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Multi-scale Temporal Prediction via Incremental Generation and Multi-agent Collaboration
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Formalizes multi-scale temporal prediction (MSTP) over temporal and state scales, releases an MSTP benchmark on Action Genome and GraSP, and proposes IG-MC, a plug-and-play incremental generation (DM + Stable Diffusion VG) with multi-agent collaboration for state forecasting.
Problem
Predicting multiple fine-grained states of a scene at multiple look-ahead intervals is difficult for vision-language models; existing methods focus on single-scale prediction and suffer error accumulation and growing visual token inputs over long horizons.
Approach
A stateless decision module predicts the next state from the current state-image pair, and a Stable Diffusion module synthesizes the next visual preview conditioned on that state in an alternating closed loop; a state transition controller and hierarchical state agents then refine predictions across phase and step levels.
Key results
- On MSTP-Surgery at 60s temporal scale, Qwen2.5-VL-7B accuracy rose from 13.20% to 51.90% with the DM module alone.
- On MSTP-General at 20-30s scale, adding DM+VG raised Qwen2.5-VL-7B accuracy from 4.76% to 60.22%.
- With DM+VG at 5s temporal scale on MSTP-Surgery, Qwen2.5-VL-7B accuracy was 42.40%, slightly below DM alone (50.80%) despite higher recall.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add DM to Qwen2.5-VL-7B-Instruct (ablation) | Qwen2.5-VL-7B-Instruct · MSTP-General, Mixture, A&S&C | Accuracy: 13.6 -> 56.11 | Helped |
| Add VG to DM (ablation) | Qwen2.5-VL-7B-Instruct · MSTP-General, Mixture, A&S&C; incremental scale T/2 | Accuracy: +7.03 | Helped |
| Add DM (ablation) | Qwen2.5-VL-7B-Instruct · MSTP-Surgery, 5s, P&S | Accuracy: 12.00 -> 50.80 | Helped |
| Add DM (ablation) | Qwen2.5-VL-7B-Instruct · MSTP-Surgery, 60s, P&S | Accuracy: 13.20 -> 51.90 | Helped |
| Add VG to DM using 1s incremental updates (ablation) | Qwen2.5-VL-7B-Instruct · MSTP-Surgery, 5s, P&S | Accuracy: -8.40 | Hurt |
| Add VG to DM using 5s incremental updates (ablation) | Qwen2.5-VL-7B-Instruct · MSTP-Surgery, 30s, P&S | Accuracy: -4.80 | Hurt |
| Add VG to DM using 5s incremental updates (ablation) | Qwen2.5-VL-7B-Instruct · MSTP-Surgery, 60s, P&S | Recall: +9.65 | Helped |
| Add VG to DM using 5s incremental updates (ablation) | Qwen2.5-VL-7B-Instruct · MSTP-Surgery, 60s, P&S | Accuracy: -14.70 | Hurt |
| Add DM (ablation) | Qwen2.5-VL-7B · MSTP-Surgery, 5s, Phase | F1: +42.63 | Helped |
| Add DM (ablation) | Qwen2.5-VL-7B · MSTP-Surgery, 5s, Step | Recall: +31.78 | Helped |
| Use incremental generation from 5s to 30s horizon | IG-MC · MSTP-Surgery, visual prediction | FID: 70.63 to 94.82 | Hurt |
Surprising findings
- Adding the VG visual module to the DM module lowered accuracy on several surgical 5s and 30s settings relative to DM alone (e.g., 50.80% to 42.40%), though recall improved.
- Image quality (FID) degrades with horizon length while decision accuracy remains comparatively stable.
Limitations
- Inference latency is high (about 68 s end-to-end), particularly at long temporal scales, so it is not real-time.
- Generated previews can be visually plausible but semantically inconsistent for rare surgical tools or anatomy, and performance is bounded by the base VLM.
- Evaluated on a single surgical dataset (GraSP, prostatectomy) and a general scene dataset; generalization to other procedures is limited.
Experimental setup
Datasets: Action Genome GraSP MSTP
Benchmarks: MSTP-General (Action Genome-based) MSTP-Surgery (GraSP-based)
Metrics: Accuracy Precision Recall F1 Jaccard PSNR SSIM LPIPS FID CLIPScore
Baselines: Qwen2.5-VL-7B-Instruct LLaVA1.5-7B Gemma3-27B InternVL3-8B SurgVLM-7B
Builds on: Stable Diffusion 3.5 Large Qwen2.5-VL Action Genome GraSP
Closest papers by meaning
- SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics (NeurIPS 2026 · similarity 0.86)
- MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments (CVPR 2025 · similarity 0.85)
- SAMPO: Scale-wise Autoregression with Motion Prompt for Generative World Models (NeurIPS 2025 · similarity 0.85)
- TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models (ICLR 2026 · similarity 0.85)
- EVLP: Learning Unified Embodied Vision-Language Planner with Reinforced Supervised Fine-Tuning (ICLR 2026 · similarity 0.84)
- StateSpaceDiffuser: Bringing Long Context to Diffusion World Models (NeurIPS 2025 · similarity 0.84)
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