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arxiv: 2605.22907 · v1 · pith:WLCEDYMSnew · submitted 2026-05-21 · 💻 cs.CV

VideoOdyssey: A Benchmark for Ultra-Long-Context and Omni-Modal Video Understanding

Haichen He , Jiayi Zhou , Sifeng Shang , Yihan Hu , Yuanhan Zhang , Kaiyang Zhou This is my paper

Pith reviewed 2026-05-25 05:50 UTC · model grok-4.3

classification 💻 cs.CV
keywords long video understandingbenchmarkmultimodal large language modelscontinuous reasoningultra-long contextomni-modal understandingfine-grained perception
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The pith

Bottlenecks in current MLLMs for long videos extend beyond retrieval to continuous reasoning, fine-grained perception, and non-verbal omni-modal understanding.

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

The paper introduces VideoOdyssey to test models on videos where questions demand watching long unbroken segments rather than isolated clips. It defines continuous certificate length as the key measure of required viewing time and builds two subsets covering visual-only and audio-visual cases across 11 domains with average video lengths of 109 minutes. Evaluations using five levels of certificate length from seconds to hours show that models fail at sustained integration of information, detailed observation, and handling non-verbal audio-visual cues. This setup aims to expose the real cognitive demands of real-world ultra-long video tasks. If accurate, it implies that simply extending context windows will not resolve the core limitations.

Core claim

The paper claims that VideoOdyssey, with its extreme durations and multi-level continuous certificates averaging 16 minutes for the visual subset and 12.8 minutes for the audio-visual subset, demonstrates that current MLLMs struggle with continuous reasoning across varying context lengths, fine-grained perception, and non-verbal omni-modal understanding rather than simple retrieval alone.

What carries the argument

continuous certificate length, the video length a human must continuously watch to definitively answer a question; it structures question design, subset creation, and diagnostic levels to force models into long temporal integration.

If this is right

  • Models must demonstrate integration of information over continuous spans of 12 to 16 minutes on average to succeed.
  • Performance varies systematically across five certificate levels, allowing targeted diagnosis of length-dependent failures.
  • Omni-modal models require explicit handling of synchronized non-verbal audio-visual cues beyond speech.
  • Progress depends on mechanisms for fine-grained perception sustained across long temporal contexts rather than retrieval alone.

Where Pith is reading between the lines

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

  • The leveled structure could support staged training where models first master short certificates before scaling to hour-long ones.
  • It points to a need for architectures with explicit long-term memory retention separate from context window size.
  • Real-world applications such as video surveillance or lecture analysis may require similar certificate-based evaluation to ensure reliability.
  • Extending the benchmark to interactive or multi-agent video scenarios could test whether the identified bottlenecks persist under dynamic conditions.

Load-bearing premise

The questions are written so that correct answers require watching the full continuous certificate length without being solvable through partial viewing, question phrasing, or video selection biases.

What would settle it

A model that answers the benchmark questions correctly after processing only short isolated segments, or human annotators who answer correctly without watching the full certificate length, would show the metric does not capture the intended cognitive load.

Figures

Figures reproduced from arXiv: 2605.22907 by Haichen He, Jiayi Zhou, Kaiyang Zhou, Sifeng Shang, Yihan Hu, Yuanhan Zhang.

Figure 1
Figure 1. Figure 1: Continuous certificate length across various video datasets. Based on this metric, we introduce VideoOdyssey, a pioneering benchmark specifically designed for ultra-long-context and omni-modal video under￾standing. VideoOdyssey features three key charac￾teristics: 1) Extreme video duration and domain diversity: We collected 100 ultra-long videos from public platforms, spanning 11 domains and 54 fine-graine… view at source ↗
Figure 2
Figure 2. Figure 2: Examples from our benchmark. In VideoOdyssey-V, the model needs to consistently attend to detailed visual cues across an ultra-long time span, performing OCR-based counting tasks. In VideoOdyssey-AV, the model needs to build a continuous logical chain of events over this massive time span, leveraging audio-visual cues to infer character relationships. ultra-short windows. Furthermore, our analysis shows th… view at source ↗
Figure 3
Figure 3. Figure 3: Statistics of VideoOdyssey. (a) VideoOdyssey contains 11 domains and 54 subcategories. (b) VideoOdyssey-V contains 1618 QA pairs across 14 tasks to assess model capability in four dimensions. (c) VideoOdyssey-AV contains 1062 QA pairs across 18 tasks to asses model performance in four dimensions. (d) All videos exceed 60 minutes, with the longest over 4 hours. (e) VideoOdyssey-AV features three audio types… view at source ↗
Figure 4
Figure 4. Figure 4: Performance of MLLMs across five continuous certificate length levels on [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Impact of certificate window (CW) on selected models across different continuous certificate [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Impact of different inputs for selected models across three audio types on [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: illustrates the balanced answer distribution for both VideoOdyssey-V and VideoOdyssey-AV [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performancewa across different video domains on [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Performancewa across different video domains on [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Performance of MLLMs under different input modalities on [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Human performance across continuous certificate length levels [PITH_FULL_IMAGE:figures/full_fig_p024_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Case of localization error. * indicates using the certificate window, whereas no asterisk [PITH_FULL_IMAGE:figures/full_fig_p026_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Cases of fain-grained perception error. * indicates using the certificate window, whereas [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Cases of long-context reasoning error. * indicates using the certificate window, whereas [PITH_FULL_IMAGE:figures/full_fig_p027_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Case of cross-modal integration error. * indicates using the certificate window, whereas [PITH_FULL_IMAGE:figures/full_fig_p028_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Case of non-verbal audio perception error. * indicates using the certificate window, [PITH_FULL_IMAGE:figures/full_fig_p028_16.png] view at source ↗
read the original abstract

Real-world long video understanding requires models to perform continuous tracking, information integration and memory retention over massive temporal spans within extreme video durations. Mastering this intense cognitive load constitutes the fundamental bottleneck in long video understanding. While existing benchmarks have driven progress by scaling up video duration, their evaluation tasks often require comprehending only short and isolated video segments, falling short of capturing the challenge of ultra-long-context reasoning. To measure this cognitive load, we emphasize continuous certificate length, defined as the video length a human must continuously watch to definitively answer a given question. Driven by this metric, we introduce VideoOdyssey, a benchmark specifically designed for ultra-long-context and omni-modal video understanding. VideoOdyssey is characterized by three key features: 1) Extreme video duration and diversity: spanning 11 domains and 54 subcategories with an average video duration of 109 minutes; 2) Comprehensive evaluation scenarios: offering two subsets to address different research focuses, i.e., VideoOdyssey-V for probing the limits of visual understanding in MLLMs, and VideoOdyssey-AV for evaluating synchronized audio-visual understanding for omni-modal models; 3) Ultra-long and multi-level continuous certificates: extending the average continuous certificate to 16 minutes for VideoOdyssey-V and 12.8 minutes for VideoOdyssey-AV. Crucially, we design 5 granular levels from seconds to hours, providing a comprehensive diagnostic tool to evaluate models across varying context lengths and cognitive loads. Extensive evaluations show that bottlenecks of current MLLMs extend beyond simple retrieval to include struggles with continuous reasoning across varying context lengths, fine-grained perception, and non-verbal omni-modal understanding.

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.

Referee Report

2 major / 1 minor

Summary. The paper introduces VideoOdyssey, a benchmark for ultra-long-context and omni-modal video understanding. It features videos averaging 109 minutes across 11 domains and 54 subcategories, two subsets (VideoOdyssey-V and VideoOdyssey-AV), and defines 'continuous certificate length' (avg. 16 min for V, 12.8 min for AV) with five granular levels from seconds to hours. Evaluations on MLLMs are reported to show that bottlenecks extend beyond retrieval to continuous reasoning across context lengths, fine-grained perception, and non-verbal omni-modal understanding.

Significance. If the continuous certificate length metric is validated and the evaluations are methodologically sound, the benchmark could provide a useful diagnostic for long-video MLLM limitations that existing shorter-segment benchmarks do not capture, potentially guiding targeted improvements in memory and integration capabilities.

major comments (2)
  1. [Abstract] Abstract (definition of continuous certificate length): the central claim that performance drops across context lengths isolate 'continuous reasoning' load depends on the untested premise that questions require the full specified duration; no ablation, human study, or truncation experiment is described showing that shorter segments render questions unanswerable.
  2. [Evaluation section] Evaluation/results section: the abstract states that 'extensive evaluations show' specific bottlenecks, yet the manuscript supplies no task examples, data statistics, question construction details, or validation procedures for the certificate lengths, preventing assessment of whether the empirical findings support the conclusions.
minor comments (1)
  1. [Abstract] The abstract lacks any numerical data statistics (e.g., total questions, distribution across levels) or example questions, which would improve clarity even if present in later sections.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below with clarifications on our methodology while committing to revisions that improve transparency without altering the core claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract (definition of continuous certificate length): the central claim that performance drops across context lengths isolate 'continuous reasoning' load depends on the untested premise that questions require the full specified duration; no ablation, human study, or truncation experiment is described showing that shorter segments render questions unanswerable.

    Authors: Continuous certificate length is established via a human annotation protocol in which annotators determine the shortest continuous video segment that allows definitive question resolution; this process directly encodes the requirement that shorter segments are insufficient. We acknowledge that the initial manuscript did not include explicit truncation ablations or additional human studies beyond the annotation itself. We will expand the methods section with a fuller description of the annotation guidelines and consider adding supporting truncation analyses where feasible. revision: partial

  2. Referee: [Evaluation section] Evaluation/results section: the abstract states that 'extensive evaluations show' specific bottlenecks, yet the manuscript supplies no task examples, data statistics, question construction details, or validation procedures for the certificate lengths, preventing assessment of whether the empirical findings support the conclusions.

    Authors: The manuscript contains sections on benchmark construction that report video statistics, domain coverage, and the multi-level certificate design, along with question generation guidelines. To address the concern about accessibility and completeness, we will insert concrete task examples, additional summary statistics, and an explicit subsection detailing the certificate-length validation steps directly into the evaluation section. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark metric is explicitly defined without reduction to inputs or self-citations

full rationale

The paper is a benchmark introduction with no equations, derivations, fitted parameters, or predictions. The continuous certificate length is defined directly as 'the video length a human must continuously watch to definitively answer a given question' and used to curate questions; this is a design choice, not a self-referential loop or fitted input renamed as output. No self-citation chains or uniqueness theorems are invoked as load-bearing. The evaluation is purely empirical on the new dataset, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper introduces a benchmark without mathematical derivations, fitted parameters, or new postulated entities; it rests on the domain assumption that video questions can be engineered to demand sustained temporal integration.

axioms (1)
  • domain assumption Video understanding tasks can be constructed such that answering requires continuous attention to long temporal spans rather than isolated segments.
    This premise underpins the continuous certificate length metric and the claim that current benchmarks fall short.

pith-pipeline@v0.9.0 · 5858 in / 1278 out tokens · 26712 ms · 2026-05-25T05:50:10.362082+00:00 · methodology

discussion (0)

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Reference graph

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    What did you say?

    When evaluating this model using the ground-truth certificate window, the sampling strategy is as follows: • Window length < 128 seconds: We densely extract frames at a rate of 1fps to preserve maximum temporal granularity. • Window length ≥ 128 seconds: We uniformly sample 128 frames across the entire duration of the window to provide a comprehensive ove...

    2000