---
格式版本: 2
标题: "Order Matters: LVLMs as Judges for Temporal Reasoning in Image Sequences"
原文链接: "https://arxiv.org/abs/2608.10908"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T11:16:29+08:00"
入库时间: "2026-08-12T03:21:55.391Z"
来源平台: "arXiv 学术论文搜索"
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AI优质: "否"
AI打分: 3
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采集批次: "2026年8月12日2点04分20秒"
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去重键: "https://arxiv.org/abs/2608.10908"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:Order Matters: LVLMs as Judges for Temporal Reasoning in Image Sequences

[View PDF](https://arxiv.org/pdf/2608.10908) [HTML (experimental)](https://arxiv.org/html/2608.10908v1)

> Abstract:As generative multimedia evolves from static image synthesis to complex, interleaved visual narratives, a foundational bottleneck has emerged: the judgment crisis. While human perception naturally synthesizes the temporal and logical flow of a story, automated evaluation systems remain largely "blind" to sequential continuity, often failing to distinguish between a coherent narrative and a semantically shuffled or contradictory sequence. This work identifies a critical structural gap in current multimodal evaluation paradigms, arguing that the reliance on Large Vision-Language Models (LVLMs) as judges is fundamentally limited by architectural biases. Our analysis reveals a profound performance dichotomy: while models may appear competent in isolated pointwise scoring, they suffer a catastrophic collapse when required to perform pairwise discrimination of temporal order. We demonstrate that this is not merely a data-scarcity issue but a structural one. Through a series of diagnostic probes, we uncover systematic positional asymmetries, specifically primacy and recency effects, where a model's judgment of a story is significantly influenced by the placement of a frame, often more than by its semantic consistency. These biases, potentially rooted in causal masking and rotary embeddings, suggest that current transformer-based judges are inherently ill-equipped for long-form visual reasoning. By exposing these blind spots, we challenge the multimedia community to move beyond snapshot-centric metrics and instead pioneer Temporally-Aware Evaluation paradigms that treat visual sequences as unified logical structures rather than unordered collections of frames.

| Comments: |  |
| --- | --- |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| ACM classes: | I.4.9; I.2.7; I.2.4; H.5.1 |
| Cite as: | [arXiv:2608.10908](https://arxiv.org/abs/2608.10908) \[cs.CV\] |
|  | (or [arXiv:2608.10908v1](https://arxiv.org/abs/2608.10908v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10908](https://doi.org/10.48550/arXiv.2608.10908) |

## Submission history

From: Martina Ianaro \[[view email](https://arxiv.org/show-email/2a336be8/2608.10908)\]  
**\[v1\]** Tue, 11 Aug 2026 13:29:03 UTC (35,369 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10908) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
