Video In-Context Learning

• Video In-Context Learning is a paradigm that leverages demonstration video clips as context to enable models to perform reasoning, editing, and generation without task-specific fine-tuning.
• It relies on transformer architectures with specialized tokenization and fusion strategies to effectively handle multimodal signals and temporal dependencies in high-dimensional video data.
• Applications include video question answering, zero-shot imitation, and instruction-based video editing, delivering state-of-the-art performance across diverse benchmarks.

Video In-Context Learning (Video ICL) refers to the ability of deep models—especially large transformer-based architectures—to perform video reasoning, imitation, editing, or other generative/understanding tasks by conditioning on video (or video-text) examples presented at inference time, without task- or domain-specific fine-tuning. This paradigm extends the “prompting” capabilities of LLMs to multimodal video data, enabling models to leverage demonstrations, contextual clips, or episodic sequences as direct context, thereby achieving rapid adaptation, generalization, and compositionality in tasks spanning video question answering, generative video modeling, workflow automation, and instruction-based video editing. Research in the past two years has produced technical advances in tokenization, context construction, model architectures, and prompting protocols tailored for the high-dimensional, temporally-structured, and multimodal nature of video, underpinning a new class of scalable, versatile, and instruction-following video models.

1. Core Principles and Formalism

The central mechanism of Video In-Context Learning is to embed one or more demonstration videos (optionally with associated text, actions, or instructions) as explicit conditioning sequences—i.e., “context”—when presenting a new video query or generation prompt to the model. Unlike in-language ICL where token-level sequence modeling suffices, Video ICL must address modality fusion, temporal dependencies, and scaling issues.

Formally, denote a set of $k$ demonstration video clips as $$D^v = \{V_1^{(\text{demo})}, \ldots, V_k^{(\text{demo})}\}$$, where $V_i = (s_i^1,\ldots,s_i^{n_i})$ and a query $x^v$. The goal is typically to generate a continuation $y^v$ (for synthesis) or a response/label (for understanding) that is consistent with the semantics of the demos: $f_\theta(x^v) = P(y^v \mid x^v, D^v).$ Sequence-to-sequence models flatten video frames into discrete tokens via learned tokenizers (e.g., VQ-VAEs) or project video frames and corresponding textual segments into a shared embedding space (Zhang et al., 10 Jul 2024, Lin et al., 31 Jul 2024).

Key architectural ingredients include:

• Tokenization of video into sequences compatible with transformer models.
• Joint text/video embedding and fusion strategies, with explicit positional and modality embeddings.
• Prompting mechanisms that interleave multiple modalities (e.g., ask a video QA model to answer $Q$ about a video $V$ by interleaving $V$ with captions, subtitles, or plot as context).
• Training objectives that are either generative (autoregressive next-token prediction, flow-matching) or discriminative, calibrated for the given downstream task.

2. Architectures, Pipelines, and Tokenization

Model architectures for Video ICL fall into several major categories:

a. Autoregressive Transformers for Video Imitation

Decoder-only transformers, trained with next-token prediction over video-token sequences, directly generalize the ICL properties of LLMs to the video domain. Demonstration and query video clips are concatenated, with autoregressive decoding producing continuations carrying demo-consistent semantics. No explicit separator is used between clips, allowing the self-attention mechanism to implicitly condition on arbitrary “contexts” (Zhang et al., 10 Jul 2024).

b. Multimodal and Instruction-Tuned Pipelines

Multimodal LLMs receive as context interleaved sequences of video frames, subtitles, images, and external knowledge, mapped to a flat embedding sequence. Instruction-tuning is employed to train the models on context-rich, task-agnostic prompts of the form: $$\text{USER: }\langle\text{interleaved context}\rangle~ \text{Question: }q, \qquad \text{MovieSeq: }\langle\text{answer}\rangle~\text{<EOS>}$$ as in MovieSeq (Lin et al., 31 Jul 2024).

Structured variants, such as VidCtx, sample frames, obtain question-aware captions, and build question-answer prompts which fuse both local frame and temporally distant context as input (Goulas et al., 23 Dec 2024). Another approach, as in VideoICL (Kim et al., 3 Dec 2024), selects top-$k$ most relevant context examples (using similarity metrics over video/text embeddings) and iteratively runs inference with batches of context, stopping upon high-confidence outputs.

c. In-Context Generative and Editing Pipelines

Video editing in-context learning models concatenate source and (noisy or target) video token streams, optionally with text instructions, and use full or block-causal self-attention over the sequence to perform joint denoising and region-specific editing (Liao et al., 16 Oct 2025, Zhang et al., 19 Dec 2025, Ju et al., 24 Sep 2025, Li et al., 17 Dec 2025). Architectures such as EditVerse and IC-Effect employ unified interleaved sequence representations and transformer diffusion models, leveraging flow-matching or denoising score-matching objectives for spatiotemporal consistency.

3. Applications, Tasks, and Benchmark Results

Video ICL underpins a broad spectrum of applications:

• Video Question Answering: Context-aware aggregation of frame-level visual and temporal cues, as in VidCtx, which sets state-of-the-art zero-shot performance among open models on NExT-QA, IntentQA, and STAR benchmarks (e.g., NExT-All 70.7% vs. 66.3% for prior models) (Goulas et al., 23 Dec 2024).
• Action Imitation and Zero-Shot Generation: Autoregressive models synthesize video sequences consistent with the semantics of demonstration clips, enabling zero-shot transfer to unseen tasks (Zhang et al., 10 Jul 2024).
• Instruction-Based Video Editing: Pretraining with unpaired clips plus in-context prompts enables efficient learning of complex edits (add, remove, replace, style transfer) from a small number of paired samples, as in (Liao et al., 16 Oct 2025), achieving 12%–15% improvement in instruction following and editing quality vs. prior systems.
• Structured Workflow Understanding and SOP Generation: Video-LLMs with in-context demonstration pairs or pseudo-label ensemble learning yield gains (e.g., +6.6% recall, +4.2% step ordering over zero-shot baselines) on low-level workflow extraction (Xu et al., 24 Sep 2024).
• Centric Medical & Biomedical Segmentation: Video ICL is formulated as video object segmentation with time-contrastive learning for context retrieval, achieving 90.95% Dice (↑10.64%) for image segmentation and 92.45% (↑14.88%) for video segmentation over baselines (Wahd et al., 21 Jun 2025).
• Interactive Agents and Video-Based Skill Transfer: Inference-time selection of dynamic, relevant demonstration trajectories from large online video corpora yields substantial performance gains for computer-use agents, outperforming text- or transcript-only approaches on OSWorld and WebArena (Liu et al., 6 Nov 2025).

A representative sample of task categories and benchmarks includes:

Domain	Approach	Representative Result
VideoQA (NExT-QA)	VidCtx	70.7% (top-1 accuracy, SOTA) (Goulas et al., 23 Dec 2024)
Zero-Shot Video Imitation	Vid-ICL	P-Acc up to ≈49% (in-class demo Δ+7–8 %) (Zhang et al., 10 Jul 2024)
Editing (VBench/CLIP-T)	EditVerse, IC-Effect	↑state-of-the-art instruction & effect accuracy (Ju et al., 24 Sep 2025, Li et al., 17 Dec 2025)
Workflow SOP (GoldDemo)	ICE	Recall +6.6%, Ordering +4.2% over zero-shot (Xu et al., 24 Sep 2024)
Medical Segmentation	Temporal	Dice 90.95%, ↑10.64% over baseline (Wahd et al., 21 Jun 2025)

4. Data Strategies, Prompt Construction, and ICL Induction

The emergence of effective in-context learning over video depends on systematic prompt construction, data curation, and training regime design.

a. Distributional Properties for ICL Induction

Emergence of true video ICL is linked to data distributions with:

• Burstiness: Clusters of related classes (e.g., verbs or objects) force contextual reasoning.
• Skewed Marginals: Long-tail distributions encourage reliance on context for rare events.
• Dynamic Meaning: Polysemy and synonymy in narration propel semantic disambiguation (Yu et al., 2023).

EILEV demonstrates that models exposed to such distributions in training are more likely to use contextual cues during inference and exhibit greater performance improvements (“ICL slope”) as context size increases.

b. Context and Example Selection

Similarity-based retrieval mechanisms select the most relevant demonstration examples for each query, combining text and video embeddings in a weighted cosine similarity: $$S_Q((t,v),(\tilde t,\tilde v)) = \alpha\,\text{cos}(r_t(t), r_t(\tilde t)) + (1-\alpha)\,\text{cos}(r_v(v), r_v(\tilde v))$$ with $\alpha$ a hyperparameter (Kim et al., 3 Dec 2024). Iterative inference extends the effective context window by cycling through multiple batches of examples, retaining only high-confidence outputs.

c. Multimodal Interleaving and Sequence Construction

Approaches such as MovieSeq and EditVerse construct interleaved token sequences blending video, images, subtitles, plot, and captions—each augmented by positional and modality-specific embeddings. This enables transformers to unify local (per-frame) and global (cross-clip, cross-modal) reasoning, grounding generation and retrieval in all available context (Lin et al., 31 Jul 2024, Ju et al., 24 Sep 2025).

d. Specialized Prompting and Pseudo-Labeling

For evaluation and fine-tuning in complex procedural or editing tasks, pseudo-label aggregation (ICE) or chain-of-editing context is used, with weighted consensus or consistency regularization to reconcile multiple context-seeded predictions (Xu et al., 24 Sep 2024, Qu et al., 12 Jun 2025).

5. Limitations, Ablations, and Scaling Behaviors

Common limitations and empirical findings across the literature include:

• Context Window Constraints: Token budget per video remains a key bottleneck, with strategies such as iterative inference (Kim et al., 3 Dec 2024) and sparse tokenization (Li et al., 17 Dec 2025) needed to amortize context utilization.
• Temporal Consistency: Long-range dependencies, especially in video synthesis/editing, are challenging; evaluations are often limited to short clips (e.g., 4–8 frames in Vid-ICL). Multi-turn editing or propagation within transformers (block-causal masking) is necessary for stable multistep tasks (Qu et al., 12 Jun 2025, Ju et al., 24 Sep 2025).
• Data Efficiency and Generalization: Training with unpaired or synthetic edits, combined with limited high-quality fine-tuning, produces better generalization and instructional alignment than reliance solely on dense, hand-constructed datasets (Liao et al., 16 Oct 2025).
• Ablations: Removal of context, region regularization, or in-context region constraints leads to degraded edit accuracy, background preservation, or temporal fidelity, as shown in VidCtx (Goulas et al., 23 Dec 2024), ReCo (Zhang et al., 19 Dec 2025), and IC-Effect (Li et al., 17 Dec 2025).

Scaling up model and dataset size typically improves both visual fidelity and controllability, following scaling laws analogously to LLMs (e.g., Vid-ICL, EditVerse) (Zhang et al., 10 Jul 2024, Ju et al., 24 Sep 2025).

6. Future Directions and Open Challenges

Several active directions and open research questions include:

• Unified Spatiotemporal Tokenization: Joint temporal-spatial compression (e.g., 3D VQ-VAE) to enable efficient modeling of longer or higher-resolution video context (Zhang et al., 10 Jul 2024).
• Unified and Flexible Context Fusion: Further advances will likely come from architectures handling arbitrary numbers/types/modalities of context tokens, dynamically composing context at inference to suit the downstream task (Lin et al., 31 Jul 2024, Ju et al., 24 Sep 2025).
• Robust Region-Level Conditioning and Masking: Developing reliable mechanisms for explicit spatial region conditioning and attention regularization for editing and composition (Zhang et al., 19 Dec 2025).
• Fine-Grained Domain Adaptation: Techniques such as confidence-based iterative inference and ICE can further broaden generalization to out-of-distribution (OOD) domains with only demonstration examples, which remains crucial for safety-critical or specialized applications (Kim et al., 3 Dec 2024, Xu et al., 24 Sep 2024).
• Interactive Agents and Closed-Loop Reasoning: Integration of online demonstrations from video corpora and two-stage selection/reranking of locally-relevant context (Liu et al., 6 Nov 2025).
• Benchmarks and Evaluation: Emergent metrics (VLM Editing Score, Clip-T, ViCLIP-T, etc.) evaluate compositional, temporally consistent, and instruction-following abilities in context-rich settings. Large open benchmarks such as EditVerseBench, ReCo-Data, or VideoVFX accelerate research (Ju et al., 24 Sep 2025, Zhang et al., 19 Dec 2025, Li et al., 17 Dec 2025).

Video In-Context Learning, through explicit demonstration conditioning and general-purpose multimodal transformers, is now a central capability underpinning state-of-the-art solutions in video understanding, editing, reasoning, and generation. Its trajectory parallels—and now converges with—the evolution of in-context learning in LLMs, signaling a unification of multimodal, context-conditioned machine intelligence.