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arxiv: 2605.26244 · v1 · pith:BR6I2WQNnew · submitted 2026-05-25 · 💻 cs.CV · cs.MM· cs.SD

LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV

Tengfei Liu , Yang Shi , Xuanyu Zhu , Jiafu Tang , Liu Yang , Qixun Wang , Zhuoran Zhang , Yuqi Tang
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Fengxiang Wang Yuhao Dong Xinlong Chen Bozhou Li Bohan Zeng Yue Ding Xiaohan Zhang Jialu Chen Haotian Wang Yuanxing Zhang Pengfei Wan Leye Wang
This is my paper

Pith reviewed 2026-06-29 22:50 UTC · model grok-4.3

classification 💻 cs.CV cs.MMcs.SD
keywords audio-visualgenerationevaluationlongav-compassacrossbenchmarkunifiedalignment
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The pith

LongAV-Compass supplies 284 test cases and a multi-metric framework to diagnose coherence and alignment failures in minute-scale audio-visual generation across text, image, and video conditioning.

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

Existing benchmarks for audio-visual generation cover only short 5-10 second clips and rarely handle unified evaluation across text, image, and video inputs. LongAV-Compass fills the gap with a collection of 284 curated minute-long test cases organized by scenario and complexity for T2AV, I2AV, and V2AV tasks. The benchmark applies a unified evaluation framework that combines MLLM-assisted scoring with perceptual metrics including DINO-v2, ArcFace, CLIP, and ImageBind to track more than 20 dimensions such as within-segment quality, cross-segment consistency, narrative coherence, semantic alignment, and audio-visual synchronization. Experiments across 11 representative models plus human-alignment checks establish the benchmark as a diagnostic tool for identifying where current systems lose temporal consistency and semantic fidelity over extended durations.

Core claim

LongAV-Compass is a benchmark of 284 curated test cases spanning text-to-audio-video, image-to-audio-video, and video-to-audio-video generation, built through taxonomy-guided construction and evaluated via an integrated framework of MLLM-assisted assessment together with complementary metrics such as DINO-v2, ArcFace, CLIP, and ImageBind; the framework measures fine-grained dimensions of within-segment quality, cross-segment consistency, global narrative coherence, semantic alignment, and audio-visual synchronization, and experiments on eleven models with human validation demonstrate its utility in revealing limitations of current systems for sustaining coherent minute-scale outputs.

What carries the argument

LongAV-Compass benchmark, whose taxonomy-guided test construction and unified evaluation framework integrate MLLM-assisted assessment with perceptual and multimodal metrics (DINO-v2, ArcFace, CLIP, ImageBind) to score over twenty dimensions of quality, consistency, and alignment.

If this is right

  • Current models show measurable drops in identity consistency and narrative coherence as generation length reaches one minute.
  • A single framework can now compare performance across three conditioning modalities on the same set of long-form criteria.
  • The multi-metric approach isolates specific failure modes such as cross-segment drift and synchronization loss.
  • Human validation confirms that the automated scores track human judgments on the targeted dimensions of quality and alignment.

Where Pith is reading between the lines

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

  • Model developers could target the identified failure modes by retraining on subsets of the benchmark that isolate particular consistency dimensions.
  • The benchmark construction method could be extended to longer sequences or new modalities to test whether the observed degradation patterns continue or saturate.
  • Integration into training loops might allow direct optimization against the fine-grained scores rather than short-clip proxies.
  • The diagnostic results could guide selection of conditioning modality for specific application scenarios that require sustained narrative.
  • keywords:[

Load-bearing premise

The 284 curated test cases together with the chosen MLLM-assisted metrics and perceptual measures accurately capture degradation in identity consistency, narrative coherence, and audio-visual alignment over minute-scale durations.

What would settle it

Running the eleven models on the 284 cases and finding that high automated scores on all metrics correspond to low human ratings for overall coherence and alignment would falsify the benchmark's measurement validity.

Figures

Figures reproduced from arXiv: 2605.26244 by Bohan Zeng, Bozhou Li, Fengxiang Wang, Haotian Wang, Jiafu Tang, Jialu Chen, Leye Wang, Liu Yang, Pengfei Wan, Qixun Wang, Tengfei Liu, Xiaohan Zhang, Xinlong Chen, Xuanyu Zhu, Yang Shi, Yuanxing Zhang, Yue Ding, Yuhao Dong, Yuqi Tang, Zhuoran Zhang.

Figure 1
Figure 1. Figure 1: Overview of LongAV-Compass. The benchmark unifies T2AV, I2AV, and V2AV under shared taxonomy, event-level annotation, and a hierarchical evaluation framework, enabling diagnosis of long-range audio-visual failures beyond flat leaderboard comparison. ble scene transitions, and the decay of audio-visual synchro￾nization as duration increases. As summarized in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Scenario and difficulty distribution in LongAV￾Compass. The benchmark spans four application scenarios and multiple complexity levels (L1–L4), supporting analysis by both content domain and generation difficulty. 3. LongAV-Compass 3.1. Task Formulation As shown in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Data construction pipeline of LongAV-Compass. LongAV-Compass builds its benchmark data for three task types: T2AV, I2AV, and V2AV. T2AV and I2AV cases are obtained through two complementary routes: scenario- template-based LLM generation and real-video-based transcription or adaptation. V2AV cases are constructed from real videos by extracting reference clips and generating continuation scripts. After task… view at source ↗
Figure 5
Figure 5. Figure 5: Scenario-level balanced scores on I2AV task. For each scenario, each bar reports the mean balanced score of one model over all available samples in that scenario. 4.3. Main Results Tables 3–5 show that current long-form audio-visual genera￾tion systems cannot be adequately characterized by a single overall score. Strong performance requires the joint ability to execute annotated events, maintain long-range… view at source ↗
Figure 6
Figure 6. Figure 6: Case study of event-aligned evaluation in LongAV-Compass. Using a Brand Ads case as an example, the upper row decomposes the generated video into ordered events and boundary clips for transition-stability assessment. The middle row illustrates event-level QA for measuring event fulfillment, and the bottom row summarizes full-video quality signals, including holistic presentation, video quality, and text-/i… view at source ↗
Figure 8
Figure 8. Figure 8: Capability profiles of open-source models. Scores are min-max normalized per metric across the displayed models to highlight relative capability differences. performance degradation is primarily driven by drops in event fulfillment (VQA) and long-form continuity (Cont.). This trend is especially pronounced for open-source models, whose declines in these two dimensions are substantially larger than in other… view at source ↗
Figure 9
Figure 9. Figure 9: Event-count analysis. Samples are grouped into short event chains (≤4 events) and longer event chains (>4 events), and each bar reports the average balanced score for one model family [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Human-alignment validation. Each point denotes one model, with proprietary models shown as circles and open-source models shown as triangles. The three panels compare human￾derived and benchmark-derived pairwise win rates for content fi￾delity, visual quality, and long-video stability. rent systems are not failing to understand product-oriented prompts at the semantic level; rather, they struggle to reli￾… view at source ↗
Figure 11
Figure 11. Figure 11: Prompt template for T2AV real-video transcription. [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompt template for T2AV LLM-template generation. [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt template for I2AV image prior extraction. [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt template for V2AV continuation construction. [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Dataset statistics of LongAV-Compass. (a) Sample count per task with language distribution below. (b) Category distribution across tasks. (c) Complexity level distribution (L1–L4) per task. (d) Events per sample (bar = mean, error bar = min/max range). (e) Shots per sample. Identity Tracking. • subject 1: Xiao-Lin, confident skincare expert in light casual clothing. • subject 2: Xiao-Ya, gentle personalit… view at source ↗
Figure 16
Figure 16. Figure 16: Keyframe visualization of the T2AV Performance Ads case (L4). The filmstrip shows representative frames from each event, [PITH_FULL_IMAGE:figures/full_fig_p018_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Keyframe visualization of the V2AV Content-Creator case (L4). The continuation begins after the reference video, showing the [PITH_FULL_IMAGE:figures/full_fig_p019_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Keyframe visualization of the I2AV Performance Ads case (L4). Starting from the reference product image (leftmost frame), the [PITH_FULL_IMAGE:figures/full_fig_p019_18.png] view at source ↗
read the original abstract

Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to short-form settings. Existing benchmarks primarily focus on 5--10 second text-conditioned generation and rarely support unified evaluation across text, image, and video conditioning modalities. Moreover, they provide limited insight into how identity consistency, narrative coherence, and audio-visual alignment degrade over extended temporal horizons. To bridge this gap, we introduce LongAV-Compass, a systematic benchmark for minute-long audio-visual generation. LongAV-Compass contains 284 curated test cases spanning text-to-audio-video (T2AV), image-to-audio-video (I2AV), and video-to-audio-video (V2AV), organized by application scenario and generation complexity. The benchmark combines taxonomy-guided benchmark construction with a unified evaluation framework that integrates MLLM-assisted assessment with complementary perceptual and multimodal metrics, including DINO-v2, ArcFace, CLIP, and ImageBind. The framework evaluates more than 20 fine-grained dimensions covering within-segment quality, cross-segment consistency, global narrative coherence, semantic alignment, and audio-visual synchronization. Through experiments on 11 representative models together with human-alignment validation, LongAV-Compass provides a diagnostic testbed for analyzing the limitations of current systems in sustaining coherent, semantically aligned, and temporally consistent minute-scale audio-visual generation across diverse input modalities.

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 / 2 minor

Summary. The manuscript introduces LongAV-Compass, a benchmark for minute-scale audio-visual generation containing 284 curated test cases spanning T2AV, I2AV, and V2AV modalities. Cases are organized by application scenario and generation complexity using a taxonomy-guided approach. The unified evaluation framework integrates MLLM-assisted assessment with perceptual and multimodal metrics (DINO-v2, ArcFace, CLIP, ImageBind) to score more than 20 fine-grained dimensions covering within-segment quality, cross-segment consistency, global narrative coherence, semantic alignment, and audio-visual synchronization. Experiments on 11 representative models are paired with human-alignment validation to position the benchmark as a diagnostic testbed for limitations in sustaining coherent, semantically aligned, and temporally consistent minute-scale generation across input modalities.

Significance. If the curation, metrics, and human validation hold, the work addresses a genuine gap in existing short-clip benchmarks by enabling analysis of temporal degradation in identity consistency, narrative coherence, and audio-visual alignment. The multi-modality coverage and fine-grained dimensions could serve as a practical reference for diagnosing model weaknesses in long-form generation, with the human-alignment step providing a useful check on automated metrics.

major comments (2)
  1. [Abstract / Experiments] Abstract and Experiments section: The central claim that LongAV-Compass supplies a usable diagnostic testbed rests on experiments with 11 models and human validation, yet the manuscript provides no quantitative results, per-model scores, degradation trends, or error analysis. Without these, the diagnostic utility remains asserted rather than demonstrated.
  2. [Benchmark Construction] Benchmark Construction section: The 284 test cases are described as taxonomy-guided and spanning 20+ dimensions, but the manuscript supplies no explicit selection criteria, inter-annotator agreement statistics, or ablation showing that the chosen cases reliably surface degradation in identity consistency and narrative coherence over minute-scale horizons.
minor comments (2)
  1. [Benchmark Construction] A table or figure summarizing the distribution of the 284 cases across modalities (T2AV/I2AV/V2AV) and complexity levels would improve readability of the benchmark composition.
  2. [Evaluation Framework] The description of how MLLM-assisted scores are aggregated with DINO-v2/ArcFace/CLIP/ImageBind metrics would benefit from an explicit formula or pseudocode in the Evaluation Framework section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to strengthen the empirical demonstration and construction transparency.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments section: The central claim that LongAV-Compass supplies a usable diagnostic testbed rests on experiments with 11 models and human validation, yet the manuscript provides no quantitative results, per-model scores, degradation trends, or error analysis. Without these, the diagnostic utility remains asserted rather than demonstrated.

    Authors: We agree that the current manuscript version does not present the quantitative results, per-model scores, degradation trends, or error analysis. In the revised version we will add these elements from the experiments on the 11 models together with the human-alignment validation results, thereby demonstrating rather than asserting the benchmark's diagnostic value. revision: yes

  2. Referee: [Benchmark Construction] Benchmark Construction section: The 284 test cases are described as taxonomy-guided and spanning 20+ dimensions, but the manuscript supplies no explicit selection criteria, inter-annotator agreement statistics, or ablation showing that the chosen cases reliably surface degradation in identity consistency and narrative coherence over minute-scale horizons.

    Authors: We acknowledge the absence of these details. The revised Benchmark Construction section will include explicit selection criteria, inter-annotator agreement statistics, and an ablation study confirming that the 284 cases surface the targeted degradations in identity consistency and narrative coherence. revision: yes

Circularity Check

0 steps flagged

No significant circularity in benchmark construction

full rationale

The paper presents LongAV-Compass as a benchmark with 284 test cases, taxonomy-guided construction, MLLM-assisted assessment, and external metrics (DINO-v2, ArcFace, CLIP, ImageBind) evaluated on 11 models plus human validation. No derivations, equations, fitted parameters, predictions, or self-citation chains appear in the provided text. The central claim reduces to standard benchmark practice using independent external components, with no load-bearing step that reduces to its own inputs by construction. This matches the default expectation for non-circular benchmark papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the domain assumption that MLLM-assisted scoring combined with standard perceptual metrics can serve as a valid proxy for human judgment on long-form coherence and alignment; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption MLLM-assisted assessment combined with metrics such as DINO-v2, ArcFace, CLIP, and ImageBind reliably measures fine-grained dimensions including cross-segment consistency and audio-visual synchronization
    This is invoked as the core of the unified evaluation framework in the abstract.

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discussion (0)

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    and shot counts (avg 16.5–17.3), while V2A V is more I2A V Image Prior Extraction Prompt Analyze this image and produce a structured prior in JSON: { "image summary": "<one-sentence summary>", "detailed visual description": "<full visual analysis>", "subjects": [...], "key objects": [...], "environment and setting": "<scene description>", "composition and...

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    (subject) Are two women and a skincare product visi- ble?

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    (interaction) Does one woman hand the product to the other?

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    water-burst

    (scene) Is the setting a bright indoor window area? Generation Challenges.This case tests: (1) two-person interaction with distinct roles, (2) product close-up with texture demonstration (“water-burst” effect), (3) facial ex- pression transitions, and (4) brand packaging consistency across shots. C.2. V2A V Case: Content-Creator Short Film Con- tinuation ...

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    (0–9s) Camera establishes the desk scene; watch receives a notification

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    (9–20s) A hand enters frame, picks up the watch, puts it on wrist

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    (20–35s) Close-up: watch face shows fitness rings filling; demonstrates health tracking

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    (35–48s) Quick-cut montage of different watch faces and complications

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    After the reference video, the scene continues with: {event2.visual description}; {event3.visual description}

    (48–60s) Return to desk scene; brand logo and CTA ap- pear. Generation Challenges.This case tests: (1) precise preser- vation of the reference image’s visual style and objects, (2) natural transition from still product shot to dynamic video, (3) fine-grained UI animation on the watch screen, and (4) return to the original composition for the ending. C.4. ...

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    Videos are saved as full video.mp4 under each model’s output directory

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    Event-aligned clips are extracted based on canonical event boundaries fromcanonical events.json

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    Boundary clips (2 s before and after each event boundary) are extracted for transition evaluation

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    For audio evaluation, audio-bearing event clips are re- extracted from full video.mp4 to ensure audio con- tinuity