anomaly descriptions.
on average. In contrast, full method of previous work [anella et al. 2024 performs up to VLM captions per frame and additional LLM queries for summarising and scoring per 16 frame. also requires additional refinement steps that introduce massive costs of encoding captions and vector searching.
Table 13 reports the amortized processing clock time inference speed on RTX 3090 GPUs for full of the UCF-Crime test set (model loading time excluded). This gives clear supporting evidence of our efficiency advantage over the previous work.
Limitations
Despite its effectiveness, our method exhibits several limitations. First, its performance is funda- mentally constrained by the representational capabilities and prior knowledge of the underlying frozen multimodal large language models, which may occasionally introduce semantic biases or inaccuracies inherited from their pretraining data (see failure cases in Figure TT]. Secondly, due reliance on frozen models, our approach may suffer from reduced sensitivity detecting highly subtle or domain-specife. anomalies compared to explicitly fine-tuned models.
G Broader Impacts
Our work aims at enhancing public safety through better anomaly detection and interpretability in surveillance systems. However, broader deployment raises ethical considerations regarding privacy and potential misuse. Improved localization and descriptive capabilities could inadvertently facilitate invasive surveillance practices profiling if misapplied without proper goverance.. Thus, any practi- cal application of our method should be carefully regulated, ensuring transparency, accountability, and compliance with privacy laws and ethical guidelines prevent societal harm while benefiting public security and safety.
32


Reviews:
[{"review_id": "hgeRVa52yI", "reviewer": "Reviewer_Savy", "summary": "This paper presents a unified reasoning framework for video anomaly analysis. By leveraging intra-task reasoning and inter-task chaining, the approach integrates temporal detection, spatial localization, and semantic explanation into a unified framework, improving  both interpretability and generalization. The framework achieves state-of-the-art zero-shot performance on multiple benchmarks, demonstrating the potential of foundation models, when guided by well-designed prompts, to deliver practical and interpretable video anomaly analysis.", "questions": "1. How sensitive is the method to the choice of hyperparameter l, given the varying duration of anomaly events? A brief analysis would be helpful.\n\n2. In Table 2, why does adaptive m underperform compared to fixed m? Please explain the possible reasons.\n\n3. What is the inference speed of the full test-time pipeline involving multiple VLMs? Any runtime or resource analysis would be useful.", "limitations": "The paper does not explicitly discuss the limitations or potential societal impacts of the proposed method. The authors are encouraged to:\n\nDiscuss the computational cost and inference latency, especially given the use of multiple VLMs at test time.\n\nAddress possible failure cases, such as ambiguity in anomaly definition or limitations in zero-shot generalization to unseen domains.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\n1. The paper proposes a unified framework that seamlessly integrates multiple tasks in video anomaly analysis, including detection, localization, and explanation.\n2. The approach achieves state-of-the-art performance in a zero-shot setting, without requiring any additional training or fine-tuning, which highlights its strong generalization capability.\n\nWeaknesses：\n1. Since anomaly events can be either very brief or span extended durations, how sensitive is the method’s performance to the choice of hyperparameter l? A discussion or analysis on this would be helpful.\n2. In Table 2, the adaptive m setting does not outperform the fixed m version. Could the authors provide more insight into why adaptive m yields inferior results in this case?\n3. The proposed test-time reasoning involves multiple sequential steps and the use of multiple vision-language models (VLMs). What is the overall inference speed?\n4. The equations in the paper should be numbered for easier reference and clarity.", "was_revised": true, "final_justification": "Thank you for the thoughtful rebuttal. I will be maintaining my current score.", "rebuttal": "$~~~~$We thank the reviewer for the thoughtful evaluation and for highlighting the paper’s advantages of (i) a unified treatment of detection + localisation + explanation and (ii) strong zero‑shot results without extra training. In the following parts of the rebuttal, we wish to address the concerns one by one:\n\n-----\n\n### Major concerns\n\n> how sensitive is the method’s performance to the choice of hyperparameter l?\n \n$~~~~$We heuristically set our minimal suspicious window $\\ell = \\text{max}(300, T/10)$, in which 300 frames is a floor for the shortest window $W_{\\text{max}}$. Since a clip $c_i$ (the smallest scoring unit) also spans $300~\\text{frames} \\approx 10s$, we expect that lowering this floor has little effect. \n\n$~~~~$As for the ceiling term $T/10$, we explored its stability by varying divisors on $T$. \n\n**Results:**\n\n|  | $\\ell = \\text{max}(300, T/5)$ | $\\ell = \\text{max}(300, T/10)$ | $\\ell = \\text{max}(300, T/15)$ |\n| --- | --- | --- | --- |\n| UCF AUC (%) | 81.07 | 84.28 | 83.66 |\n\n$~~~~$As a result, **an overly large $\\ell$ (smaller divisor) degrades the performance**. We suspect that a large window size $\\ell$ **hides fleeting anomalies** as the window may have higher probability of containing benign frames with lower scores, resulting in a lower estimate of the surrogate video-level anomaly probability $\\tilde{s}_V$.\n\n$~~~~$In contrast, **smaller windows (larger divisor) remain stable** because the smaller window makes it **easier to find short-lived anomalies** with averaged scores near the natural decision boundary. We have included the ablation table above and discussion in the revised paper.\n\n$~~~~$In addition to the heuristics we used, it is also possible to introduce additional time-series segmentation model to identify events from sequences of frame scores.\n\n-----\n\n> Could the authors provide more insight into why adaptive m yields inferior results in this case?\n\n$~~~~$When setting a fixed $m$ value, we implicitly assumed **how ambiguiously the anomalies were defined** over the test samples.\n\n$~~~~$Empirically, a **smaller $m$ works well for UCF‑Crime and XD‑Violence where clearly defined crime/violent anomalies types may be obvious to the models**, so IntraTR can **\"trust\"** the first round prediction more by using a smaller $m$.\n\n$~~~~$However, on UBnormal, whose **anomalies are less clearly defined** (outlier human poses), the prior of **“smaller $m$” does not generalise well**, resulting in slightly lower performance. This result is expected, as greater ambiguity in anomaly definitions makes the initial estimation less reliable.\n\n$~~~~$Therefore, we offered an option of an adaptive $m$ estimating the per-video difference between normal and abnormal frames. It achieves consistently competitive performances across datasets, albeit sometimes slightly below the best fixed value. Thus, adaptive m serves as a reliable default bootstrap setting. We clarified such trade-off in **Table 7 and Section B1 (Lines 488-499)** of the submitted paper.\n    \n-----\n\n> What is the inference speed of the full test-time pipeline involving multiple VLMs?\n\n$~~~~$To answer this question, we first analyse the complexity of our inference steps and compare it with the prior zero-shot VAD work, LAVAD (CVPR ’24).\n- Our test-time IntraTR pipeline for VAD requires 1 VLM captioning query and 1 LLM scoring query per 16 frames, along with a single VLM query per suspicious video to extract tags. For those videos flagged as “uncertain”, we perform an additional LLM scoring query per 16 frames. In total, our method performs at most 1 VLM and 2 LLM queries per 16 frames, plus fewer than 1 VLM query per video on average.\n- In contrast, full method of LAVAD (CVPR ’24) performs up to **5**  VLM captions **per frame** and **2** additional LLM queries for summarising and scoring per 16 frame. It also requires additional refinement steps that introduce computational costs of encoding massive captions and vector searching.\n\n$~~~~$The table below reports **amortised processing clock time inference speed** on 2 RTX 3090 GPUs for a full UCF‑Crime test set run (model loading time excluded):\n\n| Method | VLM Captioning (sec/frame) | Caption Cleaning (sec/frame) | LLM Summary (sec/frame) | LLM Scoring (sec/frame) | Score Refinement (sec/frame) | **VAD Overall (sec/frame)** |\n| --- | --- | --- | --- | --- | --- | --- |\n| LAVAD (CVPR 2024) | 0.06736 | 0.01490 | 0.01684 | 0.01109 | 0.00673 | 0.11692 |\n| **Ours** | **0.02587** | - | - | **0.00314** | **0.00026** | **0.02927** |\n\n$~~~~$Besides the VAD task, the VAL and VAU tasks each take standard inference time of the selected VLM (VAL: Qwen2.5-VL-7B, VAU: various models tested in the paper). The VAL takes one query per frame (by task formulation), while the VAU takes one query per video.\n\n$~~~~$As show by the results, despite neither method is real-time, LAVAD requires more LLM/VLM inference and takes longer time to refine the results compared with our method. We plan to include this discussion in further revision of the manuscript.\n\n-----\n\n### Other weaknesses:\n\n> The equations in the paper should be numbered for easier reference and clarity. \n\n$~~~~$Thanks for pointing out, they have been numbered and referenced properly in the revised version.\n\n> The paper does not explicitly discuss the limitations or potential societal impacts of the proposed method.\n> \n$~~~~$We briefly discussed the **limitations and potential societal impacts** in **Section E, F (Lines 609-623)**. We have make it clearer by referencing to them in the main text in the revision.\n\n> Address possible failure cases, such as ambiguity in anomaly definition or limitations in zero-shot generalization to unseen domains.\n\n$~~~~$Thanks for the suggestion. We found some **failure cases when looking at normal or inconspicuous anomaly (e.g. Shoplifting) video samples**, where the model simply outputs either an empty tag $t_V$ list or neutral scene descriptions. e.g., for video visualised in **Fig. 7 (b),** the extracted $t_V = \\text{``crosswalk, traffic light\"}$. \n\n$~~~~$Since we are not allowed to post any external links or images during the rebuttal period, so these discussions and more qualitative analysis on failure have been included to the paper in the revision.\n\n-----\n\n$~~~~$We thank the reviewer again for the helpful feedback and high-quality review. We hope these clarifications address your concerns."}, {"review_id": "nsSEW0KZzs", "reviewer": "Reviewer_jCKt", "summary": "The paper presents a Unified Reasoning Framework for Holistic Zero‑Shot Video Anomaly Analysis, which leverages frozen vision–language models (VLMs) and large language models (LLMs) to perform, without any additional training, three core anomaly tasks: Temporal Video Anomaly Detection (VAD) via an Intra‑Task Reasoning (IntraTR) pipeline that computes frame‑wise anomaly scores, extracts contextual “anomaly tags”.Spatial Video Anomaly Localization (VAL) by chaining the VAD priors (tag list and refined scores) into a frozen VLM localization head, yielding bounding boxes for anomalous regions;Textual Video Anomaly Understanding (VAU)", "questions": "**Suggestions**- Overall, the quality of the work can be improved by comparing the proposed zero-shot technique with other prompting methods, especially Chain-of-Thought (CoT) and recently released reasoning-based models. In addition, the issue of data contamination also needs to be addressed to fully validate the zero-shot performance of the proposed method.\n- **Zero‑Shot CoT Baselines**  Addition of chain‑of‑thought (CoT) baseline would help.  \n\n- **Data Contamination** :- To avoid data contamination issues authors could provide results using LLM/VLM  released before a dataset.\n \n- **Ablate Monolithic Multimodal LLMs**   Evaluate an end‑to‑end GPT‑4V or VideoLLaMA pipeline without discrete VLM/LLM modules.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n- This work presents a novel zero-shot approach for anomaly detection\n- Method validation across multiple anomaly detection formsFramework demonstrates functionality on various modes of anomaly detection like VAU,VAD and VAL\n-Testing conducted on three benchmarks (CCTV, dash-cam/movie violence, synthetic scenes) with performance metrics compared against existing methods. Results show 91.3% AUC on XD-Violence versus 85.4% for baseline approaches.\n-Systematic ablation analysis - This work examines individual component contributions including LLM scoring, tag priors, gated refinement, and fixed versus adaptive gating margins.\n\n**Weaknesses**\n- Absence of zero-shot CoT baseline comparisons No empirical evaluation against alternative zero-shot chain-of-thought approaches.\n- Dataset contamination UCF-Crime, XD-Violence, and UBnormal datasets may overlap with model pretraining data. Evaluation lacks recent benchmarks or challenging scenarios that would validate generalization claims. Authors could refer to more recent datasets like MSAD[1]\n- Insufficient justification for modular architecture : Separation of vision (VLM) and reasoning (LLM) components lacks empirical support. No comparison with end-to-end multimodal models (GPT-4V, VideoLLaMA) to assess performance trade-offs or computational efficiency.\n[1] Advancing Video Anomaly Detection: A Concise Review and a New Dataset.Liyun Zhu, Lei Wang, Arjun Raj, Tom Gedeon, Chen Chen", "was_revised": true, "final_justification": "The authors have addressed key concerns raised during the review. They added new experiments, clarified architectural choices, and provided evidence supporting the generalization capabilities of their approach. While some aspects could be further explored, the paper is significantly strengthened, and I have increased my score accordingly.\n\n**Resolved Issues:**\n- Ran preliminary evaluation on the recent MSAD benchmark to support zero shot generalization claims and removing any chance of data contamination.\n- Justified the reasoning behind using a modular LLM-VLM pipeline instead of a monolithic VLM.\n\n**Unresolved Issues**\n- CoT baseline still missing.", "rebuttal": "$~~~~$We thank the reviewer for the detailed and helpful review, which acknowledged the novelty, versatility and completeness of our paper. Below, we address the concerns raised:\n\n\n### Zero‑Shot CoT Baselines\n\n> Addition of chain‑of‑thought (CoT) baseline would help.\n\n$~~~~$We did not find existing Chain-of-Thoughts (CoT) prompts that are customised for reasoning steps in video anomaly-related tasks. Therefore, to test whether reasoning steps can help, we chose to test the performance of a recent vision reasoning model GLM-4.1V-9B-Thinking (released Jul. 2025) that is claimed to be **a VLM capable of long chain-of-thought (CoT) inference**.\n\n- Results:\n   - Temporal Video Anomaly Detection (VAD) performance:\n    |  | UCF AUC(%) | XD AP (%) | XD AUC (%) | UBN AUC (%) |\n    | --- | --- | --- | --- | --- |\n    | GLM-4.1V-9B-Thinking (per-16-frame scoring) | 61.80 | 72.73 | 52.93 | 60.81 |\n    | **Ours** | **84.28** | **91.34**  | **68.07**  | **68.98** |\n\n   - Spatial Video Anomaly Localisation (VAL) performance:\n\n     |  | TIoU(%) |\n     | --- | --- |\n     | GLM-4.1V-9B-Thinking | 10.33 |\n     | Qwen2.5-VL-7B (baseline) | 24.09 |\n     | **Qwen2.5-VL-7B (with $t_V$)** | **25.21** |\n\n  - Textual Video Anomaly Understanding (VAU) performance:\n    - On UCF-Crime:\n        \n        \n        |  | BLEU | CIDEr | METEOR | ROUGE |\n        | --- | --- | --- | --- | --- |\n        | GLM-4.1V-9B-Thinking | 0.317 | 0.019 | 0.165 | 0.182 |\n        | **Ours** | **0.345** | **0.023**  | **0.175**  | **0.188** |\n    - On XD-Violence:\n        \n        \n        |  | BLEU | CIDEr | METEOR | ROUGE |\n        | --- | --- | --- | --- | --- |\n        | GLM-4.1V-9B-Thinking | 0.368 | **0.038** | 0.182 | 0.187 |\n        | **Ours** | **0.399** | 0.029 | **0.198** | **0.200** |\n\n$~~~~$From these observations, despite the reasoning model achieves a **comparable performance on the VAU task, we noticed that it is not working well on more specialised tasks** of temporal VAD and spatial VAL. Especially for the localisation task, we find that the thinking model reached a much lower TIoU than earlier non-reasoning Qwen2.5-VL-7B baseline. This is expected since the reasoning steps the model has learned may **not generalise to the specific tasks** requiring fine-grained understanding of video anomalies.\n\n$~~~~$On the other hand, the **reasoning steps for VAU can help the model produce more details about the video scene** (Fig. 1 (a) in [1] reported that reasoning models tend to generate much longer responses.) The excessive details described may explain the performance improvement over non-thinking models on the VAU task.\n\n\n### Data Contamination\n\n> Dataset contamination UCF-Crime, XD-Violence, and UBnormal datasets may overlap with model pretraining data.\n> \n\n$~~~~$We respectfully **disagree with this claim as there is no clear evidence for such overlap**. For all the VLM/LLM we use, none reports the use of niche VAD dataset/tasks. (See Table 1-3 in [2], Section 2.2.1 of [3] and Section 8 of [4].) Therefore, we trust those models were not trained on the VAD test samples.\n\n$~~~~$In addition, even if a handful of test examples were seen during pre‑training, given that none of them were tuned to complete VAD task, they **do not have access to the anomaly groundtruth** (temporal intervals, bounding boxes and textual anomaly descriptions). Therefore, they **lack prior knowledge of the video anomalies** beyond the “commonsense” inherited from language model components in it.\n\n-----\n\n> Evaluation lacks recent benchmarks or challenging scenarios that would validate generalization claims...MSAD\n\n$~~~~$Thanks for the suggestion. We also considered this MSAD dataset and requested access before our initial submission, but we did not receive a response at that time. Following the suggestion, we requested the dataset again during the rebuttal period and have now been granted access.\n\n$~~~~$We managed to run some **prelinminary evaluation results as shown in table below** (the first 4 rows are results taken from [5, 6]), which shows our method **achieved comparable performance to SoTA** weakly supervised (WS) baselines. We are currently running additional experiments and will include further zero-shot baseline comparisons in the revised manuscript.\n\n| Methods | MSAD AUC (%) | MSAD AP (%) |\n| --- | --- | --- |\n| *WS RTFM* | 86.7 | 66.3 |\n| *WS EGO* [5] | **87.3** | 64.4 |\n| ZS MGFN | 61.8 | 31.2 |\n| ZS UR-DMU | 74.3 | 53.4 |\n| ZS VideoLLama3-7B (end-to-end) | 63.9 | 37.6 |\n| ZS VideoLLama3-7B + Llama3.1-8B | 78.7 | 68.5 |\n| **Ours (fixed constant $m$)** | 85.9 | **76.4** |\n| **Ours (adaptive $m$)** | 86.0 | 75.9 |\n\n$~~~~$Also, given that UCF-Crime (2018), XD-Violence (2020) and UBNormal (2021) were released earlier than the emergence of large VLMs, we can hardly find any strong VLM models that are released before them. However, we wish to clarify that the ground-truth description we tested the VAU task on comes from HIVAU-70k [7], which is a **very recent benchmark released in Jan. 2025**. Making it **unlikely** to be included in the pre-training set of the models picked (7b, VideoChat-Flash, Qwen 2.5 VL all had their first release in Jan. 2025).\n\n### Ablate Monolithic Multimodal LLMs\n\n> Insufficient justification for modular architecture: Separation of vision (VLM) and reasoning (LLM) components lacks empirical support. No comparison with end-to-end multimodal models (GPT-4V, VideoLLaMA)...\n\n$~~~~$We **followed modular architecture of VLM + LLM from previous baseline**, LAVAD [8]. There are also other experiments and claims supporting this design:\n\n$~~~~$ In the manuscript, we have provided **ablation to end-to-end VLM performance** when used for scoring on every 16 frames clips in **Table 3 (a).** As a result, our discrete VLM, LLM framework provide better performance (84.28% against 77.67%).\n\n$~~~~$Same trend was also reported in Table 3 of [8] in which the performance dropped from 80.28% to 72.70% when removing the LLM-scoring module.\n\n- Another earlier work [9] also suggests such **capability of LLMs to coordinate VLM models for better reasoning**. Which could be reasons for degragations observed above when removing the LLMs.\n- Following the suggestion, we also attempted to use VideoLLaMA3-7B direct end-to-end QA with complete video inputs and asking for timestamps of anomalous intervals, which gives even poorer overall performance:     \n\n  |  | UCF AUC (%) | XD AUC (%) | XD AP (%) | UBN AUC (%) |\n  | --- | --- | --- | --- | --- |\n  | Direct QA | 58.68 | 62.52 | 33.76 | 53.73 |\n  | **Ours** | **84.28** | **91.23** | **68.03** | **69.02** |\n\n$~~~~$These rationales justify our modular VLM/LLM design over single model. We have included this discussion in the revised paper. Also, the sub-optimal end-to-end performance suggests that the VLM/LLM may still underfit on video-anomaly related tasks originally, which makes the Data Contamination appears to be less likely.\n\n-----\n\n$~~~~$Thank you for your insightful review and suggestions, which have helped us improve the paper. We hope our answer could address your concern. And we are happy to clarify any further concerns.\n\n-----\n\n[1] Chen, Xingyu, et al. \"Do not think that much for 2+ 3=? on the overthinking of o1-like llms.\" arXiv preprint arXiv:2412.21187 (2024).\n\n[2] Zhang, Boqiang, et al. \"Videollama 3: Frontier multimodal foundation models for image and video understanding.\" *arXiv preprint arXiv:2501.13106* (2025).\n\n[3] Bai, Shuai, et al. \"Qwen2. 5-vl technical report.\" *arXiv preprint arXiv:2502.13923* (2025).\n\n[4] Li, Xinhao, et al. \"Videochat-flash: Hierarchical compression for long-context video modeling.\" *arXiv preprint arXiv:2501.00574* (2024).\n\n[5] Ding, Dexuan, et al. \"Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion.\" *The Thirteenth International Conference on Learning Representations*.\n\n[6] Zhu, Liyun, et al. \"Advancing video anomaly detection: A concise review and a new dataset.\" *Advances in Neural Information Processing Systems* 37 (2024): 89943-89977.\n\n[7] Zhang, Huaxin, et al. \"Holmes-vau: Towards long-term video anomaly understanding at any granularity.\" *Proceedings of the Computer Vision and Pattern Recognition Conference*. 2025.\n\n[8] Zanella, Luca, et al. \"Harnessing large language models for training-free video anomaly detection.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024.\n\n[9] Chen, Liangyu, et al. \"Large language models are visual reasoning coordinators.\" *Advances in Neural Information Processing Systems* 36 (2023): 70115-70140."}, {"review_id": "28EYBopY8s", "reviewer": "Reviewer_DJMu", "summary": "This paper proposes a unified, training-free reasoning framework for video anomaly analysis, aiming to integrate three key tasks: Video Anomaly Detection (VAD), spatial localization (VAL), and semantic understanding (VAU), thereby enabling comprehensive zero-shot anomaly analysis. Unlike previous methods that focus solely on temporal detection or a single sub-task, this framework employs a chained task reasoning mechanism that sequentially infers \"when, where, and why\" anomalies occur during inference—without any additional training or fine-tuning.", "questions": "See in the weaknesses section.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\n1. This paper is well-motivated, as it tackles the anomaly reasoning task under a training-free paradigm.\n2. This paper is easy to read. \n\nWeaknesses：\n1. The authors claim to address the sub-task of spatial understanding; however, I could not find any quantitative evaluation of spatial performance in the main paper. Moreover, in the qualitative visualizations provided in the appendix, it is unclear where the ground-truth annotations for the spatial data come from.\n2. The authors apply Gaussian smoothing as a post-processing step, but I have two concerns. In addition to the standard deviation $\\sigma$, Gaussian smoothing also involves a window size parameter, which is not specified in the paper. Furthermore, the authors do not conduct a sensitivity analysis regarding the choice of window size.\n3. How do the authors obtain anomaly scores for every segment of the video if they only sample 180 frames?\n4. According to the methodology, the authors use both VLMs and LLMs, but the framework diagram does not clearly indicate which specific model is used at each stage.", "was_revised": true, "final_justification": "The rebuttal clarified key concerns, including spatial annotations, segment-level scoring, and model usage. While the Gaussian smoothing detail remains insufficient, the overall clarity and motivation of the paper convinced me to raise my score from Borderline Reject to Borderline Accept.", "rebuttal": "We would like to thank the reviewer for the careful read and for recognising the motivation and readability of our work. Below, we address each of the weaknesses you raised.\n\n-----\n### Lacking evaluation of spatial performance\n\n>  ...I could not find any quantitative evaluation of spatial performance in the main paper...it is unclear where the ground‑truth annotations for the spatial data come from.\n> \n$~~~~$**Table  4** reports *Temporal Intersection‑over‑Union* (**TIoU**), a metric introduced in [1] **specifically for spatial grounding**. The bounding boxes come from the officially released UCF‑Crime annotations in [1] as well. **Lines 224‑228** explain both the metric and the **source of bounding‑box annotations from [1] we use**. To improve visibility, we have referenced the definition at first mention in **Section 4** in the revised version.\n\n\n\n### Missing window size parameter for Gaussian smoothing\n\n> “Gaussian smoothing also involves a window‑size parameter, which is not specified, and the authors do not conduct a sensitivity analysis regarding the choice of window size.”\n> \n\n$~~~~$Thank you for pointing this out. To clarify, we use **the default SciPy implementation** `scipy.ndimage.gaussian_filter1d()`providing the $\\sigma$ values and leave all other parameters at their defaults following previous works [2-4]. The effective radius (window size) therefore equals $\\mathrm{round}(\\texttt{truncate}\\times\\sigma)$ with the default `truncate = 4.0`. This default setting was used in all experiments. We have updated the manuscript to make this explicit.\n\n### Concerns on method clarity\n\n> “How do the authors obtain anomaly scores for every segment of the video if they only sample 180 frames?”\n> \n$~~~~$We wish to clarify that, as stated in **Lines 236-238**, we subsample at most 180 frames in the $W_{max}$ to get the anomaly prior $t_V$. We **do not impose this upperbound of 180 frames during frame scoring step** to get the frame scores $s_i$.\n\n$~~~~$Instead, as described in **Lines 116-121**, for every 16 frames, we sample a clip centred on the index to compute the caption (see sampling details in **Section C.2, Lines 569-582**), and score each clip by querying the LLM with the captions. The output scores therefore represent initial anomaly scores per 16-frame periods. After this initial scoring, we run IntraTR refinement steps as specified in **Lines 164-172** to refine the frame anomaly scores. We have adjusted the presentation and added the clarifications in the revised paper.\n\n-----\n\n> “The framework diagram does not clearly indicate which specific model is used at each stage.”\n> \n- For the VAD task, the VLM is used for initial captioning and then to produce the video‑level anomaly tag $t_V$ (**Line 145**). The LLM then works for the initial scoring on clip captions and an additional Intra-Task Reasoning (IntraTR) to selectively refine the scores with $t_V$ augmented prompt $p^*_\\text{VAD}$ (**Lines 164-172**).\n- In our main experiments, we used VideoLLama3-7B as the default VLM and Llama3.1-8B as the default LLM (see **Lines 239-240**). We also tested other models in **Table 8.**\n- In the proposed Inter-Task Chaining (InterTC) framework, with the video‑level anomaly tag $t_V$ extracted in IntraTR steps, the subsequent localisation/understanding task can input the sample with tag $t_V$ augmented prompts $p^*_{\\text{task=VAL,VAU}}$  to any VLMs (see details covered **Section 3.2**). We varied the VLMs used for downstreams in **Table 5**.\n\n$~~~~$To improve clarity, we have modified the blocks in **Fig. 2** and added more detailed in-text descriptions in the revised version.\n\n-----\n\n$~~~~$We hope these responses address your concerns, thank you again for the insightful review, and we are happy to clarify any further points.\n\n-----\n\n[1] Kun Liu and Huadong Ma. Exploring background-bias for anomaly detection in surveillance videos.383 In Proceedings of the 27th ACM International Conference on Multimedia, pages 1490–1499, 2019.384\n\n[2] Acsintoae, Andra, et al. \"Ubnormal: New benchmark for supervised open-set video anomaly detection.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.\n\n[3] Flaborea, Alessandro, et al. \"Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection.\" Proceedings of the IEEE/CVF international conference on computer vision. 2023.\n\n[4] Micorek, Jakub, et al. \"Mulde: Multiscale log-density estimation via denoising score matching for video anomaly detection.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024."}, {"review_id": "xIzpEf362P", "reviewer": "Reviewer_TbPh", "summary": "The paper addresses video anomaly detection and analysis with a training-free framework. The framework consists of two main components: Intra-Task Reasoning (IntraTR) and Inter-Task Chaining (InterTC).  In IntraTR, the authors use pre-trained VAD models and Video LLMs to generate a list of possible anomalies as refined prompts to LLMs for reasoning. In InterTC, the authors leverage VLMs to generate bounding boxes for anomalies and draw them on the frames, followed by another VLMs with refined prompts for analysis. The authors have conducted experiments on the UCF-Crime, XD-Violence and UBNormal dataset.", "questions": "**Important questions and concerns for the authors to address. Responses will strongly influence the final rating**\n- Elaborate more on the novelty of IntraTR. Explain what the results will be if VLMs cannot capture correct suspicious behaviors (tags) or VAD baseline wrongly localizes the ambiguous segments. Providing experiments can be helpful but is not required.\n- Address the concerns for Table 3 mentioned in the weakness section.\n- Provide computational analysis to prove the claim in line 169.\n\n\n\n**Optional questions and concerns for the authors to address. Good responses can increase the final rating.**\n- Provide qualitative results for anomaly tags or refined prompts $p^*_{VAD}$\n- Analyze what clips may not be fully captured with a fixed window size (Potential Anomaly Clips in the weakness section). Discuss possible solutions that can change the anomaly boundaries to find better anomaly segments. \n\nPlease find the relevant detail for some questions in the weakness section.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n- The proposed method is straightforward and likely effective.\n- Drawing bounding boxes on frames as clues for VLMs is interesting.\n- The experimental results are promising.\n\n\n\n**Weaknesses:**\n- **Concerns about Proposed Method**\n    - The novelty of IntraTR is minor because it simply takes captions from VLMs and refined prompts (anomaly tags) as inputs for LLMs to perform VAD on ambiguous clips. In addition, the design can work if the VLM is powerful enough to find anomalies in video clips and the VAD baseline is good enough to localize ambiguous segments. \n- **Concerns about Performance Improvement**\n    - Given that the authors adopt better VLM on captioning/tag generation (e.g., LAVAD uses BLIP-2, an image-based model, and this paper uses VideoLLaMA3-7B, a video-based model), the performance gain could come from the VLM rather than the design of the IntraTR. For example, the third row in Table 3(a) shows that using frozen VLM and LLM can achieve comparable performance (80.38%) as LAVAD (80.28%) on UCF, showing that the proposed method benefits from the strong VLM/LLM. \n    - Table 3(a) does not clearly show how effective adding anomaly tags is. \n- **Concerns about Experiments**\n    - The component name in Table 3(a) and the paragraph (line261-271) do not match the corresponding name or appear in the method section (e.g., I could not find “soft-margin” and the authors use “Score-based reasoning gate” instead of “Score-gated Reasoning”).   In addition, the design of the simplest baseline (first row in Table 3(a)) is unclear but the performance is surprisingly good. Finally, the difference between LLM-Scoring and Prior-Reasoning is unclear because they both ask LLM to provide the scores and I did not see a reasoning prompt such as \"explain what happens in the scene\" in $p_{VAD}$ in line 531. The unclear table and descriptions make the reviewer hard to understand how effective each component is. \n    - The authors claim that the proposed method is computation efficiency (line 169) but do not provide detailed analysis. I would like to see the computation complexity comparison between this work and other baselines. \n   - In general, the refined prompt with generated tags is why VAD performance improves. Recognizing the anomaly tags for the video clips provides LLMs useful information for anomaly detection and understanding. It would be good if the authors can provide qualitative results of anomaly tags or refined prompts. Showing those results can strengthen the technical reliability of the proposed method. \n- **Potential Anomaly Clips**\n    - The authors set the window size to a fixed value for each video (line 237). Because the framework highly relies on the selection of anomaly clips, I wonder if some clips may not be fully captured with a fixed window size. In addition, as IntraTR only updates the prompt to refine anomaly scores, the temporal boundaries of the anomaly clips remain the same throughout testing, limiting the improvement of VAD.", "was_revised": true, "final_justification": "The authors have addressed all of my main concerns in the review, including emphasizing the novelty of IntraTR, clarifying Table 3, and providing computational analysis for the proposed method. I have been convinced that the novelty of IntraTR is not minor. Therefore, I am leaning toward increasing my final rating.", "rebuttal": "$~~~~$We thank the reviewer for providing a detailed and insightful review and recognising the strengths of this paper in methodology, ideas, and promising experimental results.\n### Concerns about proposed method\n\n> The novelty of IntraTR is minor…the design can work if the VLM is powerful enough…the VAD baseline is good enough…\n\n$~~~~$We respectfully disagree with this claim. **IntraTR** is the **first framework that quantifies the degree of video-level uncertainty of anomaly presence** and uses it to **guide the reasoning step of the zero-shot VAD task**. Therefore, it is not **simply “caption+tags+LLM.”** The necessity of such a design is as shown by the indiscriminate, no‑gate “always‑refine” variant degrading the performance **(Table 3 (a), rows 1→2)**.\n\n$~~~~$Secondly, **IntraTR does not rely on assuming that the VLM/VAD can perfectly report anomaly segments and tags**. Sample‑specific descriptive prior tags $t_V$ extracted from ambiguous intervals can yield more accurate score estimates than \"correct but coarse\" ground‑truth labels, which may not reflect visible cues in inputs **(Table 3 (b), Lines 277-281)**. Thus, IntraTR’s gains stem from how priors are derived and applied, not from assuming perfect tags or exceptionally strong models.\n\n### Concerns about Performance Improvement\n\n> ...performance gain could come from the VLM rather than the design…\n\n$~~~~$Despite simple baseline frozen caption VLM and scoring LLM with a Gaussian post-processing ($\\sigma$=10) already show a comparable performance (80.38%) to the baseline. We wish to remind that **IntraTR further improves the AUC result with a large margin to 84.28%** as shown in **Table 3 (a)**.\n\n$~~~~$To show that our **performance gain is not solely from the stronger capability of newer VLM and LLM**, we run LAVAD [1] with newer VideoLLama3 and Llama3.1-8B:\n\n  |  | UCF AUC (%) | XD AUC (%) | XD AP (%) |\n  | --- | --- | --- | --- |\n  | LAVAD (BLIP2 FLAN-T5-XL + Llama2-13B-chat) | 74.19 | 85.16 | 61.09 |\n  | LAVAD (VideoLLama3-7B + Llama3.1-8B) | 72.99 | 84.64 | 61.20 |\n  | Ours (VideoLLama3-7B + Llama3.1-8B) | **84.28** | **91.34** | **68.07** |\n\n$~~~~$We observed a drop in single VLM performance when using newer model under LAVAD on UCF-Crime. This may be due to the limited capability of sentence encoding VLM (ImageBind), which may fail to recognise more nuanced frame caption differences from newer models. This problem is mitigated on XD-Violence, where more dramatic videos than mundane surveillance footage of UCF-Crime makes raw captions encoded more recognisable in the representation space.\n\n$~~~~$In addition, we show in **Table 8** that our method remains robust under much weaker 2~3B models.\n\n$~~~~$These results demonstrate the effectiveness of proposed IntraTR in providing a further boost to the baseline VLM/LLM VAD that is already strong.\n\n\n> Table 3(a) does not clearly show how effective adding anomaly tags is.\n\n$~~~~$We acknowledge that **Table 3 (a)** does not by itself reflect the effectiveness of the extracted tag list $t_V$, while **Table 3 (b) quantitatively shows** how including tag $t_V$ and ground-truth tags $t_{\\text{oracle}}$ into the prompts **improves the VAD performance** by the IntraTR score-refinement.\n\n\n### Concerns about Experiments\n\n> The component name in Table 3(a) and the paragraph (Lines 261-271) do not match the corresponding name…\n\n$~~~~$Thanks for pointing out the inconsistency, we have updated the paragraph to ensure consistent naming for components in the revised version. For now, we provide a mapping for the inconsistent names.\n- Line 263: “LLM-based Scoring” → ”LLM-Scoring“\n- Line 264: “soft-margin refinement” → “Score-gated reasoning”\n- Line 265: ”prior-rescore” → \"Prior Reasoning”\n\n> …the design of the simplest baseline (first row in Table 3(a)) is unclear…\n\nThe simplest baseline **inputs the VAD task prompt $P_\\text{VAD}$ and 16‑frame clips directly to the VLM which outputs anomaly scores**. I.e., $S_{V}=\\theta_{\\mathrm{VLM}}\\left(c_{i}, p_{\\text {VAD}}\\right), \\quad i=1, \\ldots, T$ in which $c_{i}$ denotes the clips and $p_{\\text{VAD}}$ is standard prompt for anomaly scoring. The $S_V$ is then post‑processed by a default Gaussian smoothing with $\\sigma=10$.\n\n> …the difference between LLM-Scoring and Prior-Reasoning is unclear…\n\n$~~~~$ To clarify, the **Prior Reasoning** simply runs an additional scoring step with the extracted prior tag list $t_V$ **for all videos,** which is exactly the **no‑gate “always‑refine” variant** of IntraTR.\n\n$~~~~$On the other hand, **Score-gated Reasoning** applies the Score-based reasoning gate to enable selective Prior Reasoning on “uncertain videos” as indicated by initial prediction scores (**Lines 147-172**).\n\n> I did not sefe a reasoning prompt such as \"explain what happens in the scene\" in $p_{\\text{VAD}}$ in line 531.\n\n$~~~~$The process of making reasoning prompt $p^*_{\\text{VAD}}$ that combines $p_{\\text{VAD}}, t_V$ is described in **lines 545-550**. We have made this highlighted clearer in the revision.\n\n> The authors claim that the proposed method is computation efficiency (line 169) but do not provide detailed analysis.\n\n$~~~~$ Thanks for noticing this point! To clarify, the word “efficiency” in **line 169** was intended to say that the selective prediction nature of Score-based reasoning gate leads to more efficient refinement to VAD scores, as it skips samples which are far from the decision boundary and therefore reduces the average processing time.\n\n$~~~~$ However, we also noted that our method **is more efficient than the previous zero-shot baseline** LAVAD that essentially uses heavy VLM ensemble.\n\n- Our test‑time IntraTR for VAD requires one VLM captioning query and one LLM scoring query per 16 frames, plus a VLM query per suspicious video to extract tags and subsequently one LLM scoring query per 16 frames in those videos. (**< 1 VLM queries + 2 LLM queries per 16 frames overall**).\n- By contrast, [1] performs **5**  VLM captions **per frame** and **2** additional LLM queries for summarising and scoring captions **per 16 frames**, plus time-consuming refinement encoding massive numbers of sentences and retrieval.\n\n$~~~~$ To prove our advantage in terms of efficiency to previous baselines, the table below reports amortised speeds on 2 RTX 3090 for a UCF‑Crime test run:\n\n  | Methods | VLM Captioning (sec/frame) | Caption Cleaning (sec/frame) | LLM Summary (sec/frame) | LLM Scoring (sec/frame) | Score Refinement (sec/frame) | VAD Overall (sec/frame) |\n  | --- | --- | --- | --- | --- | --- | --- |\n  | LAVAD | 0.06736 | 0.01490 | 0.01684 | 0.01109 | 0.00673 | 0.11692 |\n  | **Ours** | **0.02587** | **-** | **-** | **0.00314** | **0.00026** | **0.02927** |\n\n> Provide qualitative results for anomaly tags…\n\n$~~~~$ Since links are not permitted in the rebuttal this year, we cannot include qualitative visualisations of the tags and refined prompts here. To show what the $p^*_\\text{VAD}$ looks like for ordinary samples, we can provide $t_V$ for samples visualized in the current manuscript. \n\n- **Fig. 7 (a)**:  $t_V = \\text{``physical altercation, assault, fighting\"}$\n- **Fig. 7 (b)**:  $t_V = \\text{``crosswalk, traffic light\"}$\n- **Fig. 7 (c)**:  $t_V = \\text{``attempted break-in, attempted burglary\"}$\n- **Fig. 7 (d)**:  $t_V = \\text{``kidnapping, assault\"}$\n- **Fig. 7 (e)**:  $t_V = \\text{``kidnapping, fighting, choking, kicking, punching\"}$\n- **Fig. 7 (f)**:  $t_V = \\text{``fighting, hitting, pushing\"}$\n- **Fig. 7 (g)**:  $t_V = \\text{``fighting, hitting with sticks, throwing objects, running away\"}$\n- **Fig. 7 (h)**:  $t_V = \\text{``[]\"}$\n\n$~~~~$ For most samples, there are clear and reasonable tags $t_V$ extracted. There are also ambiguous tags, e.g., **Fig. 7 (b),** while the performance of VAD task remains stable. We have included further qualitative results including more failure cases in the revision.\n\n### Potential Anomaly Clips\n\n> The authors set the window size to a fixed value for each video (line 237)…\n \n$~~~~$ We did **not** fix the window size for each video. Instead, we set a **partially adaptive $\\ell = \\text{max}(300, T/10)$ dependent on video length $T$**. We have revised the presentation to make it clearer.\n\n$~~~~$We would like to provide some background justification for setting a partially adaptive $\\ell$. We set the floor to 300 frames ≈ 10s as the minimal “suspicious window”, because **the smallest scoring unit(clip $c_i$) also spans 10s (following previous works LAVAD, VERA), we expect that lowering this floor has little effect.** \n\n$~~~~$As for another **ceiling term $T/10$** for the window size, we tested the stability across different divisors. This provides insights to **how the window fails to capture the anomaly events**.    \n\n  |  | $\\ell = \\text{max}(300, T/5)$ | $\\ell = \\text{max}(300, T/10)$ | $\\ell = \\text{max}(300, T/15)$ |\n  | --- | --- | --- | --- |\n  | ROC-AUC (UCF-Crime) | 81.07% | 84.28% | 83.66% |\n\n$~~~~$**An overly large $\\ell$ degrades the performance**. We suspect that larger windows **hide** short-lifed anomalies (Shoplifting, Shooting, etc.) as the average of the larger windows may have higher probability of containing normal frames with lower scores.\n\n$~~~~$In contrast, **smaller windows remain stable** because the **shorter interval makes it easier to flag short-lived anomalies** with scores near the natural decision boundary.\n\n> Discuss possible solutions that can change the anomaly boundaries…\n\n$~~~~$It is possible to use an adaptive window length $\\ell$. A promising extension is to introduce a **pre-trained time series model to segment the raw frame scores into intervals**, and take the one with highest average scores as $W_\\text{max}$. It provides flexibility to the $\\ell$ without posing assumptions on the target domain. We have added this to **Section E** in the revision.\n\n\n$~~~~$We thank the reviewer again for the insightful review, which helped us improve the paper. And we are happy to address any further concerns."}]

Author rebuttal:

Meta-review: The paper introduces a unified reasoning framework for holistic zero‑shot video anomaly analysis, which leverages frozen vision–language models (VLMs) and large language models (LLMs) to perform, without any additional training, three core anomaly tasks: Temporal Video Anomaly Detection (VAD) via an Intra‑Task Reasoning (IntraTR) pipeline that computes frame‑wise anomaly scores, extracts contextual “anomaly tags”. Spatial Video Anomaly Localization (VAL) by chaining the VAD priors (tag list and refined scores) into a frozen VLM localization head, yielding bounding boxes for anomalous regions; Textual Video Anomaly Understanding (VAU)

Strengths:
+This paper designs a zero-shot approach for anomaly detection, which is more practical.
+Method validation across multiple anomaly detection frameworks demonstrates functionality on various modes of anomaly detection like VAU,VAD and VAL.
+Performance conducted on three benchmarks (CCTV, dash-cam/movie violence, synthetic scenes) show the high AUC over baseline approaches.

Weaknesses:
-The sub-task of spatial understanding lacks quantitative evaluation of spatial performance, while the qualitative visualizations are provided in the appendix.
-Some concerns about Gaussian smoothing including a sensitivity analysis regarding the choice of window size, should be addressed.
-According to the methodology, the authors use both VLMs and LLMs, but the framework diagram does not clearly indicate which specific model is used at each stage.


During rebuttal, some responses have been provided, such as, hyperparameter sensitivity and inference efficiency, more zero-shot CoT baselines, comparing with end-to-end multimodal models, etc.

After rebuttal, The final rating is 4 BA, and all reviewers agree the significant contribution of this paper.
Decision: Accept (poster)