SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations

Bo Li; Francesco Pinto; Minzhou Pan; Zhaorun Chen

REVIEW 4 major objections 4 minor 4 cited by

SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read SafeWatch claims a video guardrail that follows custom safety policies, explains each violation, and beats prior guardrails by 28.2% on its own benchmark while cutting inference cost by 10%.

desk verdict Genuinely promising architecture and benchmark, but the external-evaluation claims are not reliable until the training/evaluation overlap and dataset-size discrepancies are resolved. read the letter →

arxiv 2412.06878 v1 pith:N4TABZ4C submitted 2024-12-09 cs.CV cs.LG

Zhaorun Chen , Francesco Pinto , Minzhou Pan , Bo Li This is my paper · ORCID

classification cs.CVcs.LG

keywords videoguardrailmultimodallargelanguagemodelsafety-policyfollowingparallelpolicyencodingvisualtokenpruningcontentmoderationbenchmarkexplainablesafetyzero-shotgeneralization

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

SafeWatch claims that a video guardrail can be simultaneously accurate, cheap, and transparent: it follows long custom safety policies, returns multi-label violation flags, and explains each flag with content-specific reasoning. The paper argues that current video guardrails fail on at least one axis—simple classifiers flag fixed categories without explanations, while prompted multimodal LLMs are slow, biased by where a policy appears in the prompt, and vague. To fix this, SafeWatch encodes each policy chunk in parallel with equal positional embeddings (PEPE) and prunes video tokens to those most relevant to each policy (PAP). On its new SafeWatch-Bench, a corpus covering six safety categories, the paper reports a 28.2% average-accuracy gain over prior guardrails, a 13.6% gain on five existing benchmarks, and about 10% lower inference cost, with under 1% accuracy loss even at 90% token pruning.

What carries the argument

The load-bearing machinery is Parallel Equivalent Policy Encoding (PEPE) and Policy-Aware Adaptive Pruning (PAP). PEPE splits long safety guidelines into per-policy chunks, masks out tokens from other policies, and applies the same rotary position embedding to every chunk so no policy is favored by its position; this makes the attention matrix block-diagonal over policies and lets the chunks be computed in parallel, removing position bias and cutting prefill cost. PAP computes a cross-attention relevance score between each policy chunk and every video token, ranks the tokens per policy, and keeps only the top-K most relevant ones before decoding, discarding noisy tokens. A safety-aware event sampler based on TransnetV2 first selects one frame per detected unsafe event, and three consecutive training stages fit the InternVL2-8B backbone to the benchmark. The combination is what the paper credits for zero-shot policy following, explanation quality, and the claimed cost-accuracy trade-off.

What would settle it

Compute duplicate or near-duplicate video retrieval between the 199,604 SafeWatch training videos and the evaluation splits of LSPD, XD-Violence, UCF-Crime, FakeSV, and FVC; if any evaluation video appears in training, the reported 13.6% external-benchmark improvement is not a transfer estimate. A cleaner test would be re-running the same benchmark suite on a freshly collected, disjoint sample of each category.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that an open 8B multimodal LLM, fine-tuned in three stages on a large policy-annotated video corpus, can beat closed frontier models at video moderation while being the only guardrail variant that both follows arbitrary written policies and explains its decisions. The design that carries the claim is PEPE plus PAP: policies are decomposed into chunks, masked from each other, and given identical rotary position embeddings so attention is order-invariant; then per-policy cross-attention scores select the top-k video tokens for each policy before decoding. Stage-wise training (multi-task guardrail fine-tuning, adaptive-pruning fine-tuning, and preference post-tuning) turns that architecture into a model that flags six safety categories and produces descriptions, JSON flags, and grounded explanations. The paper reports that this model surpasses GPT-4o, Gemini, and specialized guardrail models on SafeWatch-Bench and on LSPD, XD-Violence, UCF-Crime, FakeSV, and FVC, and that it transfers zero-shot to new policies, such as child safety or firearms, and to new prompting formats.

Load-bearing premise

The 13.6% gain on existing benchmarks and the zero-shot numbers are only valid if the evaluation videos from LSPD, XD-Violence, UCF-Crime, FakeSV, and FVC were never used in SafeWatch's training corpus; the paper never states that these test sets were excluded.

Editorial extensions

If this is right

If the claims hold, an 8B open model can outperform closed frontier APIs on video moderation while running at a fraction of the compute, changing the default deployment choice for platforms that need to moderate large volumes of video.
Policy order and phrasing stop mattering: shuffling, rephrasing, or whitelisting a subcategory changes accuracy far less than it does for autoregressive MLLM guardrails, which is exactly what customized community guidelines require.
Because 90% of video tokens can be pruned with under 1% accuracy loss on the paper's benchmark, inference cost should scale more gently with video length and with the number of policies, making per-policy attention an affordable way to handle many parallel rules.
The structured event-level annotations, with timestamps, descriptions, flags, and explanations, turn SafeWatch-Bench into a resource for tasks beyond binary moderation, such as temporal localization of violations and grounded explanation generation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An editor's read: the 13.6% existing-benchmark gain should be re-measured after an explicit train/test overlap audit; because the paper's source table lists LSPD, XD-Violence, and fake-video corpora as training sources, any test video reused there would inflate what looks like transfer.
The same policy-conditioned relevance score that drives PAP could serve as a free localization signal: the top-k video tokens for a flagged policy indicate which frames caused the flag, so attention itself could power human-auditable moderation logs without extra supervision.
If 90% token pruning really costs under 1% accuracy, dense frame sampling is largely redundant for guardrail decisions; a natural extension is to test whether event-based sampling alone matches the reported accuracy on very long videos.
PEPE's order-invariance suggests a cheap stress test for any policy-following guardrail: permute policy order at inference and require identical flags and explanations, since a model that fails that test is relying partly on positional memorization.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The architecture is the real news here. PEPE's parallel chunked policy encoding with equal RoPE is a clever way to dodge the positional bias that plagues long policy prompts, and the attention-correlation analysis (PCC dropping from -0.90 to -0.09) gives it real empirical support. PAP's token pruning also looks sensible, and the result that the model holds accuracy within 1% while pruning 90% of video tokens is worth taking seriously. The three-stage training recipe — multi-task SFT, pruning adaptation, DPO for explanation quality — is thoughtful, and the explanation-quality gains over GPT-4o are at least plausible given the human-eval component.

But the paper has a load-bearing weakness in how it measures external generalization. Table 21 lists LSPD, XD-Violence, FakeSV/FVC as training sources for SafeWatch-Bench, and Table 3 evaluates on a "random split" of those same datasets. The paper never states that test videos were excluded from training, and Figure 10's no-overlap statement only covers the SafeWatch-Bench split. If any of those external test clips or near-duplicates appear in the 199K training videos, the 13.6% improvement on existing benchmarks is mostly memorization, not transfer. The zero-shot claim on firearms is weaker for the same reason: C3 training already includes 20K shooting videos, so firearms is not an unseen category even though the policy text differs.

The dataset accounting is also off: the abstract says 2M videos, but Table 20 reports 199,604 training and 1,420 test videos. That is a two-order-of-magnitude gap that needs a clear explanation — perhaps the 2M counts something else (clips, events, or pre-filter candidates), but as written it reads as an overclaim.

The PEPE "theoretical analysis" in Appendix A.2.2 is not a real proof. It lays out a causal graph and asserts that equal RoPE makes attention independent of position; the empirical correlations support that conclusion, but the derivation is more rhetoric than math. Minor, because the empirical evidence is decent, but worth noting.

Bottom line: this is a serious paper with good ideas and a potentially useful benchmark, but the central empirical claims are not trustworthy as stated. A corrected version with dedicated held-out external splits, a resolved dataset-size reconciliation, and released code/data could change my verdict. It deserves peer review — a good referee will force the authors to close the leakage question before the numbers can be accepted.

Referee Report

4 major / 4 minor

Summary. This paper proposes SafeWatch, an 8B-parameter MLLM-based video guardrail that encodes safety policies in parallel (PEPE), prunes video tokens per policy (PAP), and is fine-tuned on a newly constructed SafeWatch-Bench dataset annotated through a multi-agent LLM pipeline. The authors report that SafeWatch outperforms prior guardrails by 28.2% on SafeWatch-Bench-Real, 27.2% on SafeWatch-Bench-GenAI, and 13.6% on five existing benchmarks, while also claiming 5.6% and 15.6% improvements in zero-shot generalization to new policies and new prompting tasks, a 10% inference cost reduction, and high explanation quality by LLM and human judges.

Significance. The problem addressed is important and timely, and the paper contains several constructive ideas: PEPE's parallel policy encoding with equivalent position embeddings is a plausible mechanism for mitigating policy positional bias, PAP's policy-conditioned token pruning is a reasonable efficiency heuristic, and the multi-stage training recipe including DPO is well motivated. The ablation in Table 6 suggests each component contributes. The open-sourced release and the large-scale annotation effort are also potentially useful community resources. However, the headline empirical claims are not currently supported: the external-benchmark evaluation appears to overlap with the training corpus, the 'unseen' policy tests are close variants of training subcategories, and the dataset size is misreported. These issues bear directly on the abstract's central quantitative claims.

major comments (4)

[Section 5.1, Table 3; Appendix B.5, Table 21] The claimed 13.6% improvement on existing benchmarks is not a valid estimate of transfer unless the test videos are disjoint from the training corpus. Section 5.1 states that the model is evaluated on a random split of LSPD, XD-Violence, UCF, FakeSV, and FVC, while Table 21 lists LSPD as a training source for C1 (Sexual Content) and XD-Violence as training sources for both C2 and C3, with hundreds of thousands of clips. The paper never states that the test videos from these public datasets were excluded from the SafeWatch training pool; Figure 10's 'no overlap' statement refers only to the SafeWatch-Bench train/test subsets. Without video-level deduplication, hashes, or a stated exclusion procedure, the Table 3 numbers may reflect memorization rather than generalization.
[Abstract, Section 4.1, Table 20] The abstract and Section 4.1 claim SafeWatch-Bench contains over 2M videos, but Table 20 reports a total of 199,604 training videos plus 1,420 testing videos, i.e., 201,024 videos. The sum of the per-subcategory counts in Table 21 is roughly 1.44M, and many of those counts are over multi-label categories, so it does not reconcile the discrepancy either. The paper should either correct the 2M claim or clearly define what unit (videos, clips, annotated events, or multi-label instances) is being counted.
[Section 5.2, Table 4, Appendix B.5, Table 21] The 'new policy categories' used to claim zero-shot generalizability are not convincingly unseen. Section 5.2 says the three categories are 'absent from SafeWatch-Bench,' but Table 21 includes a 20K 'Shooting' subcategory in C3 and a 500-sample 'Child abuse' subcategory in C2. The firearms policy is a close variant of the training Shooting content, and the children's-safety policy is semantically close to the Child abuse subcategory. Consequently, the 5.6% improvement in Table 4 is not a clean measure of generalization to foreign taxonomies; the authors should either use genuinely disjoint categories or reframe the claim as near-distribution adaptation.
[Section 3.3, Eq. (5)] Equation (5) defines r_i^j = Q_{\pi_i} K_{v_j} / (sum_{k} Q_{\pi_k} K_{v_j}), normalizing over policies for a fixed video token. Standard cross-attention weights for a policy query over video keys normalize over the video-token index. As written, r_i^j is not a probability over video tokens and is not interpretable as the 'cross-attention score between each policy chunk and each video token' stated in the text. This matters because the PAP module selects Top-K tokens on the basis of r_i^j. Please clarify whether a softmax is applied and over which index the normalization is performed; otherwise the pruning criterion is not well defined.

minor comments (4)

[Section 5.1, Table 3] The abbreviation 'UCF' is not defined at first use; it should be UCF-Crime, and the random-split protocol should specify whether official splits or new random splits were used and how label imbalance was handled.
[Section 3.1, Eq. (1)] The notation S(v) is used in Eq. (1) before the safety-aware event sampler is introduced two paragraphs later; please define the sampler symbol at its first occurrence.
[Figure 10, Appendix B.5] The caption of Figure 10 notes 'no overlap' for SafeWatch-Bench; the main text should state explicitly whether the five existing benchmark test sets are also disjoint from the training sources listed in Table 21, since this is essential for interpreting Table 3.
[Appendix B.7] The human evaluation is described only by a rubric reference and a sentence in Section 5.1; please report the number of human raters, inter-annotator agreement, and sampling procedure, as the explanation-quality scores in Tables 1 and 13 are central to the transparency claims.

Circularity Check

2 steps flagged · score 6.0 of 10

External-benchmark gains and 'unseen' firearms results are measured on training-source datasets/categories, so the headline generalization claims partially reduce to fitted inputs.

fitted input called prediction [Section 5.1 (Datasets) and Table 21 in Appendix B.5]
""To be consistent with previous works, we also evaluate these models on a random split of five existing datasets, i.e., LSPD ..., XD-Violence ..., UCF ..., Fake-SV ..., FVC ..." (Sec. 5.1). Table 21 lists the same corpora as training sources: C1: 'LSPD'; C2: 'XD-violence'; C3: 'XD-violence'; C4: 'Fake-video-corpus ... Fake short video dataset'."

The model is fine-tuned on SafeWatch-Bench-Real, whose sources include LSPD, XD-Violence, Fake-video-corpus, and Fake short video dataset (Table 21). Table 3 then reports accuracy on a 'random split' of LSPD, XD-Violence, UCF, FakeSV, and FVC. The paper never states that these external test videos were excluded from training or deduplicated; Figure 10's no-overlap statement applies only to SafeWatch-Bench train/test. Therefore the 13.6% existing-benchmark gain can be obtained by memorizing training-source videos and does not establish transfer. The 'prediction' on existing benchmarks is a fitted input unless disjointness is demonstrated.
fitted input called prediction [Section 5.1 (Generalization to New Policy Categories) and Table 21]
""To assess their generalizability to new policy categories, we further evaluate three unseen tasks during training, including children's safety (MoB dataset), firearms, road accidents (samples collected ourselves)." Table 21, however, lists 'Shooting 20K' under C3 and 'War and military actions 10K' under C5 in the training distribution."

The paper labels firearms as an unseen policy and reports a 5.6% zero-shot generalization gain, but the training corpus already contains a Shooting subcategory (C3) and war/military actions (C5), and the unseen firearms policy blocks 'Content depicting firearms, such as guns, pistols, rifles, military equipment'. Consequently the firearms evaluation is a close variant of training content, so the claimed zero-shot transfer on this task is not independent of the fitted data.

full rationale

The internal SafeWatch-Bench evaluation is not circular by itself: the paper explicitly states that the benchmark train/test sets are disjoint, so a model trained on SafeWatch-Bench and evaluated on a held-out portion of the same benchmark is a legitimate, if self-constructed, benchmark. The circularity concerns attach to the external-generalization claims. Section 5.1 evaluates on 'a random split of five existing datasets' (LSPD, XD-Violence, UCF, FakeSV, FVC), while Table 21 shows that LSPD, XD-Violence, Fake-video-corpus, and Fake short video dataset are among the data sources used to build the SafeWatch-Bench-Real training set. No exclusion or deduplication of the external test videos from training is stated anywhere in the paper, so the headline 13.6% improvement on existing benchmarks is not shown to be a transfer result; it may instead reflect fitting to the same datasets. Similarly, the paper calls firearms an 'unseen' policy while the training data already contains a Shooting subcategory (20K clips) and war/military actions (10K clips), making the reported zero-shot gain on firearms partially a measure of near-training-distribution performance. The paper also contains an internal dataset-size inconsistency: the abstract claims 'over 2M videos' while Table 20 reports 199,604 training videos and 1,420 testing videos (201,024 total); this is a credibility concern but not itself circularity. Explanation-quality evaluation uses GPT-4o as judge, the same model family used in the annotation pipeline, which is a confound for the explanation-quality claim but is secondary to the train/eval overlap issue. Because the two most prominent external-generalization claims reduce, absent evidence of disjointness, to evaluations on training-source data, the paper is partially circular in its evidence chain.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central evaluation rests on a self-built benchmark whose labels come from an LLM pipeline, on an unstated disjointness between training sources and existing benchmark test sets, and on two empirical domain assumptions about frame sampling and token sparsity. The pruning ratio K is a hand-chosen operating point rather than a derived quantity.

free parameters (2)

PAP pruning ratio K = default inferred as PR-40% from Table 6
K controls how many video tokens survive pruning; the paper reports PR-20/40/95/99 without deriving a default, and the SafeWatch row matches the PR-40 GFLOP count, indicating a hand-chosen operating point that trades accuracy against cost.
frames per safety event = 1 frame per event
Section 3.1 states 'sampling one frame per event is sufficient' based on empirical observation, without an ablation across numbers of frames per event.

assumptions (4)

domain assumption Unsafe behavior is consistent within events, so one frame per detected event captures the violation.
Section 3.1 uses this to justify the frame sampler; a violation occurring only between sampled frames would be missed.
domain assumption Safety-relevant video content is sparse, so pruning visual tokens by cross-attention relevance preserves accuracy.
Section 3.3 bases PAP on this sparsity claim; the paper verifies it only on its own benchmark in Table 6, not on independently labeled data.
domain assumption Multi-agent MLLM consensus annotations with human spot checks are ground truth for SafeWatch-Bench labels.
Section 4.2 treats the pipeline's labels as correct; only a subset is human-verified and no inter-annotator agreement is reported.
ad hoc to paper Equivalent RoPE across policy chunks removes positional bias from the full attention output.
Appendix A.2.2 sets RoPE(pi)=RoPE(pj) to claim A is independent of Z, but Equation 4 also contains query and video key terms, so independence of the final outputs from policy order is asserted rather than fully demonstrated.

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Cite this review

Pith. "Pith review of SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations." pith.science (2026). https://pith.science/paper/N4TABZ4C

@misc{pith2026241206878,
  author       = {Pith},
  title        = {Pith review of: SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N4TABZ4C}},
  note         = {Machine review of arXiv:2412.06878}
}

read the original abstract

With the rise of generative AI and rapid growth of high-quality video generation, video guardrails have become more crucial than ever to ensure safety and security across platforms. Current video guardrails, however, are either overly simplistic, relying on pure classification models trained on simple policies with limited unsafe categories, which lack detailed explanations, or prompting multimodal large language models (MLLMs) with long safety guidelines, which are inefficient and impractical for guardrailing real-world content. To bridge this gap, we propose SafeWatch, an efficient MLLM-based video guardrail model designed to follow customized safety policies and provide multi-label video guardrail outputs with content-specific explanations in a zero-shot manner. In particular, unlike traditional MLLM-based guardrails that encode all safety policies autoregressively, causing inefficiency and bias, SafeWatch uniquely encodes each policy chunk in parallel and eliminates their position bias such that all policies are attended simultaneously with equal importance. In addition, to improve efficiency and accuracy, SafeWatch incorporates a policy-aware visual token pruning algorithm that adaptively selects the most relevant video tokens for each policy, discarding noisy or irrelevant information. This allows for more focused, policy-compliant guardrail with significantly reduced computational overhead. Considering the limitations of existing video guardrail benchmarks, we propose SafeWatch-Bench, a large-scale video guardrail benchmark comprising over 2M videos spanning six safety categories which covers over 30 tasks to ensure a comprehensive coverage of all potential safety scenarios. SafeWatch outperforms SOTA by 28.2% on SafeWatch-Bench, 13.6% on benchmarks, cuts costs by 10%, and delivers top-tier explanations validated by LLM and human reviews.

Figures

Figures reproduced from arXiv: 2412.06878 by the authors.

**Figure 1.** An overview of SAFEWATCH. During data curation (top), we annotate each video in SAFEWATCH-BENCH with high-quality multi-label guardrail and explanation via a multi-agent propose-discuss consensus pipeline, i.e., we guide multiple MLLMs to iteratively improve their annotation for each video frame by reaching consensus with each other. During training (bottom-left), SAFEWATCH distills knowledge from SAFEWATCH-BENCH v… view at source ↗

**Figure 2.** SAFEWATCH-BENCH dataset, with 2M videos in total, covers six comprehensive safety categories, where each is further divided into multiple fine-grained risk subcategories to address a wide range of safety scenarios. Notably, SAFEWATCH-BENCH is split into the Real and GenAI subsets, which contain the challenging videos produced in real-world scenarios (left-side), and generative videos produced by SOTA GenAI models (… view at source ↗

**Figure 3.** The decoding pipeline of SAFEWATCH. Regarding video input (left), SAFEWATCH leverages a segmentation model to process the input video into clips based on unsafe events. Then, it samples frames from each event and encodes them into patch tokens. Regarding safety guidelines (right), SAFEWATCH encodes each policy in parallel with the equivalent RoPE embedding to ensure they are treated with equal importance. Then, for… view at source ↗

Figures from the paper (17 more)

**Figure 4.** Figure 4: Comparison of SAFEWATCH and GPT-4o across fine-grained scenarios in SAFEWATCHBENCH. We evaluate the average accuracy per subcategory. Hard Benign refers to challenging benign samples that previous models often misclassify as harmful, resulting in high false positives …

**Figure 5.** Figure 5: Comparing the performance and inference cost of S [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: Assessment of the policy attention score of S [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]

**Figure 7.** Figure 7: Assessment of the correlation between the attention score of each unsafe video category [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]

**Figure 8.** Figure 8: Detailed comparison across different guardrail models on the accuracy of each subcategory [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]

**Figure 9.** Figure 9: Assessing the quality of explanations evaluated by GPT-4o across six subcategories. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

**Figure 10.** Figure 10: The distribution of video samples in each category in the training set ( [PITH_FULL_IMAGE:figures/full_fig_p025_10.png]

**Figure 11.** Figure 11: The distribution of video length (seconds) in the training set ( [PITH_FULL_IMAGE:figures/full_fig_p025_11.png]

**Figure 12.** Figure 12: The distribution of explanation length (word count) in the training set ( [PITH_FULL_IMAGE:figures/full_fig_p026_12.png]

**Figure 13.** Figure 13: The distribution of the number of events derived by the safety-aware event sampler in [PITH_FULL_IMAGE:figures/full_fig_p026_13.png]

**Figure 14.** Figure 14: The demographic distribution of the collected videos categorized by [PITH_FULL_IMAGE:figures/full_fig_p027_14.png]

**Figure 15.** Figure 15: A case comparison of the annotation of previous existing datasets and S [PITH_FULL_IMAGE:figures/full_fig_p028_15.png]

**Figure 16.** Figure 16: A case comparison of the annotation of previous existing datasets and S [PITH_FULL_IMAGE:figures/full_fig_p029_16.png]

**Figure 17.** Figure 17: A case study of the multi-agent propose-discuss consensus pipeline applied to a video clip depicting the intentional mistreatment of an elderly person in a wheelchair, categorized as elder abuse. While the initial proposal agent misidentifies the object in the video, …

**Figure 18.** Figure 18: A case study that demonstrates three examples from S [PITH_FULL_IMAGE:figures/full_fig_p031_18.png]

**Figure 19.** Figure 19: A case study that demonstrates three examples from S [PITH_FULL_IMAGE:figures/full_fig_p032_19.png]

**Figure 20.** Figure 20: Comparison of the unsafe generative videos of S [PITH_FULL_IMAGE:figures/full_fig_p033_20.png]

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Reference graph

Works this paper leans on

22 extracted references · 19 canonical work pages · cited by 4 Pith papers

[2]

C1(Sexual Content)

Analyze the video for the presence of the following harmful con- tent categories. <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bullying; C3: Threats, Violence & Harm; C4: False & Deceptive Information; C5: Ille- gal/Regulated Activities; C6: Hateful Content & Extremism) <END HARMFUL CATEGORIES...

work page
[3]

Provide reasoning for your decision, referencing specific safety poli- cies and feedback from the discussion. Here are the safety policies: <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bullying; C3: Threats, Violence & Harm; C4: False & Deceptive Information; C5: Ille- gal/Regulated Activities...

work page
[4]

• Include detailed feedback explaining why the consensus was not reached and how the proposal can be improved in the next iteration

If consensus is not reached: • Provide a revised guardrail proposal in JSON format. • Include detailed feedback explaining why the consensus was not reached and how the proposal can be improved in the next iteration. Here are the safety policies: <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bu...

work page
[5]

Table 6: We study the individual contribution of each module and different pruning ratios (PR) on the overall performance of S AFE WATCH

Similarly, we provide a detailed break-down comparison of S AFE WATCH and GPT4-o on each subcategory and new policy categories at test time. Table 6: We study the individual contribution of each module and different pruning ratios (PR) on the overall performance of S AFE WATCH. We demonstrate the average guardrail accuracy and explanation rating evaluated...

work page 2023
[7]

Summarize the content of the video, beginning with ”DESCRIP- TION:”

work page
[8]

C1(Sexual Content)

Analyze the video for the presence of the following harmful con- tent categories. <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bullying; C3: Threats, Violence & Harm; C4: False & Deceptive Information; C5: Ille- gal/Regulated Activities; C6: Hateful Content & Extremism) <END HARMFUL CATEGORIES...

work page
[9]

Begin with ”DESCRIP- TION:”

Summarize the content of the video. Begin with ”DESCRIP- TION:”

work page
[10]

C1(Sexual Content)

Analyze the video for the presence of harmful content based on the following categories: <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bullying; C3: Threats, Violence & Harm; C4: False & Deceptive Information; C5: Ille- gal/Regulated Activities; C6: Hateful Content & Extremism) <END HARMFUL CAT...

work page

Show all 22 references

[11]

Review the initial proposal, discussion, and any judge model feed- back provided
[12]

If you oppose, suggest an updated guardrail label in JSON format

Decide whether to support or oppose the proposed guardrail label. If you oppose, suggest an updated guardrail label in JSON format
[14]

Review the guardrail proposal for this video clip and the following discus- sions among several agents
[15]

Determine whether a consensus has been reached by a majority of the agents
[16]

If consensus is reached, then summarize the guardrail annotation for this event from the proposal and the discussion history
[18]

• Provide the final guardrail annotations in the same JSON format, begin- ning with ”GUARDRAIL:”

If consensus is reached: • Concisely summarize the video content, beginning with ”DESCRIP- TION:”. • Provide the final guardrail annotations in the same JSON format, begin- ning with ”GUARDRAIL:”. • Summarize the reasoning behind the guardrail annotations, beginning with ”EXPL...
[19]

Your feedback should begin with ”FEEDBACK:” and be clear and actionable

If consensus is not reached: • Include detailed feedback explaining why consensus was not reached and what improvements are needed in the next iteration. Your feedback should begin with ”FEEDBACK:” and be clear and actionable. • Provide a revised description of the video, begi...
[20]

Accuracy: Does the model’s explanation focus on the most critical violation in the video?
[21]

Policy Compliance: Does the model’s explanation align well with the specific policies provided?
[22]

Content Specificity: Is the model’s explanation directly related to the video content? Does it avoid hallucinations or irrelevant informa- tion?
[23]

Conciseness: Is the explanation concise while effectively conveying the key points? Please analyze the model’s output step by step and provide a rating on a scale of 1 to 10 based on these criteria, where 1 is poor and 10 is excellent. The output should contain one sentence of...

2023
[40]

URL https://aclanthology.org/2023.emnlp-demo.40. Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean- baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al. Gem- ini 1.5: Unlocking multimodal understan...

2023 arXiv
[2022]

AI @ Meta Llama Team

doi: 10.1017/XPS.2020.37. AI @ Meta Llama Team. The llama 3 herd of models, 2024. URL https://arxiv.org/abs/ 2407.21783. Hui Lv and Qianru Sun. Video anomaly detection and explanation via large language models.arXiv preprint arXiv:2401.05702, 2024. Hui Lv, Chuanwei Zhou, Zhen ...

2020 arXiv
[2024]

Haomiao Ni, Changhao Shi, Kai Li, Sharon X Huang, and Martin Renqiang Min

URL https://openreview.net/forum?id=gmg7t8b4s0. Haomiao Ni, Changhao Shi, Kai Li, Sharon X Huang, and Martin Renqiang Min. Conditional image-to-video generation with latent flow diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognit...

2023 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [2]

C1(Sexual Content)

Analyze the video for the presence of the following harmful con- tent categories. <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bullying; C3: Threats, Violence & Harm; C4: False & Deceptive Information; C5: Ille- gal/Regulated Activities; C6: Hateful Content & Extremism) <END HARMFUL CATEGORIES...

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[2] [3]

Provide reasoning for your decision, referencing specific safety poli- cies and feedback from the discussion. Here are the safety policies: <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bullying; C3: Threats, Violence & Harm; C4: False & Deceptive Information; C5: Ille- gal/Regulated Activities...

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[3] [4]

• Include detailed feedback explaining why the consensus was not reached and how the proposal can be improved in the next iteration

If consensus is not reached: • Provide a revised guardrail proposal in JSON format. • Include detailed feedback explaining why the consensus was not reached and how the proposal can be improved in the next iteration. Here are the safety policies: <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bu...

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[4] [5]

Table 6: We study the individual contribution of each module and different pruning ratios (PR) on the overall performance of S AFE WATCH

Similarly, we provide a detailed break-down comparison of S AFE WATCH and GPT4-o on each subcategory and new policy categories at test time. Table 6: We study the individual contribution of each module and different pruning ratios (PR) on the overall performance of S AFE WATCH. We demonstrate the average guardrail accuracy and explanation rating evaluated...

work page 2023

[5] [7]

Summarize the content of the video, beginning with ”DESCRIP- TION:”

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[6] [8]

C1(Sexual Content)

Analyze the video for the presence of the following harmful con- tent categories. <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bullying; C3: Threats, Violence & Harm; C4: False & Deceptive Information; C5: Ille- gal/Regulated Activities; C6: Hateful Content & Extremism) <END HARMFUL CATEGORIES...

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[7] [9]

Begin with ”DESCRIP- TION:”

Summarize the content of the video. Begin with ”DESCRIP- TION:”

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[8] [10]

C1(Sexual Content)

Analyze the video for the presence of harmful content based on the following categories: <BEGIN HARMFUL CATEGORIES> {A list of policy guidelines} (For example: C1: Sexual Content; C2: Harassment & Bullying; C3: Threats, Violence & Harm; C4: False & Deceptive Information; C5: Ille- gal/Regulated Activities; C6: Hateful Content & Extremism) <END HARMFUL CAT...

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