SteinsGate: Adding Causality to Diffusions for Long Video Generation via Path Integral
Authors: Yufei Huang
Liangyu Yuan
Changxi Chi
Yunfan Liu
Cheng Tan
Siyuan Li
Jingbo Zhou
Haitao Lin
Chang Yu
Stan Z. Li
Venue: ICLR 2026 Poster (2026)
Keywords: Generative Models
Video Generation
Diffusion Guidance
Primary area: generative models

Abstract: Video generation has advanced rapidly, but current models remain limited to short clips, far from the length and complexity of real-world narratives. Long video generation is thus both important and challenging. Existing approaches either attempt to extend the modeling length of video diffusion models directly or merge short clips via shared frames. However, due to the lack of temporal causality modeling for video data, they achieve only limited extensions, suffer from discontinuous or even contradictory actions, and fail to support flexible and fine-grained temporal control. Thus, we propose Instruct-Video-Continuation (InstructVC), combining Temporal Action Binding for fine-grained temporal control and Causal Video Continuation for natural long-term simulation. Temporal Action Binding decomposes complex long videos by temporal causality into scene descriptions and action sequences with predicted durations, while Causal Video Continuation autoregressively generates coherent video narratives from the text story. We further introduce SteinsGate, an inference-time instance of InstructVC that uses an MLLM for Temporal Action Binding and Video Path Integral to enforce causality between actions, converting a pre-trained TI2V diffusion model into an autoregressive video continuation model. Benchmark results demonstrate the advantages of SteinsGate and InstructVC in achieving accurate temporal control and generating natural, smooth multi-action long videos.

Published as a conference paper at ICLR 2026
STEINSGATE: ADDING CAUSALITY TO DIFFUSIONS FOR LONG VIDEO GENERATION VIA PATH INTEGRAL
Yufei Huang 122, Liangyu Yuan', Changxi Chi!?, Yunfan Liu Cheng Tan', Siyuan Li
Jingbo Zhou' 12 Haitao Lin Chang Yu"" Stan Z. Li
Zhejiang University, Hangzhou
2 Al Lab, Research Center for Industries of the Future. Westlake University
3 Shanghai JiaoTTong University, Shanghai *hhnnhaai Artificial Intelligence Laboratory huangyu fei @west lakek..dd en; yuchang@west lake. .ww..ad................. edu.cn
ABSTRACT
Video generation has advanced rapidly, but current models remain limited to short clips, far from the length and complexity of real-world narratives. Multi-action long video generation is thus both important and challenging. Existing approaches either attempt extend the modeling length of video diffusion models directly or merge short clips via shared frames. However, due to the lack of temporal causality modeling for video data, they achieve only limited extensions, suffer from discontinuous or even contradictory actions, and fail to support flexible and fine-grained temporal control. Thus, we propose Instruct- Cileoo-Conninuttoon (In- structVC), combining Temporal Action Binding for fine-grained temporal control and Causal Video Continuation for natural long-term simulation. Temporal Ac- tion Binding decomposes complex long videos by temporal causality into scene descriptions and action sequences with predicted durations, while Causal Video Continuation autoregressively generates coherent video narratives from the text story. We further introduce SteinsGate, an inference-time instance of InstruciVC that uses an MLLM for Temporal Action Binding and Video Path Integral en- force causality between actions, converting a pre-trained TI2V diffusion model into an autoregressive video continuation model. Benchmark results demonstrate the advantages of SteinsGate and InstructVC in achieving accurate temporal con- trol and generating natural, smooth multi-actio long videos.
INTRODUCTION
Video is central medium of modern culture, encompassing both professional productions (c.g., films, anime, television) and user-generated content (e.g., vlogs, fan-made animations). Video gen- eration has thus emerged as a promising direction (Wan et al. 2025 Chen et al.) 2025 Teng al. 2023), aiming to lower creation barriers, expand narrative formats (e.g.. interactive videos, memes), and improve creative efficiency (Bruce et al./ [2024] HaCohen et al., 2024). The goal of video gen- eration is to translate user-pre provided inputs- either textual narratives (Text-to- Video, T2V) or static frames (Image-to- Video. 12V)-into coherent visual stories. Despite recent advances that enable vivid short clips, current models are constrained to only few seconds (Wan et al. 2025). far from the narrative length of real-world videos. This limitation motivates the study of long video genera- tion, where models produce action-rich and narratively complete videos from single prompt.
Long video generation faces two core challenges: long-term simulation, i.e., producing long, co- herent, and multi-action videos beyond the current duration limits; and temporal control, i.e., accurately following action-rich prompts to ensure the correct order, completeness, and smooth- ness of actions. These challenges are inherently coupled: handling complex prompts often requires longer video sequences, while effective temporal control from prompts, in turn, reduces variance and guides the model to generate temporally consistent and well-connected action sequences.
"Corresponding Author

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Published as a conference paper at ICLR 2026
History Frames with
"Wits the cup, takes sip, then places back
12V-RR Video Continuation: Wrong Motion Direction
Causality
incompletopess
*Temporal Direction Video Continuation With Temporal Causality
Temporal Co-Denolsing Continuation: Conflicted Modion Direction
Causality
Absense
A girl waivee hand makes a thumbe-up gesture makes heart gesture
give blow kiss
220 frames longer than original
A cellie table jumpss to the floor jumps to the sofa 325 frames (4x langer than original max) retracing its path jumpe to the table egain
Figure 1: Causal Video Continuation and Multi-action Long Video Generation. Upper panel: Given instructions and history, our method captures temporal causality to continue videos smoothly, making multi-action transitions natural, like resuming paused video. 12V-AR relies the last frame and often misjudges motion direction, while Temporal Co-Denoising enforces correlation rather than causality via overlapping clips, causing conflicts in multi-action scenarios. Lower panel: SteinsGate achieves accurate action-time binding and smooth multi-action long video generation.
Existing methods for long video generation fall into two categories: temporal expanding (Lu et al. 2024 Kim et al.) 2024) and temporal decomposition (Wang et al., 2023 Cai et al. (025). Temporal expanding enlarges the token capacity of diffusion models (e.g. via frequency decomposition (Lu al. 2)24)). but can only extend length marginally. Thus, we follow the idea of temporal decompo- sition (TD), which breaks long video into shorter segments, mitigating the length and control limits of temporal expanding. One framework for TD is temporal co-denoising, which generates each seg- ment independently and enforces adjacent segment correlation (temporal correlation) through syn- chronized denoising of overlapping regions of adjacent segments (Lu et al. 2024 Cai et al. 2025). However, relying on temporal correlations without fully conditioning on previous segments (i.e., temporal causality) often causes action direction conflicts (Fig. [ Since two adjacent segments generated independently, without respecting temporal causality, may exhibit completely mismatched action directions in their overlapping region. Another framework for TD 12V-AR, which autore- gressively generates each clip from only the last frame of the previous one. Only conditioning on last frame breaks temporal causality, as consecutive segments become only visually consistent while remaining blind to the dynamics of earlier segments. This break often leading to motion reversal and poor temporal coherence (Fig. D.. Finally, current TD methods only model local dependency between adjacent segments and ignore global causal planning, leading to incomplete sequences or broken causal order across multiple actions.
Motivated by the analysis above, we propose new framework for multi-action long video gen- eration, Instruct- Eesstrct-Viido--ee (Fig. 3) Temporal Action Binding, focusing on causal temporal control plan and place each action on a causal timeline, and Causal Video Continuation, focusing on temporal continuation to render the plans along the timeline. In Stage given a user prompt, we enrich and decompose it into scene and character description together with sequence of action- dutiionduration pairs, to disentangle general motions into global causal action sequences. Stage 2. pretrained video diffusion model equipped with local temporal causality autoregressively continues the video based on current action descriptions and predicted action durations, completing each action before moving to the next if the last action duration is insufficient. Overall, InstructVC translates texts into videos in natural causal order. The firt stage acts like actors, planning and performing actions along the timeline, while the next stage renders the video autoregressivelly producing the ongoing "performance".
(InstructVC). It adds global and local causality in two stages
We further introduce SteinsGate, a plug-and-plas inference-time instance of InstructVC that com- bines a Multi-modal Large Language Model (MLLM) for Temporal Action Binding and novel temporal guidance technique, Video Path Integral, to enforce causality between action blocks and seamlessly convert a pre-trained TI2V diffusion model into autoregressive video continuation
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model. The Video Path Integral takes short historical segment as input to enforee spatial and temporal causality. It samples historical frames as initial inputs for the TI2V model, predicts mul- tiple future trajectories, and uses weighted integration to guide them toward the extended direction of the past. everaging the spatial-temporal disentanglement of I2V models, historical informa- tion is propagated into the continuation, making video generation process history-aware, temporally coherent, and autoregressively extendable while accurately following action sequences. To further improve the efficiency and effectiveness of Video Path Integral in practice, we introduce three op- timizations in SteinsGate:
(1) Guidance Interval, which reduces computation for path integral and improve efficiency; (2) History-aligned Redistribution, which promotes convergence along the ex- tended direction of historical frames; and (3) Path Convergence Guidance, which strengthens the guidance progressively from weak to strong to better align generated video with historical context.
We leverage in-context learning on video dense-caption datasets [Wu et al., 2025), which provide real action sequences and durations, to teach an MLLM to enrich prompts and decompose them into detailed scenes with coherent action sequences and estimated durations. Binding actions to prompts with explicit durations--ike holding a control key in game for precise movement-reduces hal- lucinations and enables fine-gratd fine-grained temporal control, forming the basis for causal video continua- tion. During generation, the Video Path Integral prioritizes completing unfinished actions, ensuring smooth transitions the next action after the current one is executed. To evaluate framework, we construct the InstructVC Benchmark using generated multi-action storyboard-like prompts. Ex- periments show that SteinsGate and the InstructVC framework achieve accurate temporal control, smooth multi-action continuation, and natural long video generation, demonstrating our ability to translate textual narratives into coherent visual stories.
2 RELATED WORKS
Video Generation with Diffusion Models Research on video generation spans tasks, architec- tures, and generative frameworks. Text-to-video (T2V) generates videos from language (Chen et al.)
2023
2024b), while image-to-video (12V) produces temporally coherent sequences from a single
frame (Xing et al. 2023 Guo et al.] 2023). I2V models are often considered spatial-temporal dis-
entangled, injecting motion into the first frame and propagating its spatial information forward (Liu
ct al.
2025 Architecturally, early models relied on U-Net backbones (Chen et al. 2024b; Guo
al.
2023 J. but recent approaches have shifted Diffusion Transformers (DiT) (Peebles Xie
HaCohen et al.] 2024; Wan et al. 2025). In terms of generative frameworks, autoregressive models suit streaming or interactive scenarios (Bruce et al. 2024]] Chen et al.] 2024a), while dif- fusion, especially DiT-based, dominates T2V and 12V (Yang et al. [025]] Team [2024). Diffusion approaches often treat video as "3D images," ignoring its sequential and causal nature, which limits generalization to long complex motions, hinders temporal control, and restricts video length.
2022
Long Video Generation Many recent methods leverage pretrained diffusion models and decom- pose long video modeling with frequency overlapping snippets (Cai et al.) 2025: Wang et al.) 2023). For example, FreeLong (Lu et al., 2024) uses spectral blending and local- global attention to combine low- wwwfrregencyy global structure with high- high-frequency local details without extra training, reducing high-frequency distortion. Gen-L-VVieeo (Wang et al., (023) processes overlapping short clips during denoising to produce long videos with diverse semantics while maintaining frame con- sistency. On the other hand, Autoregressive methods decompose long video into causally ordered short clips via the chain rule (Chen al. 20244a] 2025 Teng et al., (025). which aids control and modeling but suffers from eтror accumulation and is less compatible with non-causal pretrained video models (Kim et al.) (2024). Inspired by these works, we propose add causality into pre- trained video diffusion foundation models at inference-time for plug-and-play temporal control and continuation. More related works could be found in the Appendix.
3 PRELIMINARIES
3.1 VIDEO GENERATION WITH DIFFUSION TRANSFORMERS
The de facto method to video generation is to encode videos into sequences of latent tokens and then apply diffusion modeling with transformer-
networks (Wan et al. 2025 HaCohen et al. 2024). commonly DiTs (Peebles& & Xiel (022). Given its scalability and strong performance, adopt Wan Video 2.1 (Wan) (Wan et al. 2025). an open-source based model, as our pretrained foundation. Wan encodes input video of frames [xx) into latent tokens {2,)
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The illustration of Video Path Integral. a) We integrate over multiple I2V video paths (i.e.. distributions or vector fields) of history frames to propagate not only spatial but also temporal information to continuing videos to add causality pre-trained diffusion models During sam- pling, the probabilities of history-aligned videos reinforce each other due to their consistency, while history-deviated videos, being diverse, fail reinforce and are gradually diluted. c) By conditioning on multiple historical frames, the continued video distribution is progressively constrained to satisfy historical conditions, approximating the true conditional distribution given the history.
Figure 2:
using a 3D causal VAE with spatiotemporal downsampling factor of 4x8x8. A denoising network Vg. implemented as an encoder-only transformer, then processes noisy latent tokens z, together with text tokens Ztezt from text encoders via spatiotemporal self-attention for denoising and cross- attention for text alignment. The noisy latents are defined as 24 = (1 t)e + iz, where is a standard Gaussian noise and t the flow-matching timestep. Training follows the Flow Matching objective (Lipman et al., (2023), expressed as:
u(2; t/c, 21) = u(2 faa)
21 Zt
C=
(22, (1)
1 1
where u(z,, 1|21) is the conditional velocity, representing a conditional video generation path. Then video generation takes the flow Ordinary Equation (ODE): dz, v(z, dt, zo ~ I).
3.2 MULTI-ACTION LONG VIDEO GENERATION
Task Formulation Given user-provided or extended prompt P = [Crat, Cimgs } contain- ing the visual description Ctrt. optionally an image condition Camg as the first frame and ordered action descriptions a, i.e., textual narratives, our goal is to translate the text narrative into video narrative by generating long-term simulation that completes each action sequentially according to the temporal order in the prompt. Unlike mainstream video generation paradigms that follow image-generation formulations, we formalize long video generation as a translation task: akin to text translation, the target video is generated autoregressively by following the logical and sequential order the source text, which explicitly requires temporal causality and continuity across actions.
Compositional Generation Given pretrained diffusion (or flow) sampler, one can sample from a single conditional distribution p(x|c). But often we need to sample from the product multiple conditionals- for example, conditioning jointly on both a pose ei and reference image 02-to get finer, more powerful control. Compositional Generation (Du et al., 2023) refers to methods that support sampling approximately from such product distributions. Its trained distributions pe (x|cs (c)) and pa (zr c2), with corresponding score functions V log p(z(c1) and Lo v log ((xc02)) one can approximately sample from the product p )p(z)(2) by adding the score:
idea is: given two pre-
V log ( (p (2) Vz logpo(z(c1) + V (o)
The estimated score of the product distribution typically needs to be paired with more advanced samplers to enable accurate sampling.
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4 METHOD
4.1 INSTRUCT VIDEO CONTINUATION
frameworks do not model temporal causality, instead using stitching techniques to merge independently generated clips. This creates a temporal coherence bottleneck, limiting flexible action transitions and making long videos appear as repeated edits of the same clip. Moreover, they neglect to construct prompts at the action level with distinct durations, typically assuming equal time spans for all prompts regardless of action complexity. This mismatch often causes actions to be skipped. incomplete, or repeated. In autoregressive generation, such errors accumulate, creating gaps between prompts (e.g.. failing to walk to a table before being asked to pick up an item), which can ultimately collapse the generation.
Motivations Previous
We therefore propose the Instruct- Videt-iiio-CCCo Continuation (InstructVC) framework (as shown in Fig. 3 Its core lies in Temporal Action Binding and Causal Video Continuation. Temporal Action Binding decomposes
long video into action-level units, predicts the duration of each, and binds them causally to the timeline. Guided by this plan, Causal Video Continuation autore- gressively generates cach action in sequence, enforcing temporal causality between actions.
Temporal Action Binding Given the strong text generation ability and rich world knowl- edge of MLLMs, we employ them as the execu- tor of Temporal Action Binding. However, di- rectly using MILM often introduces halluci- nations: the decomposed action sequences may appear linguistically coherent but lack physical plausibility and diverge from the distribution of TI2V foundation model training data, where texts correspond to realistic videos. This mis- match leads to dut-of-distribution prompts and poor video generation quality. To address this, we adopt in-context learning, providing exam- ples from video dense caption datasets with multi-action prompts to guide the MLLM in leveraging its world knowledge for more realis- tie Temporal Action Binding. More details are provided in the Appendix.
Description
"Character: a fair-akinned men stands livinng room, with black heir, beard. and mustache He horizontally ly green-white striped I-shirt, beige brown couch right side, several green plants, and Ikk white round clock, books, a photo frame, and green plant a white pot. [Action Description)
his wist. Scene: the backiround glass window with wooden frames on
several itoms held
Actionf: A woaring holds VR
Expanding Prompts and Assigning Actions Prompts and Durstion
headset his hands.
Action Time: (2a-12s)
Action2: places the
head.
Action Prompt
Action Time: [24-0444]
Binding
Action3: lowers hards looks on
VR
Auto-Rogressive
Action Time: (4a-18s)
Continuation For
Long Video Generation
Actione: The man titts his hands
loft and right and smils with the User Input: man VR headset on his head.
with a VR hearheed with
Action Time: [Bs-
exclled reactions.
The InstructVC framework. An MLLM splits the user prompt into scene description and coherent action sequences, refines each action to match model prompt style, and predicts its dura- tion, producing temporally grounded storyboard for next causal video continuation.
Figure 3:
Causal Video Continuation Guided by Tem- poral Action Binding, we explicitly model temporal causality through video continuation, generating each action sequentially in temporal order based on the history of previous ones. If the previous action is unfinished. the continuation naturally completes it first-for example, closing a laptop left half-shut before standing up- -hteeebbyy enforcing causal consistency. In the following, we describe how a pretrained video diffusion foundation model can be transformed into a Causal Video Continuation model in plug-and-play manner at inference time.
4.2 VIDEO PATH INTEGRAL
To perform explicit temporal modeling for multi-action video, we aim model the joint distribution of the video sequence. By the chain rule, this distribution can be factorized and simplified as:
NN I p(zz Z
p(Z II p(z, z
First-order Markov
p(ZI:N)
(3)
Here a1=V denotes the sequence of video segments (or action-level clips). The first-order Markov assumption approximates each segment as depending only the immediately preceding one, which is a common simplification in autoregressive video generation to improve tractability while retain- ing temporal coherence (Bruce al. 2024 Chen et al. 2024a). For simplicity, we will omit text or
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Interval for structure and motion
Interval for visual details
Original Video Distribution
History-aligned Distribution Bad 1:0.3 P
History-Devisted
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P
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Good: 0.6
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Flow Ciroction
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P
History Aligned
T-0
(8) Guidance Interval
(b) History-aligned Redistribution
(c) Path Convergence Guidance
Figure 4:
The illustration of SteinsGate framework. Our key designs are threefold: (a)Temporal
guidance is applied only during the dynamics- and airucture-controline phase, improving sampling speed without sacrificing quality; (b) Path weights are adjusted based on the motion alignment between generated (the overlapped part) and real history, shifting the distribution toward history- consistent directions. (c)The sampling velocity is anchored to the last-frame I2V velocity to respect happened history, while guidance difference gradually steers generation to add temporal causality.
image conditioning in the formulations of this section. That's said, given the pretrained video gen- ration model (z.).. we need to approximate the conditional distribution pa (z: 2;-1). A common practical simplification is to assume that consecutive video segments share overlapping history region 2 = {z. 0 1. and the conditional distribution becomes pe (z; Z,-1) Rs po %n))
Limitations of Spatial-to- Spatiollto-Temppral Guidance Note that 2₁ is subset of 2, A straightforward baseline is to cast enforeing temporal causality as a classical spatial inverse problem (Meng et al,, 2022). which studies how to infer complete sample given partial observations (c.g., the historical region) under consistency constraints. A well-studied solution is the Reconstruction Guidance tech- nique (Chung et al. (203),, which gradually reconstructs the given portion of a sample over multiple sampling steps by introducing reconstruction gradient. Under the flow matching framework, it can be formulated (Pokle et al. 2023):
(z f = (z, t) + V2, logp(zz/zz)
Zz, logp(zz|z:) = Vz. Vz.lzz - 2,
(4)
where én is the predicted history given noisy 2, and 17(t) is a coefficient with respect to the timestep. Current video models treat videos as "3D images", making it reasonable to borrow spatial-domain techniques. However, even with advanced spatial guidance (as shown in Experiment Sec. 5.2). the generated samples can perfectly reconstruct the historical portion, yet the continued video exhibits noticeable gaps from history, showing that the temporal structure is not properly modeled. Despite representing video as 3D images, predicting future frames from history remains highly uncertain, and the success of local-to-global spatial guidance does not carry over to the temporal domain.
Video Path Integral as Temporal Guidance Observations above motivates our study of temporal guidance for history-to-future video generation, propagating historical information to influence the future. In addition to implicit methods like Reconstruction Guidance, we seek an explicit solution (Fig. 2 Given the TI2V models which propagate spatial information from the first frame via conditional vector fields, we define the resulting video distribution as the 12V Video Path. Our idea is to integrate the 12V Video Paths of historical frames-thee I2V vector fields during multi-step sampling of the continued video (as shown in Fig. [ therehy explicitly propagating spatio-temporal information from history into continuation and extending 12V from Image-to- -Vdeo to History-to- Future at inference time. Video Path Integral could be formulated as:
Do t/(zz))/z)) = 1 J we (olov)6o((z t/xx;) Re
K
we (vo) (z,, flzy), (x) C {x]
(5)
where {x) denotes the history images and {>} the subset selected for Monte- Carlo Estima- tion due to frame rates and efficiency constrains in practice. And w(x)) is normalizing and temporal weighting factors for history alignment. tn represents the velocity predicted after replacing the corresponding historical segments in the generated trajectory 2; with noisy real history z supple- menting the image condition with dynamic and temporal information. For simplicity, we omit this notation in the subsequent analysis and more details are provided in the Appendíx.
The core of Video Path Integral is how temporal information is propagated into the future. The key lies in the nested structure of time: the 12V Video Path starting from history frame z, already in- cludes the path from the subsequent frame 2j+1 and so on. When integrating across the I2V Video Paths of all history frames, the trajectories consistent with the entire history-i.e., those aligned

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Hegoy
History Pronat: The man reaches toward the black cup with his right hand, and Vits smocthly
12V-4R
RO-Flow
Ours
Prompts: TThe man talies slw sp,, then places gently back on the desk."
With his right frand returingg The mon resumes bipin on the uptao
Figure 5: Qualitative Comparison for Video Continuation. Given prior history, SteinsGate follows the historical trajectory to complete the current action and transition smoothly to the next. Other causality-enforee methods fail propagate apatio-tempora information, often skipping required actions, reversing motion, or producing jumps between actions.
with temporal causality-are are reinforced, while inconsistent ones, starting from different frames, are gradually diluted. This analogous to the path integral in Quantum Physics, where path distribu- tions strengthen along the correct macroscopic trajectory and cancel out along incorrect paths. As a result, the Video Path Integral converges toward the direction consistent with historical temporal causality. This can be further interpreted from both probabilistic and sampling perspectives:
K
Probabilistic: p(z,lza) I[,,),)) Sampling: 2. log p(g)(zzz))))
Σ
2E logg(z/(=z)) (6)
j=1
j=1
That's, history conditional distribution is approximated by the product of frame-wise conditional distributions, enabling sampling via compositional generation (as Eq. 2) In the flow matching
setting, the score is converted into a vector field and further details are given in the Appendix.
4.3 STEINSGATE
To improve sampling efficiency, enforce temporal coherence with historical context, and reduce the estimation error of compositional generation, we introduce three simple enhancements (as shown in resulting in practical, plug-and-play causal video continuation method, SteinsGate.
Fig 4
Guidance Interval. Since Video Path Integral requires repeated velocity computations, we apply it only in the high-noise stage- where visual structure and motion are primarily determined to improve sampling efficiency. In later stages, which mainly refine visual details without altering overall motion, we directly use the I2V vector field of the last historical frame.
History-aligned Redistribution. Except for the last historical frame, each 12V video path over- laps with the history to varying lengths. To encourage the generated video to converge along the history, we weight different I2V video paths based on the known history, biasing the intermediate video distribution toward alignment with it. To avoid interference from static regions and varying overlap lengths, we propose Motion- Aware History Shifting, which weights each path according to the dynamic similarity between its predicted historical trajectory and the ground-truth history:
we( wr(vo(Zr: / (z))) = cos-similarity (m.. (m))
mj:H- = Zj+ 1:# 2j:H-1
where m is the motion vector within the predicted history with Uy and the true history.
Path Convergence Guidance. To reduce the estimation error of compositional generation, we adopt a more powerful sampling technique. Unlike traditional, time-consee consuming MCMC methods (Du et al. 2023), inspired by AutoGuidance (Karras et al. 2024), we propose Path Convergence Guid- ance (PCG): the 12V velocity of the last frame-without without temporal causality- used as the weak model estimate, while the result of Video Path Integral serves as the strong model estimate. Their
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Mochi-1 CagVeaaX Seaa playybourd
Ours
Prompte:
phone"
hand and tavess cotlie
flr phone and begins byping" Tauustte her hear"
Figure 6: Multi-action Long Video Generation. We conduct a system-level comparison across di- verse open- souree and commercial models. Results show that SteinsGate achieves accurate ac- tion-time binding and supports coherent multi-action text-to-video narrative translation.
DiTCurl SkyReel-V2 MAGI-1 FIFO SteinsGate w/o VPI w/o Gl w/o HR w/o PCG
CSCV
0.76
0.83
0.82
0.71
0.82
0.74
0.81
0.79
0.78
Motion Smoothnesst
0.93
0.96
0.96
0.89
0.97
0,94
0.97
0.95
0.96
Text- Imate Alignmentt
0.31
0.34
0.33
0.29
0.32
0.31
0.33
0.32
0.31
Quantitative Comparison and Ablation Study. VPI denotes Video Path Integral, GI de- notes Guidance Interval, and HR denotes History-aligned Redistribution. We compare SteinsGate with mainstream DiT-based autoregressive and Temporal Co-Denoising methods. SteinsGate out- performs other inference- time methods and achieves performance comparable to costly diffusion- foreing approaches. Each component contributes our efficiency and effectiveness.
Table I:
difference is then used the weak-to-strone guidance velocity Upey vo(ze 2n,) uo Flast)- Combined with classifier-free guidance(CFGG), our sampling procedure can be summarized as:
{ mraat + WpVpeg + w2(vv Flast) vo (ze (aast ()) 0/g = {1 ofgast + (2 (Ze Dinst) - 1o (2, 11
if f S bmid
(8)
t tmid
where biii the interval threshold (usually set to 0.3) and vo Flast; 0) denotes the 12V velocity without text condition (for simplicity, we omit the text condition, assuming it is present unless specified), and W1,Wg are guidance strengths (usually set to 1.5 and 5.0 respectively) for PCG and CFG. Causal Video Continuation follows the ODE: dz, og (Zt, 2h. T1:K kdt,, Zo (O,, I).
EXPERIMENTS
In this section, we conduct both qualitative and quantitative experiments across multiple tasks, in- cluding video continuation, action time binding, and multi-action long video generation, comparing against baselines from causality enforcing, temporal decomposition, and autoregressive approaches.
5.1 EXPERIMENTS SETUP
Datasets. We construct the InstructVC benchmark from video dense captions in MinT (Wu al. 2025) and StoryBench (Bugliarello et al. (023),, which provide storyboard-lize like temporal captions with sequential actions. These prompts are further expanded and diversified to match the InstructVC format. To evaluate the capability of Temporal Action Binding and ensure broader coverage of diverse scenarios, we additionally expand short prompts from VBench (Huang et al., 2024).
Baselines. We primarily compare against autoregressive baselines that perform autoregression along the temporal axis while still generating frames with diffusion. For the video continuation, we ad- ditionally implement causality-efforcing baselines that constrain adjacent clips at inference time, including 12V-AR, an enhanced version of Reconstruction Guidance (Huang et al. (2225) under flow matching (RG-Flow), and the classic -ntiial-uiddane method SDEdit (Meng et al.) (022). For temporal action binding and multi-action long video generation, we benchmark against diffusion- forcing-base based text-to-video models (SkyReel-V2 (Chen et al. 2025), MAGI-1 (Teng et al. 2025)
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Oure Binding
Arimaton
Prompts: "A.att malks towards a bow""
"Whikss moter
Vis its head"
Top. Panal: get playa plano" Botton Panal: "Tho workors angage in swordight
Figure 7: Ablation Study and More Results. We concatenate action prompts as clip prompts and evenly distribute predicted durations for clip-by-clip autoregressive generation (w/o Action Bind- insu.Rssults highlight the importance of Temporal Action Binding for precise temporal control and demonstrate the necessity and potential of MLLMs in handling more complex long video generation. SteinsGate also preserves the pretrained model's capabilities, supporting diverse video styles.
and the training-free FIFO-Diffusion (FIFO) (Kim et al. 2024 We also include temporal co- denoising methods, represented by DiT-based DiTCtrl (Cai al., 2025), noting that many earlier approaches relied on U-Net backbones. Finally, we provide qualitative comparisons with additional open-source models (Mochi-1 (Team] 2024). Cog CogVideoX-511 (Yang et al., (0255) and the commer- cial Sora (Storyboard version). More details could be found in the Appendix.
5.2
VIDEO CONTINUATION
As core task of the InstructVCC framework, we conduct qualitative experiments on text-based video continuation. Using prompts from the InstructVC benchmark, we generate historical videos from the earlier action prompts and continue them with subsequent prompts. We implement causality- enforcing baselines on Wan2.1 same SteinsGate by applying inference-time techniques to enforce causal continuity between the generated continuation and the history. Results (Fig. (5) show that SteinsGate successfully continues videos along the causal trajectory of the history and faith- fully follows text instructions, while other methods often ignore required actions, produce motions opposite to the historical trend (the 2nd and 4th rows), or create discontinuities between history and continuation (the 3rd row).
5.3 MULTI-ACTION LONG VIDEO GENERATION
To evaluate multi-action long video generation, perform system-level comparison includ- ing both qualitative and quantitative experiments. Qualitatively, we focus on action time bind- eng-exxcuting each action within the specified duration. Results in Fig. show that our method accurately generates the specified actions within the given time intervals, producing high-qualit long videos with coherent and natural motions. In contrast, other methods struggle to generate actions precisely, often skipping actions or disrupting their temporal order. Quantitatively, we mea- sure multi-action continuity and video quality following the DiTCcrl protocol Tab. D. Metrics include the Clip Similarity Coefficient of Variation (CSCV) to assess transition smoothness, CLIP similarity to evaluate alignment between prompts and video clips, and VBench Motion Smoothness to assess whether generated motions smooth and physically plausible. Results show that our inference-time method achieves performance comparable to costly training-based T2V diffusion- forcing methods, while outperforming other griniin-ffree approaches.
5.4 ABLATION STUDY
To illustrate the effect of Temporal Action Binding and the contributions of SteinsGate compo- nents, we compare against a global conditioning bascline (w/o Temporal Action Binding) where the scene description and multi-action prompts are concatenated and total action duration evenly di- vided across segments for video continuation. Results in Fig, show that Temporal Action Binding enables accurate temporal control, preventing skipped or misordered actions that cause disconti- nuities. Additional ablations in Tab. [ confirm that removing Video Path Integral while keeping Temporal Action Binding with 12V-AR reduces temporal guidance effectiveness, whereas intro- ducing the Guidance Interval preserves most performance while halving inference time. We also showcase additional videos in diverse styles (Fig. 7 to demonstrate SteinsGate's 's ability to preserve the capabilities of the pretrained model while supporting wide range of user requirements.

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6 CONCLUSION
We propose the InstructVC framework for multi-action long video generation, enabling stronger temporal control and more natural long-term simulation through Temporal Action Binding and Causal Video Continuation. We further introduce SteinsGate, an inference-time instance of In- structVC, which uses an MLLM and the temporal guidance technique Video Path Integral to inject causal awareness into pre-trained video diffusion models. A remaining limitation is long-term con- sistency, which is itself highly challenging research direction, as our focus is on temporal causal continuity and thus maintaining coherence relies on selecting appropriate history length.
ACKNOWLEDGEMENT
This work supported by National Science and Technology Major Project (No. NN.220222001151001) National Natural Science Foundation of China Project (No.624B2115 WU2022A009) from the Center of Synthetic Biology and Integrated Bioengineering of Westlake University and the Hangzhou Postdoctoral Daily Funding Program (No.10514000265825000 2025).
12.U21A220 No.U21A20427)) Project (No.
REFERENCES
Hritik Bansal, Yonatan Bitton, Michal Yarom, Idan Szpektor, Aditya Grover, and Kai-Wei Chang. Talc: Time-aligned captions for multi-scene texi-to-video generation. arXiv preprint 2.XY:22405.04682, 2024.
Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al. Genie: Generative inter- active environments. In Forty-first International Conference Machine Learning. 2024.
Emanuele Bugliarello, Hernan Moraldo, Ruben Villegas, Mohammad Babaeizadeh, Mohammad Taghi Saffar, Han Zhang, Dumitru Erhan, Vittorio Ferrari, Pieter-Jan Kindermans, and Paul Voigtlaender. StoryBench: Multifaceted Benchmark for Continuous Story Visualization. In Advances in Neural Information Processing Systems, volume 37. Curran Associates, Inc., 2023.
URL httttts/s////xxxiv wwtpp//.//pppoppttpooooooo /2308 10r.116665 160
Minghong Cai, Xiaodong Cun, Xiaoyu Li, Wenze Liu, Zhaoyang Zhang, Yong Zhang, Ying Shan, and Xiangyu Yue. Ditctrl: Exploring attention control in multi-modal diffusion transformer for tuning-free multi-prompt longer video generation. In Proceedings of the Computer Vision and Patlern Recognition Conference, pp. 7763-7772, 2025.
Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz. Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. ArXiv, abs/2407.0133 01392, 2024a. URL httpss//ap:: wwwanti www.scholar.rrrr.. wwwpscchllaa.oo/ooooouuuo /or.p//C/rrrrD:: 27C8696221
Guibin Chen, Dixuan Lin, Jiangping Yang, Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang. Sheng Chen, Zheng Chen, Chengcheng Ma, Weiming Xiong, Wei Wang, Nuo Pang, Kang Kang, Zhiheng Xu, Yuzhe Jin. Yupeng Liang. Yubing Song, Peng Zhao, Boyuan Xu, Di Qiu, Debang Li. Zhengcong Fei. Yang Li, and Yahui Zhou. Skyreels-v2: Infinite-length film generative model, 2025. BtRLbttps://// /twp//aavvvvv////////a//.......a wttp://.rrrgaaaa///eeeeee abt. 13074
Haoxin Chen, Menghan Xia, Yingqing Hc, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang. Chao Weng, and Ying Shan. Videocrafterl: Open diffusion models for high-quality video generation, 2023.
Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, and Ying Shan. Videocrafter2: Overcoming data limitations for high-quality video diffusion models, 2024b.
Hyungjin Chung. Jeongsol Kim, Michael Thompson Mccann, Mare Louis Klasky, and Jong Chul Ye. Diffusion posterior sampling for general noisy inverse problems. In The Eleventh Interna- tional Conference on Learning Representations, 2023. URL /ttttpp///ppenrvvie... net/
10

---

Published as a conference paper at ICLR 2026
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Sussman Grathwohl. Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and memc. In International con- ference on machine learning, pp. 8489-8510. PMLR, 2023.
Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffu- sion models without specific tuning. arXiv preprint arXiv:2307.04725, 2023.
Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, Poriya Panet, Sapir Weissbuch, Victor Kulikov, Yaki Bitterman, Zeev Melumian, and Ofir Bibi. Ltx-video: Realtime video latent diffusion. arXiv preprint arXiv:2501.00100 2024.
Yufei Huang, Yunshu Liu, Lirong Wu, Haitao Lin, Cheng Tan, Odin Zhang. Zhangyang Gao, Siyuan Li, Zicheng Liu, Yunfan Liu, al. Eva: Geometric inverse design for fast protein motif sooffl-caffolding with coupled flow. In The Thirteenth International Conference on Learning Representations, 2025.
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang. Yuanhan Zhang, Tianx- ing Wu, Qingyang Jin, Nattapol Champaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. VBench: Comprehensive benchmark suite for video generative models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recogni- tion, 2024.
Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024.
Tero Karras, Miika Aittala, Tuomas Kynkäänniemi, Jaakko Lehtinen, Timo Aila, and Samuli Laine. Guiding a diffusion model with a bad version of itself. In The Thirty-eigtth Annual Conference on Neural Information Processing Systems, 2024. URL http:: htttps:://penreviee... www.:ennttfooroeeee
id=bg6fvPVs33
Jihwan Kim, Junoh Kang, Jinyoung Choi, and Bohyung Han. Fifo-diffusion: Generating infinite videos from text without training. In NeurlPS, 2024.
Yaron Lipman, Ricky Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In The Eleventh International Conference Learning Repre- sentations, 2023. URL http::::://pepeeiiiiiie
net/ wttp://ouummrdddodoo /dd
Shaoteng Liu, Tianyu Wang, Jui-Hsien Wang, Qing Liu, Zhifei Zhang, Joon- Joon-Young Lee, Yijun Li, Bei Yu, Zhe Lin, Soo Ye Kim, al. Generative video propagation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 17712-17722, 2025.
Yu Lu, Yuanzhi Liang, Linchao Zhu, and Yi Yang. Freelong: Training free long video gener- ation with spectralblend temporal attention. In The Thirty-eighth Annual Conference on Neu- ral Information Processing Systems, 2024. htthttts:// http::///opneeee.eeeeeeeeeeeeeee www.mete//oorrr.. /tt../pss////ppee////.......
X9Fga5200v
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun- Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations, 2022. URL
https:// arrivv
//apps://axxv..../ wwwpwwwww//................oottt wwwgg/abb... /a/ps///18 01073
Gyeongrok Oh, Jachwan Jeong, Sieun Kim, Wonmin Byeon, Jinkyu Kim, Sungwoong Kim, and Sangpil Kim. Mevg: Multi-event video generation with text-to-video models. In European Con- ference on Computer Vision, pp. 401-418. Springer, 2024.
William Peebles and Saining Xie. Scalable diffusion models with transformers. arXiv preprint arXiv:2212.09748, 2022.
Ashwini Pokle, Matthew J Muckley, Ricky TQ Chen, and Brian Karrer. Training-fre free linear image inverses via flows. arXiv preprint 13.00433 2023.

---

Published as a conference paper at ICLR 2026
Genmo Team. Mochi 1. htttpp:://gtttuu..om//eeeemmt//p
2024.
Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Minggiu Tang, Shuai Han, Tianning Zhang, W. Q. Zhang, Weifeng Luo, Xiaoyang Kang, Yuchen Sun, Yue Cao, Yunpeng Huang, Yutong Lin, Yuxin Fang, Zewei Tao, Zheng Zhang, Zhongshu Wang, Zixun Liu, Dai Shi, Guoli Su. Hanwen Sun, Hong Pan, Jie Wang. Jiexin Sheng, Min Cui, Min Hu, Ming Yan, Shucheng Yin, Siran Zhang, Tingting Liu, Xianping Yin, Xiaoyu Yang, Xin Song. Xuan Hu, Yankai Zhang, and Yuqiao Li. Magi-1: Autoregressive video generation at scale, 2025. URL
org/ 2505. 13211
Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. Phenaki: Variable length video generation from open domain textual description. arXiv preprint arXiv:2210.02.993 2022.
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.203 20314, 2025.
Fu- Yun Wang, Wenshuo Chen, Guanglu Song, Han-Jia Ye, Yu Liu, and Hongsheng Li. Gen-I-video: Multi-text to long video generation via temporal co-denoising, 2023.
Ziyi Wu, Aliaksandr Siarohin, Willi Menapace, Ivan Skorokhodov, Yuwei Fang, Varnith Chordia, Igor Gilitschenski, and Sergey Tulyakov. Mind the time: Temporally-controlle multi-event video generation. In CVPR, 2025.
Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen, Xintao Wang, Tien-Tsin Wong, and Ying Shan. Dynamicrafter: Animating open-domain images with video diffusion priors, 2023.
Xin Yan, Yuxuan Cai, Qiuyue Wang, Yuan Zhou, Wenhao Huang, and Huan Yang. Long video diffusion generation with segmented gross-attention and content-rich video data curation. Pro- ceedings of the Computer Vision and Pattern Recognition Conference, pp. 3184-3194, 2025.
Zhuoyi Yang, Jiayan Teng. Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang. Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Zhang, Weihan Wang. Yean Cheng, Bin Xu, Xiaotao Gu, Yuxiao Dong, and Jie Tang. Cogvideox: Text-to-video diffusion models with an expert transformer. In The Thirteenth International Conference on Learning Representations, 2025. URL http:::t////oo https://openzeview. www.e/wwweeeeee.eee.eee...eeeeee wtt.:e//oorooooo-oo2o2 g9
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A APPENDIX
A.1 MORE RELATED WORKS AND BACKGROUNDS
Long Video Generation Recent attempts combine diffusion with autoregressive modeling by varying frame noise levels (Chen et al. 2024a), but they conflict with the training paradigm of video foundation models, creating severe train-inference gap (Kim et al. (2244) that requires costly post-training for each new foundation model (Chen et al.) [025] [Tng et al. 2025). Temporal de- composition splits long videos into shorter clips to ease length and control limitations, but merging them coherently is challenging. I2V-AR generates clips autoregressively from the last frame of the previous segment, enforcing spatial continuity but lacking temporal causality, while temporal co- denoising overlap and synchronized denoising, which can degrade quality. Recent training-free free methods, such as DiT-Ctrl (Cai et al.) 2025), extend short video models by generating overlapping clips and modeling denoising trajectories maintain temporal coherence.
Multi-prompt Video Generation Multi-prompt video generation is natural form of long-video generation. Previous works in this area have mostly focused on multi-scene video generation (Vil- legas et al., 2022, Cai et al., (025), aiming for smooth scene transitions similar to video editing. Some later works VVillegas et al. 2022 Oh et al. 2024) explored using different prompts to express different actions, but they usually assign equal durations to each action and do not account for their dynamic differences. More recently, methods (Wu et al., 2025) Bansal et al., 2024; Yan et al. (005) with time-alle aligned multi-action prompts have been proposed: however, these methods typically gen- erate different video segments with different text conditions in a single denoising window, which limits the maximum video length they could generate. In this work, building on time-aligned multi- action prompts, we introduce Video Path Integral, which transforms a fixed-length video generation model into a segment-leve autoregressive generation model, enabling long-term multi-action video continuation.
Path Integral The path integral is one of the formulations of quantum mechanics, providing a bridge between the probabilistic behavior of the microscopic world and the deterministic patterns observed in the macroscopic world. In this paper we only borrow the underlying idea and thus give a conceptual introduction. The core notion is that macroscopic trajectory (e.g.. light traveling along the shortest path) can be viewed as the result of integrating over all possible microscopic paths, each weighted by corresponding quantity. While every possible path is explored probabilistically, contributions reinforce along the true trajectory and cancel out elsewhere, yielding the stable macro- scopic path we observe-for instance, light appearing to travel strictly along the shortest route.
A.2 TEMPORAL ACTION BINDING
Spatial Description Expanding Previous multi-prompt frameworks do not model temporal causality, relying instead on extra stitching techniques to merge independently generated clips. This creates a temporal coherence bottleneck, restricting ficxible action transitions and making long videos appear as repeated edits of the same clip rather than coherent multi-action sequences. Be- sides, previous multi-prompt video generation frameworks also neglect to construct prompts at the action level with distinct durations. Instead, they typically assume equal time spans for all prompts, regardless of the number or type of actions involved. This mismatch often causes actions to be skipped, left incomplete, or repeated. In autoregressive generation, such omissions executions create gaps between consecutive prompts (e.g.. failing to walk to a table before being asked to pick up an item on the table), leading to error accumulation and eventual collapse of the generation.
incomplete
MLLMS as Actors for Temporal and Spatial Description Expanding Given a user prompt-ranging from a broad description (e.g., man is working) to explicit list of ac- tions Mons-MLLMM with given contexts is employed to expand the prompt into a detailed scene de- s
scription (covering environment and characters) and coherent sequence of actions. Each action is then refined to better align with the style of pre-trained model prompts (c.g., using simple verbs with explicit motion magnitudes). Leveraging world knowledge and contextual information, the MILMM
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also predicts the likely duration of each action. The result is complete temporally grounded prompt consisting of a scene description and a sequence of temporal-action binding descriptions.
The storyboard like prompts take the format: "name". "seed", "action-num" "uue""""ceeeddsrriippii basic setup and a sequence of action descriptions "action-id" paired with "frame-num-id follows up to bind each actions to the timeline. When different actions occur simultaneously in close succession, we group them into single action prompt and predict a joint duration for the whole.
for
A.3 IMPLEMENTATION STEINSGATE
Video Path Integral Video Path Integral takes short segment of historical video as input for both spatial and temporal causality. During the generation flow, it randomly samples several historical frames the initial frame for the TI2V model to predict multiple possible future trajectories, or video paths. It then uses weighted integration to guide these trajectories step by step, converging them along the extended direction of the historical frames effectivcly "pressing play" on a paused video. We leverage the spatial- -emporal disentanglement of I2V models by introducing multiple his- torical frames, allowing past spatial and temporal information to propagate into the continued video. This enables the generated video to be history-awarr and understand temporal progression. Dur- ing inference, the diffusion model extended autoregressively similar block diffusion-wwie accurately following action sequences.
In the video continuation task, we directly take as input either user-provided videos or previously generated clips. For multi-action long video generation, we first generate the initial segment from text or image-text prompts, then apply Video Path Integral to achieve causal video continuation, producing the complete long video. The Video Path Integral process works as follows: given the length of the previous video, we select a segment as history according to a fixed ratio (note that the number of frames must satisfy the format 4N + 1). We then initialize noise with the target length (usually the duration of the next action). During each denoising step, a random set of history frames is chosen as conditional frames. Their noisy counterparts are concatenated with the segment to be generated, after which 12V velocities are predicted from the selected history frames. Velocities corresponding to the newly generated part are combined through weighted sum, and the result is updated according to PCG. An algorithm workflow could be refered to Alg
Algorithm A training-free video continuation method for multi-action long video generation
Input: Pretrained video model vgg, prompts with N sgements P=(Ctet, Cimgs { (4)))) where {at). {st] are action prompts and latent frame number for each segment.
Output: Multi-action long vidcos x I:N with action control.
1: Generate: the first video chunk zz with T2V (u(Crat, a1) or TI2V va(Cent, a1 Ging).
2: Decode: Decode the video latent 2. into video frames x!
3: for each video segment E [2, N of multi-action long video do
4: calculate the history length H = [0.21,] and select the last H frames from the previous
segment x as the history frames }
5: for each denoising step E [1, 7] with total denoising step do
6: if the denoising step t 0.3T then
7:
Calculate the monte-carlo estimation video path integral: Random select K (2,3) subset {Ix K from history frames
8:
9:
Calculate the weighted vector field uy (2), (/2n) = Ek= wero Ctzt 0gy srg).
K
Calculate the PCG vector Epeg = (2) 122x) - (2:
Ctze: 0g,
11:
else
12:
Obtain last frame TI2V vector field va (zfl /tmmt Os xK); Calculate ig with PCG CFG Guidance as in Equation 8
13:
14:
Continuing the ODE update step: dz v (zf: Ni.K, Ctnt, dd;;
15: end if
16: end for
17: Decode the video latent zi into video frames x"
18:
end for
19: return the multi-action long video !::N
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History Frame Selection adopt simple, empirical strategy for selecting the historical seg- ment: we set the number of historical frames to approximately 20% of the length of the upcoming segment (Niistory x 0.2 J.umem).This choice aims to balance historical conditioning with text-driven control. Since the Wan2.1 model can process at most 81 frames per inference, using too many his- torical frames would reduce the available capacity for generating the segment, thereby harming text adherence. Through empirical evaluation, we find that allocating 20% history and 80% newly generated frames provides good trade-off. Because the generation length per step varies (typically 49-81 frames), using a relative ratio is more appropriate. This results in using roughly 13-25 histor- ical frames- sufficient to preserve temporal dynamics while avoiding excessive constraints on the upcoming motion.
the requirement above that the video length follow the 4N+1 format imposed by the underlying pre-trained video generation model. The current Video VAE (Wan VAR in our work) encodes the first frame independently and then compresses every subsequent four frames into one latent frame, which necessitates that the generated video length be of the form 4N+1. To remain aligned with the base model, we follow the same constraint: the historical segment constructed to satisfy the 4N+1 format, and the newly generated segment follows the 4N format, that the combined sequence (history + new frames) also conforms to the required 4N+1 structure.
During each step, randomly sample K=2-3 history frames from total history frame H Nhistory for a Monte-Carlo estimation for Video Path Integral. The choice of K is kept between 2-3 primarily control the per-step inference cost. Using larger K would make the sampling time grow quickly and become impractical. The specific frames selected may vary across steps, and after multiple denoising iterations, nearly all historical frames are eventually covered. This Monte Carlo estimation strategy allows the otherwise costly process of mixing H 12V paths to be amortized across iterations, improving sampling efficiency in practice.
Guidance Interval Guidance Interval refers to the use of Video Path Integral during the first part of the denoising process, specifically from time step == to 0.3 (equivalent to the first 15 discrete steps under a 50-step DDIM schedule). This interval was chosen as balance between efficiency and performance: applying Video Path Integral over more steps can slightly improve results, but the sampling time increases linearly with the number of steps, making larger thresholds less cost- effective.
Path Convergence Guidance Similar to Classifier Free Guidance and AutoGuidance, weak-to- strong guidance methods generally require extrapolative formulation, i.c., using guidance weight larger than 1. The key intuition is that interpolation between the weak and strong direc- tions often leads to worse results than using the strong direction alone, whereas extrapolation shifts further along the tweak-to-strong' direction Ustrong Uweak (as shown in Fig, (b). typically yielding outputs better than using the strong direction alone.
Theoretically, this can be understood from the perspective of distribution shift: the guided direction corresponds to modified distribution P(x) P( .;)". Only when the exponent > 1 does the distribution shift sufficiently toward the desired region, enabling effective guidance.
A.4 EXPERIMENT SETUP
Baselines We implement all Causality Enforcing Baselines for causal video continuation based on the same underlying model, 1an2.1, and ensure that these baselines share the Temporal Action Binding framework with SteinsGate, i.e., they are conditioned on the same multi-action prompts. For SDEdit, we adopt a similar idea by replacing the historical segment of the generated video during sampling with noised version of the real history. For RG-Flow, we interpolate between the generated history and the ground-truth history, and then predict an updated vector field based on the interpolated history:
Epp |2 Iee [2:] 2t
iz (2.(2)))
E. Zz
H) t)zz, + EE, [2:]
H]
1-t
Finally, 12V-AR simply generates the subsequent video segment by using the last frame of the pre- vious segment as input to the T12V model.
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We select representative baselines for multi-action long video generation. DiTCtrl and FIFO- Diffusion the most related training-free free baselines for SkyReel-v2 and MAGI-1 are indeed the large-scale training-basse bbsen vidco continuation models, which we also include for comparison. Furthermore, we include the commercial model Sora (storyboard enhanced version) for a system level comparison. Results suggest that SteinsGate achieve better performance than all training-free baselines and comparable performance with large-scale training- based baselines and commercial models. SteinsGate, as a training-free proof of concept, demon- strates both the feasibility and the advantages of the InstructVC approach combining global causal planning with local video-causal continuation for temporal causality modeling. It also shows the practical value of Video Path Integral as novel form of temporal guidance.
video generation. Meanwhile,
All baselines are experimented following their official Hugging Face repositories or codebases. Ex- periments are conducted on single NVIDIA A100 GPU.
Setups We build SteinsGate on Wan2.1, 1, a leading open-source Text-Image-to- Video DiT model. For mullii-rompp generation, we apply Temporal Action Binding to structure prompts, setting each clip length to its predicted duration 15 fps. For fingleprommp generation, scene description and all action prompts, with the total length set to the sum of action durations. All prompts are expanded with GPT-4o (Hurst et al., (024), generation uses an Euler sampler with 50 steps, and outputs are rendered at 480x720 resolution.
concatenate the
All baselines evaluated on the same single NVIDIA A100 S0GB GPU. For video continuation baselines, we re-implemented each method within a unified codebase to ensure consistent samplers and denoising steps. For multi-action long-video generation, we conduct a system-level compari- son: all baselines run with their default, recommended configurations, with no inference-time con- straints. We compare only the final video quality. For latency, the vanilla I2V-AR baseline (no extra inference-time overhead, just for video generation) takes approximately 30 minutes to generate a 15-second video, while SteinsGate requires around 38 minutes, an acceptable 25% inference time overhead in practice.
Metrics We adopt the evaluation metrics used in DiTCtrl [1] and VBench [3], including CSCV, Motion Smoothness, and Text-Image Alignment. The detailed definitions can found the origi- nal paper; here we provide brief summary:
Clip Similarity Coefficient Variation (CSCV) (Cai et al. 2025 : a metric specifically designed to evaluate the transition smoothness of multi-promy prompt videos, defined as:
1
= of Mi+1, I...., N CSCV = 1 + X a
(10)
where x; denotes clip frame features, o and μ are standard deviation and average for clip similarity score respectively. The Coefficient of Variation CV (ss)/p(s) describes the degree of unifor- mity, which can largely punish the isolated situation. The function 1+x] projects the score to [0.1]. the larger the better.
Tmxt- Alignment: a commonly adopted metric using CLIP Similarity assess the aligmment between given prompts and output video clips
(Cai al. 2025 to
Motion smoothness: a metric from VBench (Huang et al. 2024) evaluate whether the motion in the generated video is smooth and follows the physical law of the real world.
Since our focus on evaluating motion smoothness during multi nulti-prompt transitions an aspect closely tied to video continuation quality-it is difficult to establish a consistent and objective stan- dard across different human raters, which can easily introduce bias. Given that we do not have the resources recruit and train professional annotators, we instead use automated metrics such as the Clip Similarity Coefficient Variation (CSCV), which offer consistent measurement and reliably reflect multi poult--prompt transition quality.
Benchmark Construction Using GPT-4o, we constructed diverse prompt dataset consisting of 60 long- form prompts with character and scene deseriptions, along with a set of action descriptions. Since our method is training-free, the dataset is used purely for testing and contains no train/val
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Published as a conference paper at ICLR 2026
PCG Weight
History Length Ratio
Guidunce Interval
Selected Fraiee Nam.
Metric
w=0.5
w=1.0
w=1.5
r=10%
m20%
1300%
fow=0.11
fur=0.5
K=]
K=2
K=3
CSCV
0.75
0.78
0.82
0.75
0.82
0.79
0.76
0.82
0.81
0.74
0.79
0.82
Motion Smoothness
0.93
0.95
0.97
0.94
0.95
0,95
0.97
0.97
0.94
0.9
0.97
Text-Image Alignment
0.31
0.29
0.32
0.31
0.32
0.31
0.31
0.32
0.33
0.31
0.31
0.32
Table 2: More Ablations. History Length Ratio specifies how many frames from the end of the previous segment are used as history, computed relative to the length of the upcoming segment; balances the amount of historical context versus newly generated content within a fixed window.tmid determines the cutoff in the denoising schedule after which we stop applying Video Path Integral and instead use the vector field conditioned the last frame. Selected Frame Number denotes how many historical frames are sampled in each step when applying the Video Path Integral's 's Monte- Carlo estimation; these frames are used to compute the 12V video paths for that step.
pog www..egttt-11 1.5 pap wol..r-t weggtet.5
Figure 8: Qualitative Ablation Study for Path Convergence Guidance weight. Prompt: "a man working at a desk and then close his computer for a leave."
splits. Most prompts are adapted from the MinT (Wa et al., 2025) test set via GPT-4o rewriting, while a smaller portion is directly generated by GPT-4o.
Roughly 30% of the test prompts contain four actions, about 50% contain three actions, around 10% contain more than four actions, and the remaining 10% contain two actions. The scenes are primarily human-centric (around 80%), which aligns with the strengths of current video generation models and the typical application setting for multi-action long-video generation. These include outdoor scenes, indoor scenes, full-body and half-body shots, as well as various human-object interaction scenarios. The remaining 20% involve other scene types such as animals and landscapes. In terms of duration, about 70% of the videos are around 20 seconds long, 18% are around 15 seconds, and the remaining 12% exceed 20 seconds.
A.5 MORE EXPERIMENTS RESULTS
More Ablations We conducted additional ablation studies, and the results are shown in Table 2 The findings indicate that our choice of history ratio r 20% achieves good balance among the model's 's maximum manageable temporal window, the difficulty of history adherence, and consis- tency with both text and historical context. For each step of the Video Path Integral, we K 2, which offers strong performance while keeping the inference-time overhead acceptable. For Path Convergence Guidance, we adopt the more effective extrapolation strategy with w 1.5. For the Guidance Interval, we use midd 0.3, which provides the best trade-off between inference-time cost and generation quality.
We also provide more qualitative results in Figure 8 The gral (VPI) velocity exhibits high variance and contains multiple plausible motion directions, which can be observed from the mixed and unstable motions when peg = 1.0. When peg 1.0. the model interpolates between the VPI velocity and the last-frame 12V velocity, causing it to lean toward the latter. Although this reduces motion mixing, often leads to incorrect motion directions due
Video Path Inte-
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Published as a conference paper at ICLR 2026
Figure 9: More qualitative cases. Prompt for the upper: "Anime style. A girl sits at a piano playing.", the middle: "A woman greets and talks the camera, then ends by making heart gesture and blowing kiss toward the camera.", the bottom: "A cat jumps off a table onto the floor, then leaps onto sofa, and finally walks back to the table."
to over-reliance on the last-frame guidance. In contrast, when peg > 1.0, the model extrapolates between the two velocities, effectively suppressing the potentially incorrect direction suggested by the last-frame 12V velocity. As a result, the guidance shifts the model toward more plausible and coherent motion directions.
More qualitative results including human, animate-style and animal are provided in Figure g
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Published as a conference paper at ICLR 2026
A.6 USE OF LLMs
Manuscript preparation. We used a large language model only for polishing the paper and all contents and primary writing were conducted by the authors.
Method role of MILM. our method, multi-modal large language model (MLLM) serves as the executor of Temporal Action Binding. The instruction prompts and in-context learning examples used to train this behavior are provided below.
Please help me enrich the user prompt for video generation. Given single text prompt, you need to extend to a list of temporal captions and global caption. Or given a list of temporal captions, you need give a global caption and polish the temporal captions with reference to the captions before. The goal is to enhance the short user prompt and provide more context to the video generator, that the generated video contains more detailed and coherent events. Please focus solely on visual elements and actions without mentioning ambient sounds or other non-visual sensory details.
The temporal captions describe sequential events happening in the scene and their lasting time (in frame_num). should follow these rules:
1. Each event should maintain similar entities and background scenes.
2. Each event prompt must contain only a single motion or action.
3. Each event prompt be easily described by video clip shorter than 5s (8 (ffrrmmes).
4. The event lasting time is measured frame numbers (the default fps=15). should reflect the time needed perform each action in real world which is usually different between events. The frame number should be in 4*N+1 format.
5. Each event should be smoothly connected its adjacent events, i.c., they can be plausibly presented in a video without any cuts.
6. Each event prompt can also contain the camera motion the beginning if it is important to the event.
7. There should no more than events.
8. The whole video should not exceed 30s.
9. Be careful not to alter the action sequence in the provided temporal captions; ensure that the action sequence remains consistent with the original temporal captions.
10. Refine temporal captions to ensure smooth narrative flow by incorporating contextual de- tails from the global caption, such spatial layout and object relationships. Use appropriate pronouns to maintain consistent reference and create seamless transitions between consecutive temporal captions. Objects should not appear suddenly-if referenced in later captions, they must be introduced or implied in preceding ones. Briefly mention results of previous actions in subsequent captions. such as adding "with the VR headset still on" after was put on earlier. The combined word count of the global caption plus any single temporal caption should be 80-100 words.
The global caption is general description of the scene containing:
1. The background the scene such as the spatial layout objects.
2. The main entities involved in the scene and their attributes, such as clothing, age, and appear- ance of a person.
3. The weather if it is outdoor scene.
4. Camera angles and movements if they are important to the scene.
5. Do not include overall summary of the video content; focus on describing the appearance of the environment and the characters.
6. You may focus on describing appearance, but avoid introducing complex spatial layouts excessive number of objects.
Example I-N: ..... (in context learning examples)
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Reviews:
[{"review_id": "x6bPQZb45e", "reviewer": "Reviewer_nDSM", "summary": "This paper proposes a new framework, Instruct-Video-Continuation (InstructVC), to address the challenge of generating coherent, multi-action long videos. The framework contains two stages: Temporal Action Binding, which decomposes a user prompt into a sequence of segment plans with durations via LLM, and Causal Video Continuation, which autoregressively generates the video according to that plan. The authors introduce SteinsGate, a training-free, plug-and-play method of this framework that introduces a novel temporal guidance technique called Video Path Integral (VPI). VPI enforces causality by integrating multiple I2V video paths from different history frames.", "questions": "A baseline could be directly using the last frame of the last segment, with a new prompt, to obtain the new segment. And concat all segments together. Is this setting the same as the w/o VPI results?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The method is training-free and plug-and-play. It does not need the training resources, thus it could be integrated easily into methods. \n2. The novelty of the VPI technique is quite sound and reasonable. The writing is also good. \n3. The method is somewhat time-consuming when inference, while the authors recognise this and propose a speeding strategy of Guidance Interval GI. From the ablation study, this GI method achieves comparable results before and after speedup.", "weaknesses": "Overall, the paper is logically rigorous, addresses an important problem, and proposes a novel method. I do not see major flaws. However, there are a few minor points that, if answered by the authors, I would consider revising my score after rebuttal:\n1. While VPI is a training-free method, it appears to introduce significant inference-time overhead. It will be better to quantify the increase in computational cost. Specifically, how many times more computationally expensive is the VPI method (which samples and integrates $K$ frames) compared to the baseline of generating from only the last frame?\n2. The Path Convergence Guidance (PCG) method introduces a new hyperparameter, $w_1$. The paper seems to suggest setting this parameter to 5.0, but lacks a detailed explanation. While the concept may be intuitive to some, why must $w_1$ be a value greater than 1 (implying \"over-correction\")? Why can it not be less than 1 (implying a smoothing correction, similar to an EMA) or exactly 1 (implying a full replacement of the weak model's path)? The paper would be strengthened by a qualitative analysis, or ideally, an ablation study or visualization that justifies this specific choice.\n3. The paper provides very little detail on how the \"segment history\" is selected. The Appendix (L657) only mentions that it is chosen based on a \"fixed ratio\" and that the frame count must satisfy a \"$4N+1$\" format. Why for both of these? Intuitively, the choice of history length seems highly sensitive: \n- A history that is too short may prevent VPI from capturing sufficient information to continue the motion naturally.\n- A history that is too long might make it extremely difficult to align a video generated from a very early frame with the distant future, potentially conflicting with the HR mechanism.\n4. The method of introducing different prompts for different temporal segments (with or without an MLLM) is not entirely uncommon. The paper may be missing citations to some related works that have explored similar multi-prompt temporal decomposition strategies:\n\n[1] Yan, Xin, et al. \"Long video diffusion generation with segmented cross-attention and content-rich video data curation.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[2] Bansal, Hritik, et al. \"Talc: Time-aligned captions for multi-scene text-to-video generation.\" arXiv preprint arXiv:2405.04682 (2024).\n\n[3] Oh, Gyeongrok, et al. \"MEVG: Multi-event Video Generation with Text-to-Video Models.\" ArXiv, 2023, https://arxiv.org/abs/2312.04086.\n\n[4] Villegas, Ruben, et al. \"Phenaki: Variable Length Video Generation From Open Domain Textual Description.\" ArXiv, 2022, https://arxiv.org/abs/2210.02399", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AFDPXk4Wej", "reviewer": "Reviewer_8cn5", "summary": "This paper proposes a framework for multi-action long video generation, Instruct-Video-Continuation (InstructVC) that contains two control stages: Temporal Action Binding and Causal Video Continuation. The authors further introduce an inference-time instance SteinsGate.", "questions": "Please see above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper is well organized.  \n\n2. The topic of long video generation is worth exploring in the research community. This paper targets this important problem.\n\n3. The paper provides some video demos to help reviewers better evaluate the performance of the proposed method.", "weaknesses": "There are some concerns and questions about this paper:\n\n1.\tThe third paragraph of the introduction is a bit confusing. The first part mentions two methods for generating long videos: temporal expanding and temporal decomposition. So, to which category does the latter part of the paragraph, I2V-AR, and another work, belong?\n\n2.\tIt is recommended that the author provide a corresponding video for Fig. 1. A few frames are insufficient to convey the author's intended meaning.\n\n3.\tThe author repeatedly emphasizes the term \"temporal causality.\" What exactly does this term mean? Why does its absence lead to phenomena such as temporal inconsistency and action direction conflict in the generated video?\n\n4.\tThe two paragraphs following the introduction cover a wide range of topics, such as Temporal Action Binding, Causal Video Continuation, Guidance Interval, History-aligned Redistribution, and Path Convergence Guidance. This can be quite confusing, as it raises the question: which part is the core of the proposed method? Which part is the key to solving long-action video generation?\n\n5.\tThe video demos in the supplementary materials all seem to involve very simple actions and are basically long videos within a single content. I think focusing on story-based long video generation would be better. Additionally, the video \"woman_gestures\" is clearly discontinuous, exhibiting obvious splicing artifacts from multiple video clips.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "N0A8u6g8vx", "reviewer": "Reviewer_inkc", "summary": "The paper introduces Instruct-Video-Continuation (InstructVC), a two-stage inference-time framework for multi-action long video generation that adds temporal causality to pretrained video diffusion models. Stage 1 (Temporal Action Binding) uses an MLLM with in-context examples to decompose and expand a user prompt into a scene description plus a sequence of action-duration pairs. Stage 2 (Causal Video Continuation) converts an Image-to-Video (I2V) diffusion model into a history-aware autoregressive continuer via Video Path Integral (VPI), which integrates multiple I2V “video paths” from sampled historical frames to bias future trajectories toward history-consistent directions. The practical instance SteinsGate adds three optimizations—Guidance Interval, History-aligned Redistribution, and Path Convergence Guidance—to reduce compute and improve alignment. Experiments on a constructed InstructVC benchmark and ablations report improved temporal control, motion smoothness, and multi-action continuity vs. several inference-time baselines and show competitive performance with heavier training-based methods.", "questions": "1. Can the authors precisely define the notation and dimensionality used in Eq. (5) for Video Path Integral, including what each index (i, j, K, H) represents and how histories are sampled in practice?\n\n2. How exactly is the mapping performed from image-conditioned I2V vector fields to the history-conditioned vector fields ve(Zt | xj); give the algorithmic steps used at each denoising iteration.\n\n3. What is the precise schedule for Guidance Interval: is it a fixed fraction of the continuous time variable, and how was 0.3 chosen?\n\n4. When you say the Video Path Integral approximates p(Zi | Zh) by product of frame-wise conditionals (Eq. 6), what independence assumptions are implied and when do they break down?\n\n5. How many prompts and total videos are in the InstructVC benchmark (train/val/test splits), and what is the distribution of number-of-actions, scene types, and durations?\n\n6. What are the exact definitions and implementations for CSCV+, Motion Smoothness, and Text-Image Alignment metrics, and how were thresholds or evaluation pipelines calibrated?\n\n7. Were human raters used for causal correctness or action completeness? If so, how many raters, what instructions, and inter-rater agreement (e.g., Cohen’s kappa) were measured?\n\n8. For quantitative comparisons, were compute budgets and sampling steps matched across methods (same sampler, same number of denoising steps)? Please provide latency and GPU-memory numbers.\n\n9. Please provide a sensitivity analysis for the number of history frames K, the selection strategy for those frames, and the history-length ratio used for different action durations.\n\n10. How robust is SteinsGate to noisy or incorrect history (e.g., partially corrupted frames, temporal jitter, or mismatched last-frame poses)?\n\n11. How does performance degrade over very long sequences beyond 30s, and what empirical evidence supports the claim that VPI reduces error accumulation compared to I2V-AR?\n\n12. What failure modes are most common (motion reversal, skipped actions, object disappearance), and can you quantify their frequencies per baseline?\n\n13. What MLLM model/version and temperature/prompting strategy was used for Temporal Action Binding, and what in-context examples were provided (exact prompts/examples)?\n\n13. Please provide quantitative measures of MLLM output quality: rate of hallucinated or physically implausible action sequences, distribution shift vs. the TI2V training captions, and any post-filtering applied.\n\n14. Will code, exact prompts, model checkpoints (Wan2.1 config and weights), and InstructVC benchmark splits be released?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper isolates two well-motivated, complementary challenges for long video generation—temporal causality and temporal control—and proposes a coherent two-stage solution.\n\n2. Video Path Integral provides a principled, interpretable way to propagate temporal information from multiple history frames and to bias generation toward causally consistent continuations without retraining the foundation model.\n\n3. The three SteinsGate optimizations (Guidance Interval, History-aligned Redistribution, Path Convergence Guidance) address computational cost and estimation noise, making the method more viable in practice.\n\n4. Temporal Action Binding leverages MLLM capabilities for temporally grounding and duration prediction, enabling fine-grained temporal control over multi-action sequences.\n\n5. The paper includes qualitative continuation examples, a system-level comparison for multi-action long generation, and ablations that quantify contribution of each component.\n\n6. The method can be applied at inference-time to pretrained TI2V/DiT models, which increases practical applicability and lowers barrier for adoption.", "weaknesses": "1. The VPI argument is mostly conceptual and analogy-driven (path integral intuition); formal bounds or rigorous analysis of when/why VPI converges to the true conditional distribution are missing.\n\n2. Performance and long-term consistency appear sensitive to how much and which historical frames are used, but the paper provides only heuristic choices and limited analysis of failure modes for varying history lengths.\n\n3. Although in-context learning is used to reduce implausible decompositions, reliance on an MLLM can still produce action sequences or durations that are out-of-distribution for the pretrained video model; mitigation and quantitative assessment are limited.\n\n4. Benchmark construction and evaluation transparency: the InstructVC benchmark is constructed by expanding dense-caption datasets, but details on dataset size, prompt diversity, split protocols, evaluation metrics definitions, and human evaluation procedures are sparse.\n\n5. Reported baselines are appropriate, but the paper lacks comparisons against recent large-scale training-based long-video systems (or more thorough hyperparameter-matched baselines) that could better contextualize headroom and limitations.\n\n6. The metrics focus on smoothness and CLIP alignment; deeper semantic correctness (action completeness, causal correctness judged by humans) and statistical significance of improvements are not fully reported.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)