Title: Multi-scale Temporal Prediction via Incremental Generation and Multi-agent Collaboration
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-scale Temporal Prediction

Incremental Generation

Multi-agent Collaboration
Abstract: Accurate temporal prediction is the bridge between comprehensive scene understanding and embodied artificial intelligence. However, predicting multiple fine-grained states of scene at multiple temporal scales is difficult for vision-language models.
We formalize the Multi‐Scale Temporal Prediction (MSTP) task in general and surgical scene by decomposing multi‐scale into two orthogonal dimensions: the temporal scale, forecasting states of human and surgery at varying look‐ahead intervals, and the state scale, modeling a hierarchy of states in general and surgical scene. For instance in general scene, states of contacting relationship are finer-grained than states of spatial relationship. For instance in surgical scene, medium‐level steps are finer‐grained than high‐level phases yet remain constrained by their encompassing phase. 
To support this unified task, we introduce the first MSTP Benchmark, featuring synchronized annotations across multiple state scales and temporal scales. We further propose a novel method, Incremental Generation and Multi‐agent Collaboration (IG-MC), which integrates two key innovations. Firstly, we propose an plug-and-play incremental generation to keep high-quality temporal prediction that continuously synthesizes up-to-date visual previews at expanding temporal scales to inform multiple decision-making agents, ensuring decision content and generated visuals remain synchronized and preventing performance degradation as look‐ahead intervals lengthen.
Secondly, we propose a decision‐driven multi‐agent collaboration framework for multiple states prediction, comprising generation, initiation, and multi‐state assessment agents that dynamically triggers and evaluates prediction cycles to balance global coherence and local fidelity. Extensive experiments on the MSTP Benchmark in general and surgical scene show that IG‐MC is a generalizable plug-and-play method for MSTP, demonstrating the effectiveness of incremental generation and the stability of decision‐driven multi‐agent collaboration.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents the IG-MC framework, a novel solution for multi-scale temporal prediction (MSTP) that addresses long-horizon error accumulation and cross-scale consistency through two core innovations: incremental generation and decision-driven multi-agent collaboration. The work is supported by the first MSTP Benchmark, enabling unified evaluation across general and surgical scenes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is well-organized, with clear definitions of temporal/state scales and a logical flow from problem formulation to methodology and experiments. This makes complex concepts like incremental diffusion and multi-agent coordination accessible.\n2. The integration of incremental generation and multi-agent collaboration is a contribution. This closed-loop design ensures state-visual synchronization and cross-scale consistency, addressing limitations of single-scale or open-loop methods.\n\nWeaknesses:\n1. The iterative nature of incremental diffusion and multi-agent coordination inherently introduces latency, yet the paper lacks critical metrics such as inference time per time step, GPU memory consumption during inference, or scalability with longer temporal horizons.\n2. The experiments primarily compare against Qwen2.5-VL-7B-Instruct, a general vision-language model. They omit comparisons with surgical-specific models or state-of-the-art MSTP methods, making it hard to contextualize IG-MC’s advances in surgical domains.\n\nQUESTIONS:\nQ1. The experimental comparisons are limited to Qwen2.5-VL-7B-Instruct, a general-purpose VLM. Given the focus on surgical scenes, why were there no comparisons with surgical-specific models? Such comparisons would better demonstrate IG-MC’s advantages in domain-specific tasks.\n\nQ2. The paper claims IG-MC maintains \"high performance across temporal scales,\" but there is no analysis of failure modes. Does the SD module generate anatomically invalid visuals for rare surgical tools or steps? Do long-horizon predictions degrade due to accumulated errors in incremental generation? Does multi-agent collaboration fail to enforce cross-scale consistency?\n\nQ3. The paper does not discuss failure cases, such as scenarios where incremental generation accumulates errors or multi-agent coordination breaks down. Understanding these cases is critical for improving robustness.\n\nLIMITATIONS:\nThe paper acknowledges limitations (e.g., SD module dependency on pre-trained diffusion models, VLM-bound performance, inference latency), but these are not deeply explored.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the IG-MC to address the challenge of multi-scale temporal prediction by decomposing the task into temporal and state scales. It uses an incremental generation mechanism to maintain temporal consistency in state-image synthesis and a decision-driven multi-agent system to hierarchically refine predictions across scales, enabling cross-scale coherence and real-time interaction between state forecasts and visual generation. Finally, the authors introduce the MSTP benchmark with synchronized multi-scale annotations for general and surgical scenes to demonstrates the significant advancement in multi-scale prediction accuracy and robustness of the IG-MC.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n(1) This paper is well organized.\n\n(2) This paper focuses on the multi-scale temporal prediction task in embodied artificial intelligence, which is a meaningful research field. \n\n(3)This paper introduces the first MSTP Benchmark, which features synchronized annotations across multiple state scales. \n\nWeakness: \n\n(1) Although the paper is well-engineered, its novelty lies primarily in integrating state-of-the-art modules (VLMs and Stable Diffusion for visual prediction) rather than proposing fundamentally new architectures or learning paradigms.\n\n(2) The necessity and mechanism for introducing state hierarchies need further clarification. Additionally, the small number of state scales evaluated (e.g., only 2 levels in the STP-Surgery benchmark) is insufficient to robustly validate the method's effectiveness.\n\n(3) In Lines 156-157, the authors claim that “the iterative nature of this process allows for error correction across time steps, as both state and visual representations are continuously refined based on each other’s outputs.” Please clarify why this enables error correction across time. Are there additional experiments to substantiate this claim?\n\n(4) Visual generation via Stable Diffusion can become unstable over long time horizons. How does this method prevent error accumulation in long-horizon predictions and ensure trustworthiness (as claimed in Lines 75-77), given its tendency for instability in extended sequences?\n\n(5) The training details for the State Transition Controller agent are unclear. Please clarify how it learns to reliably produce accurate continuation signals (to maintain the current state) or identify the precise hierarchical level where state transitions should initiate.\n\n(6) In Table 1 (MSTP-Surgery Benchmark), adding the SD module results in decreased Accuracy and Precision in some cases. This counterintuitive observation requires further explanation.\n\nQUESTIONS:\nThe detailed questions are shown in the Weaknesses section.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes IG-MC, Incremental Generation via Multi-Agent Collaboration, a framework for multi-scale temporal prediction with multi-modal data. This framework is composed of two main ideas. The first is the concept of incremental generation, an alternative prediction process that interleaves state forecasting and image generation (e.g., via diffusion). The second main concept is modelling the future prediction problem as a hierarchical system with coarse states all the way fine-grained actions. This second component is modelled as multiple LLM agents, one per level, which sequentially feed each other state information in order to construct the complete state. These are modulated by a State Transition Controller. The Multi-Scale Temporal Prediction benchmark is proposed. Experiments are performed on this benchmark and showcase the strength and effectiveness of the IG-MC framework.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The presented framework seems novel to me. The use of the sequence of LLM agents to construct the state is interesting to me.\n\n- Interleaving the decision making module with the Stable diffusion module seems like a good strategy for accurate temporal forecasting.\n\n\nWeaknesses:\n- The clarity of the presentation needs improvement, as can be seen by my many questions, particularly for Section 3. I think the presentation of the method should be made much clearer since this is the main contribution of the work.\n\n- There's a large line of related work missing particularly for temporal prediction with hierarchical structure. Here are just a couple meant as guidance to more related work:\n    - Hierarchical learning: Rohde, Tobias, Xiaoxia Wu, and Yinhan Liu. \"Hierarchical learning for generation with long source sequences.\" arXiv preprint arXiv:2104.07545 (2021).\n    - Multi-Token Prediction (multi-task learning): Gloeckle, F., Idrissi, B. Y., Rozière, B., Lopez-Paz, D., & Synnaeve, G. (2024). Better & faster large language models via multi-token prediction. arXiv preprint arXiv:2404.19737.\n    - Options Framework: Klissarov, M., & Precup, D. (2021). Flexible option learning. Advances in Neural Information Processing Systems, 34, 4632-4646.\n\n- The experimental section lacks certain details that make it difficult to asses the significance of the results. \n    - The baseline used is not described. Was the base Qwen model finetuned? If not, this does not seem like a fair comparison. \n    - Since Incr. Scale vs. Temporal scale are unclear, it is difficult to understand the significance. Both seem like values that would be dependent on the dataset.\n    - What is the size of the datasets used here? It is important to understand the size of the training set vs. the validation/test set.\n\n- I do not believe it is valid to claim a contribution for multi-scale Temporal prediction dataset since no details are provided on these datasets and what they provide. I believe the community needs more details on the methodology of its construction and some statistics on its composition.\n\nQUESTIONS:\n- line 92: Are those percentages?\n\n- Paragraph at Line 129: It is quite unclear the difference between temporal scale and incremental scale. And this seems quite important to understanding the results section 4.2. I see that these are explained in the supplementary section; this should be clarified in the main text.\n\n- Line 136: the whole paragraph could use improvements in the presentation. It is unclear what the levels are and I think here the reader can benefit from an example. Additionally, on lines 141-142, are the environments $C_k$ or are these actions?\n\n- Line 141: is timestep $t_k$ timestep $t$ at level $k$? I guess from equation 3, I can deduce that $k$ is the sub-timestep for the level we are in.\n\n- line 160: recursive -> recurrent in my opinion.\n\n- line 168: the current state should be $\\mathbf{s}_k$, not $/mathcal{S}_k$, since the latter is the state space, no?\n\n- If I understood correctly, the STC agent is tasked with either changing the state or keeping the same state. How can the state remain unchanged? Don't you always need to transition at some (finer) level? The second task of STC makes sense to me: the coarse levels can remain unchanged for a few transitions while we change finer levels.\n\n- line 172: level $i$ --> level $l$ for consistency.\n\n- Line 176: I think what is described there (and in equation 5) is not really iterative refinement, but more iterative state construction where as you iterate through the different agents, you append elements to the final state rather than change $(i<l)$ state values. Is that correct?\n\n- Line 206-207: I'm guessing this is the text encoder from the original Stable diffusion model. How is it ensured that the hierachical embedding space aligned well with the text encoder's semantic structure?\n\n- line 211: what is anatomical plausibility? And point 3 is unclear,  Is it just a projection of the latent input?\n\n- Line 230: what is meant by end-to-end differentiability here? On line 221, we highlighted that this is a modular stack with both main components being trained separately. So can the authors clarify what is mean by line 230? Also, what is the state-action mapping $\\pi$? This is the first mention of this function.\n\n- Figure 2: What is the point of the \"Images\" box? Also, I would like to suggess splitting the figure into three figures: \n    1) overview (top half of the figure).\n    2) DM Blocks, connected to Section 3.3.\n    3) SD Blocks, connected to Section 3.4.\n\n- Suggestion: I believe the reader would benefit from a good example figure that shows a full \"episode\" of the model in action. Perhaps this can replace Figure 1 which in my opinion doesn't provide much additional value compared to Figure 2 (top).\n\nLIMITATIONS:\nLimitations are not discussed in the main paper, but are present in the appendix (the one in the main pdf, not the supplementary).", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces the Multi-Scale Temporal Prediction (MSTP) task, which requires forecasting future scene states at multiple time horizons and abstraction levels. A new benchmark (MSTP) is proposed with synchronized annotations across temporal and state scales in general (human activities) and surgical (workflow) settings.\nThe proposed method, IG-MC, combines:\n1. Incremental Generation: predicting state-image pairs progressively over time to avoid error accumulation.\n2. Multi-Agent Collaboration: using a hierarchy of VLM agents (LLM-based) to refine predictions from coarse to fine state levels.\nExtensive experiments show IG-MC significantly improves performance (e.g., +30–40 points on Accuracy, F1) across tasks and domains.\n\nSTRENGTHS AND WEAKNESSES:\n1. Quality\nStrengths:\n- Clear formalization of a new prediction problem with a matching benchmark.\n- Strong experimental results across multiple tasks, domains, and metrics.\n- Comprehensive evaluation (Accuracy, F1, Jaccard, SSIM, FID, etc.) with ablations on module impact.\n- Well-designed pipeline combining prediction and generation in a feedback loop.\n\nWeaknesses:\n- Lacks comparison to strong existing baselines (e.g., prior hierarchical or video forecasting models).\n- No discussion of computational efficiency or inference latency.\n- SD module occasionally reduces accuracy despite improving recall.\n-Implementation complexity (multi-agent design) may hinder reproducibility without further detail.\n\n2. Significance\nStrengths:\n- Introduces a new, practically relevant benchmark and task formulation.\n- Demonstrates substantial gains in surgical and general video prediction.\n- Closed-loop design (prediction + visual simulation) offers a novel paradigm.\n- Could impact both embodied AI and forecasting communities.\n\nWeaknesses:\n- Benchmark and method are designed for hierarchical discrete-state tasks—generalizability is not fully proven.\n- Benefits may partly stem from use of large-scale pretrained models rather than method per se.\n\n3. Originality\nStrengths:\n- First to combine incremental state-image generation with hierarchical VLM agent prediction.\n- First benchmark for synchronized multi-scale scene understanding.\n- Creative integration of state-space forecasting with diffusion-based visual synthesis.\n\nWeaknesses:\n- Main components (LLMs, diffusion, multi-agent) are adapted from prior work—novelty lies in their combination.\n- Related ideas (e.g., feedback in forecasting, hierarchical state modeling) have been explored separately before.\n\nQUESTIONS:\n1. Is it possible to compare IG-MC to stronger or more traditional baselines to strengthen claims of effectiveness?\n2. Is it possible to clarify if agents are separate models or prompt variants of one model; describe training/fine-tuning strategy?\n3. Is it possible to discuss when visual generation hurts performance and suggest improvements or mitigations (e.g., filtering low-confidence images)?\n4. Can the authors provide inference time analysis and hardware specs; discuss feasibility for real-time applications like surgery?\n5. Can the authors discuss how IG-MC can be adapted to non-hierarchical or continuous prediction tasks?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-22"}