InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows
Bibliographic record
Abstract
Understanding long-form videos, such as movies and TV episodes ranging from tens of minutes to two hours, remains a major challenge for multi-modal models. Existing benchmarks often fall short in testing the full range of cognitive skills needed to process these temporally rich and narratively complex inputs. We introduce InfiniBench, a comprehensive benchmark designed to rigorously evaluate the capabilities of models in long video understanding. InfiniBench offers: (1) Over 1,000 hours of video content, with an average video length of 52.59 minutes, (2) The largest set of question-answer pairs for long video comprehension, totaling around \totalSampleNumber, (3) Eight diverse skills that span both grounding-based (e.g., scene transitions, character actions) and reasoning-based (e.g., deep context, multi-event linking) understanding, and (4) Rich annotation formats, including both multiple-choice and open-ended questions. We conduct an in-depth evaluation across both commercial (GPT-4o, Gemini 1.5 Flash) and open-source (Qwen2.5-VL, InternVL2.5) vision-language models. Results reveal that current models remain far from solving long video understanding: on grounding-based skills, the top open-source model (Qwen2.5-VL) and GPT-4o achieve only 39.4% and 48.1% accuracy, respectively. Interestingly, several models achieve non-trivial performance using only the movie or episode title, without watching the video, revealing a reliance on pre-trained world knowledge that partially compensates for the absence of visual or temporal understanding. These findings highlight critical gaps in current approaches and underscore the need for models that truly engage with long visual narratives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".