cs.CVJun 6, 2025
SaveHow Well Do Vision-Language Models Understand Movies?
Organizations: Instituto Superior Técnico, Universidade de Lisboa · Instituto de Telecomunicações · ILLC, University of Amsterdam · Language Technology Lab, University of Amsterdam ·
Abstract
Vision-language models (VLMs) can increasingly handle extended contexts and answer questions about long videos--but can they understand the narrative structure of movies as humans do? To investigate this question, we introduce MF (Movie Facts and Fibs), a fully manually curated benchmark designed to evaluate whether models can identify, recall, and comprehend information central to the movie's storyline, using both the visual and textual modalities. MF includes over 50 open-licensed movies, each paired with manually constructed sets of claim pairs: one true claim (fact) and one plausible but false (fib), totaling over 850 pairs. Our analysis across 20 open-source and 2 proprietary models reveals a substantial gap relative to human performance. While subtitles provide a strong signal for narrative understanding, other textual sources (e.g., synopses) are less informative. In contrast, when visual information is distributed across the entire movie and jointly embedded with subtitles, models struggle to identify and effectively use the relevant content. However, when the visual input is restricted to claim-relevant visual content, models improve significantly, indicating substantial room for improvement in processing large multimodal contexts and distinguishing relevant from irrelevant information. Overall, our findings highlight persistent challenges in multimodal narrative understanding for current VLMs. We release our data and code to support further research in this direction.
Figures & tables
Figure 1: Illustration of two claim pairs (each with a fact and a fib ) from the movie “The Little Princess” (1939). Our claims target memorable events, focusing on key points of the narrative such as emotional arcs and causal relationships between characters, and require reasoning across different granularities ( single-scene and multi-scene/global ).
Figure 2: Dataset construction process involving three main stages: movie collection, data annotation, and quality control.
| Method | #Params | Pairwise Accuracy (%) | Accuracy (%) | ||||
| Subs | Video | Video+Subs | Subs | Video | Video+Subs | ||
| Baselines | |||||||
| Random | - | 25.0 | 25.0 | 25.0 | 50.0 | 50.0 | 50.0 |
| Human | - | – | – | 84.1 | – | – | 90.5 |
| Closed Models | |||||||
| GPT-4o | - | 48.8 | 19.5 | 49.8 | 72.9 | 55.5 | 73.2 |
Table 1: Performance (%) of open-weight and closed models on MF 2 under different input modalities. Best values overall are bolded ; best per group are underlined . Asterisk (*) denotes models that failed to follow the instruction prompt in a substantial fraction of samples and their results are therefore not reported. Numbers in parentheses indicate the number of active parameters.
| Model | Video-only | Video+Subs | ||
| (full) | (targeted) | (full) | (targeted) | |
| Gemini 2.5 Pro | 38.6 | 49.4 (+10.8) | 59.4 | 74.0 (+14.6) |
| Gemma3-27B | 27.2 | 36.9 (+9.7) | 42.7 | 49.5 (+6.8) |
| Qwen3VL-30B | 12.6 | 22.3 (+9.7) | 40.8 | 52.4 (+11.6) |
| InternVL3.5-38B | 29.1 | 41.7 (+12.6) | 51.5 | 53.4 (+1.9) |
| Qwen2.5VL-72B | 34.0 | 29.1 (-4.9) | 46.6 | 53.4 (+6.8) |
Table 2: Pairwise accuracy when models are provided with “targeted” frames as visual input versus frames sampled uniformly from the entire movie. “Targeted” frames refer to frames extracted from the annotated time intervals that contain the visual content relevant to each claim. Results are reported on a single-scene claim subset under video-only and video with subtitles settings. Values in parentheses indicate the performance difference relative to the full-movie setting. Positive and negative deltas denote performance gains and drops, respectively.
| Input Modality | |||||
| Metric | Video | Subs | Video+Subs | Synopsis | Movie Title |
| Pairwise Acc. | 37.9 | 58.9 | 63.8 | 27.9 | 45.4 |
Table 3: Performance of Gemini 2.5 Pro across different input modalities. Video uses only the visual stream; Subs includes only subtitle information; Synopsis relies only on the synopsis of the movie obtained from Wikipedia; Video+Subs combines both visual and subtitles inputs; and Movie Title uses only the claim, along with the movie title and release year, without access to movie content.
Figure 3: Pairwise accuracy for Gemini 2.5 Pro per comprehension dimension and reasoning granularity when varying the input modalities.
Appendix figures & tables29 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 4: Number of claim pairs per movie.
| Comprehension Dimension | Definition |
| Event/Entity Understanding | Involves identifying key entities (e.g., people, places, or objects) and understanding the events they participate in. This includes tracking entities across scenes, interpreting their roles, and recognizing their interactions and relationships throughout the narrative. |
| Temporal Perception | Requires reasoning about the timeline of events—determining whether actions occur before, after, or simultaneously—and may also include counting or sequencing events. The focus is on broader temporal relationships within the narrative. |
| Emotion Understanding | Involves recognizing the emotional states of characters and interpreting how these emotions evolve throughout the story. |
| Causal Reasoning | Focuses on identifying cause-and-effect relationships between events or actions, including both explicit and implicit dependencies that may span multiple scenes. |
Table 4: Definitions of comprehension dimensions.
| Model | Pairwise Acc | Average Acc | Acc on Facts | Acc on Fibs |
| Video | ||||
| Gemini 2.5 Pro | 37.9 | 64.4 | 47.4 | 81.5 |
| Gemma3-27B | 31.5 | 61.2 | 57.4 | 65.1 |
| Qwen3VL-IT-30B | 19.2 | 56.9 | 24.3 | 89.5 |
| InternVL3.5-38B | 26.6 | 60.0 | 38.8 | 81.1 |
| Qwen2.5VL-72B | 29.7 | 58.8 | 45.4 | 72.1 |
Table 5: Reporting pairwise accuracy, average accuracy, accuracy on facts and accuracy on fibs for some open-weight models and Gemini2.5. Results highlight that simple accuracy on its own can be misleading.
| Model | Movie Title | Synopsis | Subs | Video | Video+Subs |
| Gemini 2.5 Pro | 45.4 | 27.9 | 58.9 | 37.9 | 63.8 |
| Gemma3-27B | 28.1 | 29.6 | 42.3 | 31.5 | 39.7 |
| Qwen3VL-IT-30B | 12.9 | 26.6 | 33.5 | 19.2 | 43.8 |
| InternVL3.5-38B | 27.0 | 31.8 | 47.5 | 26.6 | 46.2 |
| Qwen2.5-VL-72B | 15.2 | 25.7 | 37.6 | 29.7 | 47.5 |
| LLaVA-Video-72B | 17.1 | 38.7 | 41.7 | 15.6 | 41.8 |
Table 6: Extended version of Table 3 reporting pairwise performance for Gemini 2.5 Pro and mid- to large-scale open-weight models across different input modalities. Video uses only the visual stream; Subs includes only subtitle information; Synopsis relies only on the synopsis of the movie obtained from Wikipedia; Video+Subs combines both visual and subtitles inputs; and Movie Title uses only the claim, along with the movie title and release year, without access to movie content.
Figure 5: Pairwise accuracy for large-scale models with video and subtitles, and human baseline per comprehension dimension and reasoning granularity.
| Model | #Params | #Frames | HF Checkpoint |
| Closed Models | |||
| GPT-4o ( OpenAI et al., 2024 ) | - | 50 | – |
| Gemini 2.5 Pro ( Team et al., 2023 ) | - | 120 | – |
| Open-weight Models | |||
| mPLUG-Owl3* ( Ye et al., 2025a ) | 7B | – | mPLUG/mPLUG-Owl3-7B-240728 |
| VideoLLaMA3 ( Zhang et al., 2025 ) | 7B | 180 | DAMO-NLP-SG/VideoLLaMA3-7B |
Table 7: Overview of the evaluated vision-language models, including parameter scale, number of input frames, and hf checkpoint.
Figure 6: Guidelines provided for the data annotation procedure (Part 1).
Figure 7: Guidelines provided for the data annotation procedure (Part 2).
Figure 8: Guidelines provided for the data annotation procedure (Part 3). This part of the guidelines provides examples given to annotators to illustrate how to assign reasoning granularity labels. While more examples were shared during the annotation process, we include a selection here for illustrative purposes.
Figure 9: Guidelines provided for the data annotation procedure (Part 4). This part of the guidelines provides examples given to annotators to illustrate how to assign comprehension dimension labels. While more examples were shared during the annotation process, we include a selection here for illustrative purposes.
Figure 10: Guidelines provided for human evaluation (Part 1).
Figure 11: Guidelines provided for human evaluation (Part 2).
Figure 12: Direct prompt template used for open-weight models.
Figure 13: Explanation prompt template used for closed models.
| Movie (Year) | Genre (IMDB) | Lang. | Duration |
| The Last Chance (1945) | Drama, War | en, it | 93.84 |
| They Made Me a Criminal (1939) | Boxing, Film Noir, Crime, Drama, Sport | en | 91.21 |
| Tokyo After Dark (1959) | Drama | en | 81.23 |
| The Sadist (1963) | Horror, Thriller | en | 91.63 |
| Suddenly (1954) | Film Noir, Psychological Thriller, Crime, Drama, Thriller | en | 76.71 |
| Sabotage (Hitchcock) (1936) | Psychological Thriller, Spy, Crime, Thriller | en | 75.92 |
Table 8: Metadata of collected movies (Part 1).
| Movie (Year) | Genre (IMDB) | Lang. | Duration |
| The Emperor Jones (1933) | Drama, Music | en | 76.29 |
| The Deadly Companions (1961) | Adventure, Drama, Western | en | 93.62 |
| The Red House (1947) | Film Noir, Drama, Mystery, Thriller | en | 100.39 |
| Trapped (1949) | Film Noir, Crime, Drama, Thriller | en | 79.4 |
| City of Fear (1959) | Crime, Drama, Thriller | en | 75.18 |
| Kid Monk Baroni (1952) | Action, Drama, Sport | en | 79.56 |
Table 9: Metadata of collected movies (Part 2).
| Example 1 (Part 1) - Movie: "Suddenly" |
| Pidge brings the water Setting up the circuit Pop spills the water The electrocution |
| Fact ( ✓ ): The grandfather, Peter Benson, intentionally spilled the water on the floor. |
| Fib ( ✗ ): The grandfather, Peter Benson, accidentally spilled the water on the floor. |
| Granularity : Multi-scene/Global |
Table 10: Illustrative Example 1 from the movie “Suddenly” . Gemini fails to predict both claims correctly in every modality setting.
| Example 1 (Part 2) - Movie: "Suddenly" |
| Video only setting |
| Prediction on Fact : The grandfather, Peter Benson, is seen at 2:17 reaching for a glass of water. He then appears to deliberately drop the glass, causing the water to spill on the floor. This creates a distraction, allowing the woman to crawl towards a gun. The context of the scene, where the family is being held hostage and looking for an opportunity to fight back, combined with the grandfather’s actions and the subsequent events, strongly suggests the spill was intentional. TRUE (Ground-truth: TRUE) |
| Prediction on Fib : The video introduces an older character who is later identified as Peter Benson (around 0:46-0:47). Much later in the video, during a tense hostage situation, Peter Benson is seen sitting in an armchair holding a glass of water. At timestamp 2:18, he is startled and gets up abruptly, causing the water in his glass to spill onto the floor. The context of the scene, where he reacts to the unfolding events, suggests the spill was accidental due to his sudden movement or reaction. TRUE (Ground-truth: FALSE) |
Table 11: Illustrative Example 1 from the movie “Suddenly” . Gemini fails to predict both claims correctly in every modality setting.
| Example 2 (Part 1) - Movie: "Sabotage (Hitchcock)" |
| Aquarium Planning the sabotage Meet in cinema Kid enters the bus The time arrives Bus explosion People in the cinema Explosion in cinema |
| Fact ( ✓ ): The second bomb exploded in the cinema. |
| Fib ( ✗ ): There was a second bomb about to explode, but it didn’t. |
| Granularity : Global |
| Context ( Spoiler alert ! ) : Mr. Verloc and the terrorist meet at the aquarium, where the terrorist first mentions the plan to explode a bomb (he mentions “a parcel of fireworks” and asks Mr. Verloc to meet his friend the bombmaker). The terrorists meet again in the cinema, to discuss about the sabotage. Later in the movie the explosion happens inside the bus. While the second explosion happens inside the cinema. Gemini fails to predict both claims correctly in every modality setting. |
Table 12: Illustrative Example 2 from “Sabotage (Hitchcock)” . Gemini predicts the pair of claims incorrectly in every modality setting.
| Example 2 (Part 2) - Movie: "Sabotage" |
| Prediction on Fib : The transcript describes two main bomb-related incidents. The first is the explosion that kills Stevie. The second occurs at the cinema where the old man (the bomb maker) threatens to “blow the place sky high.” The transcript later confirms an explosion there: “The roof’s gone. The whole of the back of the place - clean blown off.” This indicates the second bomb did explode. Therefore, the statement that a second bomb was “about to explode, but it didn’t” is false because it did explode. FALSE (Ground-truth: FALSE) |
| Video-only setting |
| Prediction on Fact : The video shows one clear explosion involving a bus on a street around the 1:46 mark. Following this, there are various scenes, including one where people are watching a cartoon in a cinema. However, there is no depiction or indication of a second bomb exploding, nor is there any explosion shown occurring inside the cinema. FALSE (Ground-truth: TRUE) |
Table 13: Illustrative Example 2 from “Sabotage (Hitchcock)” . Gemini predicts the pair of claims incorrectly in every modality setting.
| Example 2 (Part 3) - Movie: "Sabotage" |
| Video w/ subs setting |
| Prediction on Fact : The first act of sabotage was the power station blackout. The second planned act of sabotage, referred to as “a parcel of fireworks”, was to be left in the cloakroom at Piccadilly Circus underground station […] This parcel, carried by Stevie on a bus, explodes on the bus at 1:45. A newspaper headline later confirms “WEST END BOMB SENSATION. Fully-laden bus was blown up in Piccadilly this afternoon”. […] An explosion then occurs at the cinema, as described by an officer: “The roof’s gone. The whole of the back of the place - clean blown off”. This is a separate, third bomb event. Therefore, the second bomb (the one Stevie was carrying) exploded on the bus, not in the cinema. The explosion in the cinema was a distinct event caused by a different bomb wielded by the bird shop owner. FALSE (Ground-truth: TRUE) |
| Prediction on Fib : The statement claims: “There was a second bomb about to explode, but it didn’t.” Let’s analyze the bombs in the film: 1. The first act of sabotage is the power station blackout. While not explicitly shown as a bomb explosion, it is an act of “wilful destruction.” 2. The second major plot device is the bomb intended for Piccadilly Circus on Lord Mayor’s Show day. Verloc gives this bomb to Stevie. This bomb explodes prematurely on the bus, killing Stevie and many others […]. So this bomb did explode. 3. The third instance involving a bomb is at the cinema, where the Professor (the bomb maker) is cornered by the police. [..] This bomb does explode […]. In all instances where a bomb is primed or ready for its purpose within the narrative, it either explodes (bus bomb, Professor’s bomb in the cinema) or fulfills its sabotage (power station). There is no scene in the film where a bomb is “about to explode” and then “didn’t.” The bombs that are set up to go off, do go off. Therefore, the statement is false. FALSE (Ground-truth: FALSE) |
Table 14: Illustrative Example 2 from “Sabotage (Hitchcock)” . Gemini predicts the pair of claims incorrectly in every modality setting.
| Example 3 (Part 1) - Movie: "Scandal Sheet" |
| News about the killer Sales increasing.. Sales increasing.. |
| Fact ( ✓ ): The sales of the newspaper increased after it was revealed that the editor was the killer. |
| Fib ( ✗ ): The sales of the newspaper decreased after it was revealed that the editor was the killer. |
| Granularity : Single-scene |
Table 15: Illustrative Example 3 from “Scandal Sheet” . The pair of claims relies primarily on visual information. Gemini predicts them incorrectly when given only a single modality, but when provided with both the transcript and the video, it correctly identifies the pair of claims.
| Example 3 (Part 2) - Movie: "Scandal Sheet" |
| Video only setting |
| Prediction on Fact : The video shows a chart of the “NET PAID CIRCULATION NEW YORK EXPRESS” at various points. Initially, at 0:10, the circulation is shown with an upward trend. After the editor is revealed to be the killer and is subsequently shot (around 2:26-2:28), the final shot of the movie (2:29) shows a newspaper headline “KILLER!!” with the byline “By Steve McCleary” superimposed over the same circulation chart. At this point, the line on the chart indicating sales has reached its highest point, implying a significant increase in sales coinciding with or immediately following the revelation and capture of the killer editor. TRUE (Ground-truth: TRUE) |
| Prediction on Fib : The movie shows a chart of the “New York Express” net paid circulation at 0:10, indicating an upward trend in sales from October to September, reaching 650,000. Later, after the editor (the man standing by the chart at 0:10) is revealed as the killer and is shot (around 2:27), the final shot at 2:29 shows a newspaper headline “KILLER!!” superimposed over the same circulation chart. The numbers and the upward trend on this chart are identical to the one shown at 0:10. The movie does not provide any information or visual evidence suggesting that the newspaper’s sales decreased after the editor was revealed as the killer. The chart displayed at the end is the same one that showed increasing sales. TRUE (Ground-truth: FALSE) |
Table 16: Illustrative Example 3 from “Scandal Sheet” . The pair of claims relies primarily on visual information. Gemini predicts them incorrectly when given only a single modality, but when provided with both the transcript and the video, it correctly identifies the pair of claims.
| Example 4 (Part 1) - Movie: "Tight Spot" |
| Fact ( ✓ ): When Hallet shows Conley a photograph of an individual who was on the yacht, she immediately recognizes the person. |
| Fib ( ✗ ): When Hallet shows Conley a photograph of an individual who was on the yacht, she claims not to recognize the person. |
| Granularity : Single-scene |
| Context ( Spoiler alert ! ): This pair of claims to be answered correctly requires both the visual and text cues. Hallet shows Conley a photograph (visual information) and asks whether she recognizes the person (Senior Maracelli). Conley recognizes the person. When the model is given only the subtitles, it misclassifies both the fact and the fib (as visual cues are absent). In contrast, when provided with both the video and the subtitles, it predicts each one correctly. |
Table 17: Illustrative Example 4 from “Tight Spot” . Gemini fails to predict both claims correctly when given only the subtitles or only the visual cues. However, when provided with both modalities together, it correctly predicts both claims.
| Example 4 (Part 2) - Movie: "Tight Spot" |
| Video only setting |
| Prediction on Fact : The statement asks whether Conley immediately recognizes the person in the photograph shown to her by Hallet. In the video, at 00:30, Hallet takes out a photograph. At 00:31, he shows it to Conley. Conley looks down at the photo, then at 00:32, she looks up at Hallet with an expression that seems more contemplative or questioning rather than one of instant, clear recognition. She then glances back at the photo before looking at Hallet again and starting to speak at 00:33. Her reaction involves a pause and a sequence of looks, which does not convey immediate recognition. “Immediate” suggests an instantaneous, reflexive understanding, which is not evident in her more measured response. FALSE (Ground-truth: TRUE) |
| Prediction on Fib : The statement claims that when Hallet shows Conley a photograph of an individual from a yacht, Conley claims not to recognize the person. Observing the video, there is a scene from approximately 0:29 to 0:36 where a man (presumably Hallet, played by Edward G. Robinson) shows a photograph to a woman (presumably Conley, played by Ginger Rogers). The woman looks at the photograph (0:31-0:32). However, the video segment does not include any audible dialogue or clear visual cue (like shaking her head definitively or mouthing “I don’t know”) from the woman that would confirm she “claims not to recognize the person.” Her facial expression is somewhat pensive or surprised but not an explicit denial of recognition. She then proceeds to make a phone call (0:34). The context that the photograph is of “an individual who was on the yacht” is also not explicitly provided by the visual information in this clip. Since the crucial part of the statement - her claiming not to recognize the person - is not depicted or audible in the provided video evidence, the statement cannot be verified as true based on the available information. FALSE (Ground-truth: FALSE) |
Table 18: Illustrative Example 4 from “Tight Spot” . Gemini fails to predict both claims correctly when given only the subtitles or only the visual cues. However, when provided with both modalities together, it correctly predicts both claims.
| Example 5 (Part 1) - Movie: "Tight Spot" |
| Pete in taxi Walking the stairs of the church Pete being shot |
| Fact ( ✓ ): Pete Tinelli was shot. |
| Fib ( ✗ ): Pete Tinelli was poisoned. |
| Granularity : Multi-scene |
Table 19: Illustrative Example 5 from “Tight Spot” . Gemini fails to predict the pair of claims correctly, in every modality setting.
| Example 5 (Part 2) - Movie: "Tight Spot" |
| Video only setting |
| Prediction on Fact : A man is visibly shot around the 1:57-1:58 mark in the video. In this scene, a man is held by two others, another man (resembling Edward G. Robinson) nods, a gunshot sound is heard, and the man being held slumps. However, throughout the provided video clip, there is no dialogue, on-screen text, or any other information that identifies the man who was shot as “Pete Tinelli.” […] FALSE (Ground-truth: TRUE) |
| Prediction on Fib : The video contains several scenes that lead to the conclusion that Pete Tinelli was poisoned. At 1:58, a group of men are shown in what appears to be a garage, and one man is forcibly made to drink something. He subsequently collapses at 2:01. Later, at 2:05, a woman asks, “What happened to Pete Tinelli?” and is told, “He won’t bother you anymore.” Shortly after, at 2:08, one man informs another, “Pete Tinelli’s been taken care of.” When asked “How?”, the response is “Poison.” This sequence of events and dialogue directly confirms that Pete Tinelli was poisoned. TRUE (Ground-truth: FALSE) |
Table 20: Illustrative Example 5 from “Tight Spot” . Gemini fails to predict the pair of claims correctly, in every modality setting.
| Example 6 (Part 1) - Movie: “A Bucket of Blood” |
| Holding the cat sculpture Drops it The damage Moment of realization |
| Fact ( ✓ ): The gallery owner dropped the cat sculpture, causing minor damage. |
| Fib ( ✗ ): The gallery owner dropped the cat sculpture, but it remained intact. |
| Granularity : Single-scene |
Table 21: Illustrative Example 6 from the movie: “A Bucket of Blood” . Gemini fails to predict the pair of claims correctly, in every modality setting.
| Example 6 (Part 2) - Movie: “A Bucket of Blood” |
| Video-only setting |
| Prediction on Fact : The statement is: “The gallery owner dropped the cat sculpture, causing minor damage.” To verify this, we need to identify a “gallery owner,” a “cat sculpture,” an act of “dropping” by the gallery owner, and resulting “minor damage.” 1. Gallery Owner: * Leonard de Santis is the owner of "The Yellow Dog" cafe, which also functions as an art gallery where poetry is read and art is displayed […] Walter Paisley creates a sculpture of a cat by encasing his landlady’s dead cat in clay (0:16-0:20). This is the most prominent cat sculpture. * At 0:29, Leonard and Carla are seen holding other, more stylized white cat sculptures, indicating these might be common art pieces in their circle. 3. Dropped the cat sculpture, causing minor damage: * At 0:29, Leonard (the cafe/gallery owner) is shown holding one of the stylized white cat sculptures. He does not drop it. […] No scene in the provided video shows a character identifiable as "the gallery owner" dropping a cat sculpture and causing minor damage. The closest event involves a police officer tapping Walter’s cat sculpture, causing a tiny chip. Therefore, the statement is false. FALSE |
| Prediction on Fib : The statement says: “The gallery owner dropped the cat sculpture, but it remained intact.” Let’s break this down: 1. Identity of the “gallery owner”: In the movie clip, the gallery owner is Maxwell Brock, often seen wearing a beret or a suit, and he runs “The Yellow Door” gallery. 2. Did the gallery owner drop a cat sculpture?: * At 0:27, the main character, Walter Paisley (a busboy who becomes an artist), is carrying a large abstract sculpture (not a cat) when he is bumped by Carla. Walter drops this sculpture, and it shatters. Walter is not the gallery owner. * Walter Paisley creates a sculpture of a cat (by encasing his landlady’s dead cat in clay) around 0:22. This sculpture is later handled but not dropped by the gallery owner. * At 0:50, Maxwell Brock (the gallery owner) is seen handling a different white, stylized cat sculpture. He picks it up, examines it carefully, and places it back down. He does not drop this sculpture. Since there is no instance in the provided video where the gallery owner (Maxwell Brock) drops any cat sculpture, the first part of the statement ("The gallery owner dropped the cat sculpture") is false. Therefore, the entire statement is false. FALSE |
Table 22: Illustrative Example 6 from the movie: “A Bucket of Blood” . Gemini fails to predict the pair of claims correctly, in every modality setting.
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