Video-Next-Event Prediction (VNEP)

Updated 21 November 2025

Video-Next-Event Prediction (VNEP) is a multimodal task that anticipates subsequent events by integrating video context, dialogue, and external knowledge with both semantic precision and visual fidelity.
It spans formulations like multiple-choice event selection, structured event induction, and generative video synthesis, each using tailored models and evaluation metrics such as MCQ accuracy and FVD.
Successful VNEP approaches leverage compositional video understanding, commonsense reasoning, and cross-modal fusion, yet face challenges in long-horizon predictions and robust real-world deployment.

Video-Next-Event Prediction (VNEP) constitutes a family of multimodal learning tasks that aim to infer or generate the most probable subsequent event given a video context, with increasing emphasis on both semantic precision and visual fidelity. VNEP formalizes next-event anticipation not merely as textual prediction (as in classical NEP), but often as dynamic video synthesis that directly demonstrates the modeled future. This paradigm spans a spectrum from multiple-choice event selection, through structured argument inference, to fully generative spatio-temporal synthesis. Success in VNEP demands integration of compositional video understanding, commonsense knowledge, temporal reasoning, and cross-modal fusion.

1. Formal Definitions and Task Taxonomy

VNEP is instantiated under several formulations, reflecting evolving objectives and architectures across recent literature.

Multiple-choice future selection: Given a video $V$ with aligned dialogue $L$ and optional external knowledge $K$ , the model chooses the more likely event from candidates $\{e_1, e_2\}$ immediately following the context. Probabilistically, $y^* = \arg\max_{i \in \{1,2\}} P(y=i | V, L, K)$ , where joint embeddings of $(V, L, e_i)$ are scored via an MLP fused over video and text features (Lei et al., 2020).
Structured event induction: Given a chain of structured event graphs $C = (G_{E_1}, ..., G_{E_k})$ , AVEP (Action-centric Video Event Prediction) requires the prediction of the next event’s trigger verb and its set of arguments. Nodes encode multimodal features from both frames and text spans (Su et al., 19 Oct 2025).
Generative video answering: The formal VNEP definition requires, for input video context $V_{in}$ and a question $q$ , producing a novel short video $V_{out}$ illustrating the next event. This is factored as $V_{out}^* = \arg\max_{V_{out}} \mathbb{E}_{s \sim \pi_{VLM}} \left[\pi_{VDM}(V_{out}|s, V_{in}) \right]$ , where $\pi_{VLM}$ generates next-event captions and $\pi_{VDM}$ synthesizes video grounded in both semantic and visual context (Cheng et al., 20 Nov 2025).
Autoregressive temporal abstraction: In self-supervised NEP, a model consumes past frames $X_p$ and autoregressively predicts a tokenized future summary $S_f$ , optimizing $L_{NEP} = - \sum_{t=1}^N \log P(S_f[t] | X_p; \theta )$ (Wang et al., 28 May 2025).

These formulations serve distinct experimental and practical purposes, but all share the intent to anticipate or instantiate the next temporally relevant event, leveraging extensive multimodal context and causal reasoning.

2. Dataset Construction and Benchmarking

Dataset design for VNEP is characterized by methodological advances to maximize the semantic richness and predictive challenge:

Video-and-Language Event Prediction (VLEP): 10,234 clips (TV, vlogs), 28,726 future-event examples, each annotated with a premise, two candidate event descriptions (positive/negative), and rationales. Annotation involves adversarial human/model-in-the-loop procedures—examples are iteratively refined to suppress trivial or artifact-driven negatives via real-time model feedback and cross-domain bipartite matching. Round-wise adversarial filtering reduces premise-oblivious model exploitability (75.3% → 59.6%) (Lei et al., 2020).
V1-33K: 33,000 automatically segmented video instances from sports, cooking, surveillance, and interaction domains, with past/future splits (avg. 12s per segment). Future summaries $S_f$ are generated by VLM captioners and critiqued/refined by LLMs to yield high-fidelity temporal event abstractions (Wang et al., 28 May 2025).
VANS-Data-100K: Comprehensive dataset for generative VNEP: 100K context–question–answer triplets (30K procedural, 70K predictive), comprising standardized short clips and human-in-the-loop template-based QA generation. Ground-truth answers include both semantic captions and full next-event videos (Cheng et al., 20 Nov 2025).
AVEP: 35,264 annotated videos, ~178K event graphs, 498K multimodal argument nodes, verbs and noun labels spanning >2K and >6K unique tokens, respectively. Each event is structured as argument graphs for compositional inference and graph-based attention (Su et al., 19 Oct 2025).

Benchmarking protocols employ multiple-choice accuracy, top-K classification, F1/precision/recall for argument prediction, BLEU/ROUGE for captioning, Fréchet Video Distance (FVD), and CLIP-based metrics for video–text alignment.

3. Model Architectures and Fusion Strategies

VNEP methods encompass canonical architectures and bespoke fusion modules to address multimodal and hierarchical reasoning demands.

Multimodal Transformers: Baseline encoders extract video appearance and motion features (e.g., ResNet-152, ResNeXt-101, ViT), fuse with contextual dialogue (RoBERTa, fine-tuned on ATOMIC), and employ a multimodal transformer layer to yield joint embeddings. The fusion leverages concatenation and positional encoding across time and modality (Lei et al., 2020).
EventFormer: A node-graph hierarchical attention transformer employing multi-layer GNN-based feature extraction and dual-level attention—node-level (softmax over queries/keys), then block-summed graph-level, and finally re-broadcasted cross-attention. Coreference encoding leverages cyclic sinusoidal schemes to tie argument nodes across events, supporting fine-grained argument retrieval (Su et al., 19 Oct 2025).
SSR-based models: Events are represented as rooted structures of (verb, (role, entity)), flattened as token sequences for transformer encoders. Structured symbolic inputs (SSR) plus event-sequence context enable improved macro-accuracy (e.g., 58.6% with auxiliary arguments, 59.2% with VisualCOMET pretraining) (Lu et al., 2023).
Generative cascades: VANS integrates a VLM (Qwen-2.5-VL-3B) for semantic reasoning and next-event captioning, paired with a video diffusion model (Wan-2.1-1.3B DiT) for synthesizing temporally-consistent output videos. VAE tokenization of reference frames ensures appearance and identity preservation, with concurrent semantic guidance via caption conditioning (Cheng et al., 20 Nov 2025).
Example-guided predictors: VPEG implements stochastic video prediction guided by nearest-neighbor retrieval in motion-code space, priors constructed as Gaussian mixtures over empirical means/covariances from retrieved “expert” trajectories, and a multi-term loss (reconstruction best-of-N, variance-matching, adversarial) to align future frame synthesis with the multi-modal ground-truth (Xu et al., 2020).

4. Training Objectives, Instruction Tuning, and Reinforcement Learning

Learning objectives in VNEP are tailored to promote robust temporal inference and visual–semantic consistency.

Cross-Entropy and Focal Loss: Standard cross-entropy for event selection and verb classification, focal loss for argument retrieval (to address class imbalance), and auxiliary MSE terms for embedding alignment (Su et al., 19 Oct 2025).
Self-supervised NEP loss: Negative log-likelihood over predicted future summary tokens, encouraging chain-of-thought temporal abstraction (Wang et al., 28 May 2025).
Supervised Fine-Tuning (SFT), Critique Fine-Tuning (CFT), and Distillation: Instruction tuning strategies, either imitating chain-of-thought traces, integrating GPT-4-generated critiques, or mixing sample types at batch-time; SFT yields most efficient gains (Wang et al., 28 May 2025).
Joint-GRPO: Coordinated reinforcement learning to align reasoning and synthesis. A two-stage loop alternates VLM optimization via joint semantic/visual reward (ROUGE-L, CLIPSim, template faithfulness), then VDM tuning via video and semantic alignment metrics. Empirically, Joint-GRPO outperforms both standalone and naïve joint RL or SFT baselines (BLEU@1, FVD, CLIP-V/T) (Cheng et al., 20 Nov 2025).

5. Empirical Results and Ablation Analyses

Evaluation across VNEP tasks demonstrates several convergent findings:

<table> <thead> <tr> <th>Study</th> <th>Best Model Acc/Score</th> <th>Human Performance</th> </tr> </thead> <tbody> <tr> <td>VLEP (Lei et al., 2020)</td> <td\>67.46% MCQ accuracy (video+dialogue+ATOMIC)</td> <td\>90.50% (video+dialogue+future)</td> </tr> <tr> <td>FutureBench (Wang et al., 28 May 2025)</td> <td\>63.4% (NEP+Mix); >81% (RL for 1-2 hop)</td> <td>-</td> </tr> <tr> <td>EventFormer (Su et al., 19 Oct 2025)</td> <td>ACC@1: 22.71%; Noun F1: 46.24%</td> <td>ACC@1: 38.46%; Noun F1: 57.83%</td> </tr> <tr> <td>VANS (Cheng et al., 20 Nov 2025)</td> <td>BLEU@1: 0.3257; FVD: 78.32; CLIP-V: 0.8021</td> <td>Human eval: 4.8/5 overall</td> </tr> </tbody> </table>

Ablation studies consistently show:

Multimodal fusion and commonsense injection each yield incremental gains; removing knowledge or video features degrades performance.
Richer future summary length improves multi-hop inference.
Node-graph and coreference encoding in event-centric models outpace linear or vanilla attention strategies.
SSR structure (verb+argument) outperforms pure video feature fusion, with contextual event sequence providing an additional boost (Lu et al., 2023).
Joint-GRPO yields additive improvement on both semantic and visual dimensions, with isolated RL showing weaker gains and increased instability.

6. Limitations, Open Challenges, and Prospective Research

VNEP remains a challenging paradigm due to several factors:

There exists a persistent gap to human-level prediction—e.g., 90.5% vs. 67.5% (VLEP)—attributed to commonsense gaps, temporality failures, and insufficient fine-grained grounding (Lei et al., 2020).
RL post-training is compute-intensive and sensitive to reward formulation; reward hacking or collapse can occur if components are omitted (Cheng et al., 20 Nov 2025).
Most systems are confined to short single-step predictions; extensions to long-horizon, multi-step, or open-ended video-event reasoning are open problems (Cheng et al., 20 Nov 2025, Wang et al., 28 May 2025).
Integration of richer external knowledge (dynamic retrieval, larger graphs), improved joint video–language representations, and generative modeling for open-ended event prediction are identified as urgent challenges (Lei et al., 2020, Wang et al., 28 May 2025, Su et al., 19 Oct 2025).
SSR models reveal that compositional event context is essential, but direct fusion with video features remains problematic due to noise and irrelevance (Lu et al., 2023).

Future directions include scalable multitask/instruction tuning, end-to-end differentiable vision-language-synthesis pipelines, adaptive demonstration personalization, and open benchmarks for rationale quality and longer-term forecasting.

7. Conceptual Significance and Research Impact

VNEP connects and advances several domains:

Temporal and causal reasoning in multimodal LLMs/MLLMs.
Compositional event induction, graph-based logic over video sequences, and structured argument inference.
Video generation as procedural “answering” rather than mere entertainment, supporting intuitive and customized learning.
Dataset design incorporating adversarial filtering, chain-of-thought, and high-fidelity semantic/visual alignment.
Reinforcement learning paradigms for cross-modal optimization.

A plausible implication is that VNEP will form a core foundation for next-generation interactive agents in domains requiring physical demonstration, commonsense anticipation, and multi-hop video understanding. The systematic benchmarking and continual architecture refinement documented in the literature, notably (Cheng et al., 20 Nov 2025, Wang et al., 28 May 2025, Lei et al., 2020, Su et al., 19 Oct 2025, Lu et al., 2023), and (Xu et al., 2020), establish robust empirical groundwork and guide future exploration toward cognitive fidelity and practical deployment.