Pop-Up Distractions Reveal Bag-of-Events Behavior in Video Large Language Models

Dishant Zaveri; Khoa D. Doan; Kuan-Hao Huang; Oscar Chew; Patricia Lu; Qian-Hui Chen; Serhii Honcharenko

arxiv: 2605.27101 · v1 · pith:WWDXHIOLnew · submitted 2026-05-26 · 💻 cs.CV · cs.CL

Pop-Up Distractions Reveal Bag-of-Events Behavior in Video Large Language Models

Oscar Chew , Serhii Honcharenko , Qian-Hui Chen , Patricia Lu , Dishant Zaveri , Khoa D. Doan , Kuan-Hao Huang This is my paper

Pith reviewed 2026-06-29 18:12 UTC · model grok-4.3

classification 💻 cs.CV cs.CL

keywords video large language modelsbag-of-eventstemporal groundinghallucinationvideo understandingDistractionBenchsubject-event association

0 comments

The pith

Video large language models process videos as unordered collections of events rather than temporally structured sequences.

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

The paper introduces DistractionBench, which inserts unrelated advertisement clips into videos to test whether VideoLLMs maintain correct associations between subjects and events over time. All eleven evaluated models frequently hallucinate by attributing actions from the inserted clips to subjects in the original video. This behavior is characterized as bag-of-events processing, where the model treats the input as a bag of independent events without regard to their order or context. A reader would care because many real-world video tasks require precise temporal linking, and this reveals a systematic limitation in current models.

Core claim

VideoLLMs exhibit bag-of-events behavior, processing videos as collections of events rather than temporally structured sequences, as evidenced by their tendency to hallucinate interactions between entities from inserted unrelated segments and the main video content.

What carries the argument

DistractionBench, a controlled intervention that inserts short unrelated advertisement clips into longer videos to expose failures in subject-event temporal association.

If this is right

All evaluated VideoLLMs lack reliable mechanisms for linking subjects to events across time.
Models will attribute actions from any disjoint segments to the primary video subjects.
Video understanding tasks involving interruptions or scene changes will trigger event mixing.
New training or architectural changes are required to enforce temporal subject-event associations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Real videos with natural scene cuts may trigger the same event-mixing errors observed with ads.
Next-token training alone may not penalize violations of temporal order strongly enough.
Extending the test to other distraction types could quantify how much temporal structure is missing.

Load-bearing premise

The inserted advertisement clips are sufficiently unrelated to the main video so that any cross-attribution indicates missing temporal grounding rather than other response artifacts.

What would settle it

A model that correctly describes only the main video events and never mixes in actions or entities from the inserted ad clips across repeated trials with varied placements.

Figures

Figures reproduced from arXiv: 2605.27101 by Dishant Zaveri, Khoa D. Doan, Kuan-Hao Huang, Oscar Chew, Patricia Lu, Qian-Hui Chen, Serhii Honcharenko.

**Figure 2.** Figure 2: Overview of the four subtasks in DistractionBench. We evaluate VideoLLMs using three question types: [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Error rates by temporal distance. BoE errors [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 5.** Figure 5: The structured prompt utilized for GPT-4o video attribute annotation. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Characteristics of NATURALADS. Left: Ad durations in seconds. Right: Relative ad positions; boe question: A question that intentionally mixes concepts from the main video and the advertisement segment. • This question should describe an event that does not actually occur in the video. • Expected answer: No irrelevant question: A modified version of the boe question, where the advertisementrelated concept … view at source ↗

**Figure 7.** Figure 7: Molmo2 4B: Error Rates by temporal distance [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

**Figure 8.** Figure 8: Qwen3.5 9B: Error Rates by temporal distance [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗

**Figure 9.** Figure 9: LLaVA-OneVision 7B: Error rates by number [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗

**Figure 10.** Figure 10: Molmo2-4B: Error rates by number of frames. 10 [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗

read the original abstract

A key capability for video understanding is reliably linking subjects to events across time, yet whether Video Large Language Models (VideoLLMs) actually achieve this remains unclear. In this work, we introduce DistractionBench to evaluate whether VideoLLMs can robustly link subjects and events in the presence of unrelated video segments. Through controlled interventions, such as inserting short advertisement clips into longer videos, we show that VideoLLMs frequently hallucinate interactions between entities from different segments, incorrectly attributing actions from injected advertisements to subjects in the main video. We characterize this systematic hallucination as bag-of-events (BoE) behavior, where models process videos as collections of events rather than temporally structured sequences. Evaluating 11 popular VideoLLMs, we find that all models exhibit substantial BoE behavior. Our findings suggest that VideoLLMs lack reliable mechanisms for temporal grounding and motivate the development of models with more robust subject-event association.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

VideoLLMs mix actions from inserted ads into main videos across all 11 models tested, but the jump to missing temporal grounding mechanisms rests on unshown controls.

read the letter

The core observation is that inserting short unrelated ad clips into longer videos causes every tested VideoLLM to attribute ad actions to main-video subjects. The authors label this bag-of-events behavior and present it as evidence that the models lack reliable temporal grounding.

The new element is DistractionBench itself. The controlled ad-insertion setup gives a direct probe for cross-segment mixing that prior work does not appear to have used in this form. Running the same test on 11 models and getting the same pattern supplies a clear empirical result that stands on its own.

The paper does well at documenting the scale of the problem. A uniform finding across current models makes the limitation concrete rather than anecdotal.

The soft spot is the causal interpretation. The stress-test point holds: the abstract supplies no details on how hallucinations were scored, what prompt variations were tried, or what controls ruled out generic multi-segment confusion or prompt sensitivity. Without those, the data show mixing occurs but do not yet isolate it to absent temporal mechanisms specifically. If the full paper adds those checks, the claim strengthens; otherwise the mechanism story stays provisional.

This is for groups building or auditing video LLMs who need concrete failure cases. A reader focused on temporal robustness benchmarks would get direct value from the intervention design.

It deserves peer review. The empirical pattern is worth referee scrutiny on methods and controls even if the deeper explanation requires tightening.

Referee Report

3 major / 2 minor

Summary. The paper introduces DistractionBench, which inserts short unrelated advertisement clips into longer videos to test whether VideoLLMs can maintain subject-event associations across temporal segments. It reports that all 11 evaluated VideoLLMs exhibit 'bag-of-events' (BoE) behavior by hallucinating interactions that incorrectly attribute actions from the ad clips to subjects in the main video, and concludes that this indicates a lack of reliable temporal-grounding mechanisms in current models.

Significance. If the empirical findings hold after appropriate controls, the work supplies a concrete benchmark for a previously under-tested failure mode in video understanding and supplies falsifiable evidence that current VideoLLMs treat input as unordered collections of events. The introduction of a controlled intervention benchmark is a constructive contribution to the evaluation of temporal reasoning in multimodal models.

major comments (3)

[Abstract and §4] Abstract and §4 (Experiments): The central claim that observed cross-segment hallucinations demonstrate absent temporal-grounding mechanisms (rather than prompt sensitivity, generic long-input hallucination, or failure to follow multi-segment instructions) is not secured, because the manuscript supplies no description of control conditions that would disambiguate these alternatives (e.g., text-only event lists, explicitly segmented prompts, or shuffled but temporally coherent video).
[Abstract] Abstract: The uniform finding that 'all models exhibit substantial BoE behavior' is stated without any reported quantification method, hallucination rate metric, number of test videos, or statistical measure, so the data-to-claim link cannot be evaluated from the provided text.
[§3] §3 (DistractionBench): The assumption that inserted advertisement clips are 'sufficiently unrelated' to the main video is load-bearing for the BoE interpretation, yet no quantitative measure of semantic or visual overlap between ad and main segments is supplied to support this premise.

minor comments (2)

[Introduction] Notation for 'bag-of-events' is introduced without a formal definition or comparison to related concepts such as bag-of-words models in NLP; a short clarifying paragraph would improve readability.
[§4] The list of 11 evaluated models should include version numbers, parameter counts, and exact prompting templates used, to support reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript accordingly to strengthen the claims and presentation.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Experiments): The central claim that observed cross-segment hallucinations demonstrate absent temporal-grounding mechanisms (rather than prompt sensitivity, generic long-input hallucination, or failure to follow multi-segment instructions) is not secured, because the manuscript supplies no description of control conditions that would disambiguate these alternatives (e.g., text-only event lists, explicitly segmented prompts, or shuffled but temporally coherent video).

Authors: We agree that explicit control conditions would help isolate temporal-grounding failures from other factors such as prompt sensitivity. Our core intervention uses inserted video distractions to probe cross-segment attribution in the native video setting. In revision we will add an explicitly segmented prompt control (clearly labeled segments without visual distractions) and discuss its results relative to the main condition. Text-only event lists are less directly comparable to the video temporal-grounding question but can be noted as a limitation. revision: yes
Referee: [Abstract] Abstract: The uniform finding that 'all models exhibit substantial BoE behavior' is stated without any reported quantification method, hallucination rate metric, number of test videos, or statistical measure, so the data-to-claim link cannot be evaluated from the provided text.

Authors: Section 4 defines the hallucination rate as the percentage of trials in which an action from an inserted ad is incorrectly attributed to a subject in the main video, reports results over 50 test videos, and includes mean rates plus standard deviation across the 11 models. We will move the key quantitative summary (e.g., average hallucination rate and range) into the abstract for immediate evaluability. revision: yes
Referee: [§3] §3 (DistractionBench): The assumption that inserted advertisement clips are 'sufficiently unrelated' to the main video is load-bearing for the BoE interpretation, yet no quantitative measure of semantic or visual overlap between ad and main segments is supplied to support this premise.

Authors: Clip selection was performed by manual review to ensure domain mismatch. In revision we will add quantitative support: average cosine similarity of CLIP text embeddings between main-video captions and ad captions, plus average frame-level visual feature similarity, confirming low overlap. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark with no derivations or fitted quantities

full rationale

The paper introduces DistractionBench as an empirical intervention (inserting unrelated ad clips) and reports observed hallucinations across 11 VideoLLMs, labeling the pattern 'bag-of-events (BoE) behavior.' No equations, parameters, or first-principles derivations appear; the central claim is an observational finding from new experiments rather than a reduction of any quantity to prior inputs by construction. Self-citations, if present, are not load-bearing for any mathematical step. The work is self-contained as a benchmark study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the empirical results from the new benchmark; the only notable addition is the descriptive label for the observed failure mode.

axioms (1)

domain assumption VideoLLMs are expected to reliably link subjects to events across time in video input.
Stated in the opening sentence of the abstract as the key capability under test.

invented entities (1)

bag-of-events (BoE) behavior no independent evidence
purpose: Descriptive label for the systematic hallucination in which models treat videos as unordered collections of events rather than temporally structured sequences.
New term introduced to characterize the observed failure mode; no independent evidence provided outside the benchmark results.

pith-pipeline@v0.9.1-grok · 5710 in / 1232 out tokens · 42878 ms · 2026-06-29T18:12:01.332325+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 2 canonical work pages · 1 internal anchor

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Qwen3-VL Technical Report

Qwen3-vl technical report.arXiv preprint arXiv:2511.21631. Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, and 1 others. 2025. Molmo and pixmo: Open weights and open data for state-of-the-art vision-language models. InProceedings of the Computer Vision an...

work page internal anchor Pith review Pith/arXiv arXiv 2025
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<answer>Yes</answer> or <an- swer>No</answer>

Longvideobench: A benchmark for long- context interleaved video-language understanding. InThe Thirty-eight Conference on Neural Informa- tion Processing Systems Datasets and Benchmarks Track. Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou. 2023. When and why vision-language models behave like bags-of-words, and what to ...

work page arXiv 2023
[3]

{clean_action}

dataset to retain videos with clear human actions. We deploy an off-the-shelf YOLOv8n (Jocher et al., 2023) object detection model across all video frames. A video is retained only if it con- sistently contains exactly one detected person. This filtering step ensures that the subsequent question- answering pairs can unambiguously target a sin- gle, distin...

2023

[1] [1]

Qwen3-VL Technical Report

Qwen3-vl technical report.arXiv preprint arXiv:2511.21631. Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, and 1 others. 2025. Molmo and pixmo: Open weights and open data for state-of-the-art vision-language models. InProceedings of the Computer Vision an...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[2] [2]

<answer>Yes</answer> or <an- swer>No</answer>

Longvideobench: A benchmark for long- context interleaved video-language understanding. InThe Thirty-eight Conference on Neural Informa- tion Processing Systems Datasets and Benchmarks Track. Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou. 2023. When and why vision-language models behave like bags-of-words, and what to ...

work page arXiv 2023

[3] [3]

{clean_action}

dataset to retain videos with clear human actions. We deploy an off-the-shelf YOLOv8n (Jocher et al., 2023) object detection model across all video frames. A video is retained only if it con- sistently contains exactly one detected person. This filtering step ensures that the subsequent question- answering pairs can unambiguously target a sin- gle, distin...

2023