CollabVR introduces a closed-loop framework for collaborative video reasoning that pairs Vision-Language Models (VLMs) with Video Generation Models (VGMs) at step-level granularity.
论文原始摘要
Recent "Thinking with Video" approaches use Video Generation Models (VGMs) for visual reasoning by producing temporally coherent Chain-of-Frames as reasoning artifacts. Even strong VGMs, however, exhibit two recurring failure modes on goal-directed tasks: long-horizon drift on multi-step tasks and mid-clip simulation errors that compound. Both stem from the absence of explicit reasoning built upon the VGM's short-horizon visual prior, a role naturally filled by Vision-Language Models (VLMs), but where to place the VLM is non-trivial: upfront plans commit before any frame is generated and post-hoc critiques over whole videos intervene too late. We propose VLM-VGM Collaborative Video Reasoning (CollabVR), a closed-loop framework that couples the VLM with the VGM at step-level granularity: the VLM plans the immediate next action, inspects the clip the VGM generates, and folds the verifier's diagnosis directly into the next action prompt to repair detected failures. On Gen-ViRe and VBVR-Bench, CollabVR improves both open-source and closed-source VGMs over single-inference, Pass@$k$, and prior test-time scaling baselines at matched compute, with the largest gains on the hardest tasks. It also yields further improvements on top of a reasoning-fine-tuned VGM, indicating that step-level VLM supervision is orthogonal to and stackable with reasoning-oriented fine-tuning. We provide video samples and additional qualitative results at our project page: https://joow0n-kim.github.io/collabvr-project-page.
Paper Collector 中文速览
VLM与VGM协同的闭环视频推理框架
方法概述
VLM规划下一步动作，VGM生成视频片段，VLM验证并诊断，将诊断反馈融入下一动作提示，形成步骤级闭环修复机制
核心贡献
提出CollabVR框架，通过VLM与VGM在步骤级闭环协作，解决长时漂移和累积错误问题
原始来源与核验范围
核验仅覆盖论文正文中的主张、证据片段和参考文献关系。NGJOO 未独立运行作者代码、重做实验或验证真实部署效果；页面中的可复现性分数是文档完整度评估，不是复现实验结果。
核心问题
How can Vision-Language Models and Video Generation Models be combined to overcome their complementary failure modes and improve collaborative video reasoning?
核心方法
CollabVR implements a closed-loop framework with two modules: VLM-Driven Progressive Planning that adaptively plans one action at a time based on previously generated frames, and VLM-VGM Collaborative Reasoning that verifies each generated clip and folds diagnosed failures back into revised prompts. The framework is evaluated on Gen-ViRe (72 samples, 6 categories) and VBVR-Bench benchmarks using open-source (VBVR-Wan2.2, Cosmos-Predict-2.5) and closed-source (Veo 3.1) video generation models against Single Inference, Pass@k, and VideoTPO baselines.
论点验证
The framework is fully specified in Algorithm 1, Figure 3, and Sections 3.2-3.3. It is implemented and extensively tested on two benchmarks (Gen-ViRe, VBVR-Bench) with multiple VGMs (VBVR-Wan2.2, Veo 3.1, Cosmos-Predict-2.5) and VLMs (Gemini 2.5 Pro,
Specific numerical results are provided in Table 1. VBVR-Wan2.2 improves from Pass@1 0.391 to 0.531 (+0.140), and Veo 3.1 improves from 0.481 to 0.550 (+0.069). These are concrete, quantitative findings with clear experimental setup.
Figure 2 shows the performance-cost tradeoff visually, demonstrating CollabVR reaches higher scores at lower budget. The paper reports Cost as VGM-generated seconds per sample. However, exact numerical cost values are not provided in the main text -
Table 2 shows VBVR-Wan2.2 with CollabVR achieves gains over baselines. The paper states VBVR-Wan2.2 is 'a VGM already fine-tuned on reasoning data' and shows CollabVR provides additional improvements, demonstrating orthogonality to fine-tuning.
Specific percentages from the user study are provided: CollabVR preferred over Pass@1 in 91.7% and over Pass@4 in 78.9% of decisive comparisons (excluding Equal votes which comprised 17.7% of trials). These are concrete numerical findings.
Table 4 shows Qwen3.5-9B + CollabVR scores (0.475 on Gen-ViRe, 0.560 on VBVR-Bench) which surpass Pass@k and VideoTPO baselines using Gemini 2.5 Pro. This is a specific, verifiable numerical comparison.
Figure 13 shows the sweep of per-step attempt budget M from 1 to 5. Specific gains are reported: +4.89% from M=1 to M=2, +1.59% from M=2 to M=3, +0.97% from M=3 to M=4, +0.51% from M=4 to M=5. These are concrete numerical findings from ablation.
Table 8 provides specific numerical results: Veo 3.1 with N=1 and M=2 reaches 0.527, compared to Pass@1 of 0.481, giving a +0.046 gain. This is a concrete finding from controlled ablation.
Table 8 shows the full M1+M2 configuration (max N=3, max M=2) reaches 0.550, compared to M2-only ceiling of 0.527, giving +0.023 improvement. This is a specific numerical finding.
Table 8 (last row) shows that with forced N=3 for every sample, Veo 3.1 drops to 0.450, compared to 0.550 with adaptive N selection. This is a specific numerical finding demonstrating the importance of adaptive N.
Specific inter-rater agreement metrics are provided: 66.3% average pairwise raw agreement (decisive-only) and Gwet's AC1 = 0.575. These are standard metrics for measuring annotator agreement.
The design choice is justified by comparison to pre-planning in Section 3.2. Figure 4b shows progressive planning yields better performance-cost trade-off than pre-planning. The ablation studies (M1 alone) validate the module's contribution. However,
The structured judgment interface is clearly defined in Section 3.3 and Algorithm 1 (line 6). The (v, d) structure with accept/reject and diagnosis is implemented and tested. Figure 10 shows a concrete example of this interface in action.
The choice of Gemini 2.5 Pro is justified empirically by Figure 9 showing it aligns most closely with human annotators on all three axes (plan depth, verification, evolution). Alternative VLMs are tested in ablation (Table 4).
... 共 38 个论点
可复现性评估
较低可复现性 (0%)
缺失的复现细节
- No code repository available - core implementation of CollabVR framework is not accessible
- No data repository available - Gen-ViRe and VBVR-Bench datasets not publicly accessible
- Missing random seeds for reproducibility of stochastic generation processes
- Missing hardware specifications (GPU types, memory, compute infrastructure)
- Missing software environment details (Python version, deep learning frameworks, library versions)
- Missing exact prompts/templates used for VLM planning and verification steps
- Missing temperature and sampling parameters for video generation models
- Missing detailed API configurations for closed-source models (Veo 3.1, Gemini 2.5 Pro) including API versions and rate limits
- Appendix A.1 referenced for full baseline configuration but not available in provided text
- Missing number of experimental runs and statistical significance measures (standard deviations, confidence intervals)
局限与证据边界
- Test-time orchestration cannot overcome a VGM that lacks the underlying capability: abstract or symbolic transformations stay hard because the generator never approximates them.
- Our gains diminish on lower-capability VGMs whose weak per-step instruction-following compounds errors across sub-clips faster than re-generation can repair.
- The verifier is also imperfect, allowing a fraction of failed clips to propagate downstream.
- Future directions include reasoning-oriented VGM training and finer-grained failure localization, which can be orthogonally integrated into our test-time loop.
- VLM detection failure: The verifier itself fails to identify the issue, so no recovery is triggered and the incorrect clip is finalized.
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分析时间：2026-05-16T13:18:56+00:00 · 数据来源：Paper Collector