Video Large Language Models

Updated 4 September 2025

Video LLMs are multimodal models that combine video encoding and large language models to interpret and generate descriptive content based on video data.
They leverage modality encoding, cross-attention, and sequence reasoning to fuse spatial, temporal, and semantic features into unified token sequences.
They enable advanced tasks like video QA, captioning, and creative content generation while facing challenges in fine-grained temporal reasoning and robust safety.

Video LLMs (Video LLMs) are a class of multimodal models that integrate large-scale LLMs with video understanding components to process, interpret, and generate natural language grounded in video data. These systems represent a convergence of computer vision and natural language processing, offering unified frameworks for tasks requiring spatial, temporal, and semantic reasoning over video. Recent advances have revealed both significant progress and notable challenges as the field develops architectures, methodologies, and evaluations tailored to the unique complexity of video content.

1. Foundational Architectures and Methodologies

Video LLMs employ heterogeneous architectural strategies depending on the modeling paradigm and the modality integration mechanism:

Modality Encoding and Fusion: The dominant class of architectures begins with a visual encoder (e.g., TimeSformer, CLIP-ViT, ViT-L/14) that processes frames or video clips into spatial–temporal feature sequences. These are often aggregated via attention pooling, Q-formers, spatial-temporal convolutions, or slow-fast pathways before being projected into the embedding space of the LLM. Approaches such as LaViLa (Zhao et al., 2022), VideoLLM (Chen et al., 2023), and Valley (Luo et al., 2023) modify frozen pre-trained LLMs with cross-attention modules or connectors, enabling the LLM to condition text generation on visual (and sometimes audio) tokens.
Sequence Reasoning with LLM Backbones: Video LLMs leverage the long-range sequence modeling and causal reasoning strengths of decoder-only LLMs (e.g., GPT-2, T5-Decoder, Vicuna-7B). They ingest unified token sequences comprising both language and visually encoded tokens. In VideoLLM (Chen et al., 2023), for example, a semantic translator projects video features to a compatible token format for autoregressive sequence modeling.
Spatial-Temporal and Multimodal Integration: Recent extensions, such as Video-LLaMA 2 (Cheng et al., 11 Jun 2024), incorporate spatial-temporal convolution (STC) connectors and jointly trained audio branches, enabling robust integration of visual and auditory cues. These design choices enhance the preservation of spatial-temporal order and enable effective cross-modal grounding.
Efficient Long Video Processing: For long-form videos, hierarchical token merging (LongVLM (Weng et al., 4 Apr 2024)), streaming memory encoding (VideoStreaming (Qian et al., 25 May 2024)), slow-fast two-stream fusion strategies (SlowFast-LLaVA-1.5 (Xu et al., 24 Mar 2025)), and attention-debiased token pruning (AdaTP (Sun et al., 26 May 2025)) are critical. These mechanisms address computational bottlenecks, balance fine detail with long-range context, and reduce unnecessary redundancy without degrading performance.

2. Training Strategies and Data Scalability

Modern Video LLMs are characterized by staged and multi-source training strategies:

Pre-Training: Models typically leverage large-scale, paired video–text corpora or video/image–caption datasets (e.g., Ego4D, Webvid-2M, CC595k, Valley-702k). Pre-training objectives center on contrastive learning, masked LLMing, or caption generation, allowing models to acquire robust cross-modal alignment and general visual understanding.
Pseudo-Narration and Data Augmentation: Systems like LaViLa (Zhao et al., 2022) address annotation scarcity by using auto-generated narrations, increasing the density and diversity of supervisory signals. Techniques include visually conditioned captioning (the “Narrator”), text-to-text rephrasing (the “Rephraser”), and prompt-based visual grounding augmentation (LLM4VG (Feng et al., 2023)). These approaches enhance temporal synchronization, coverage, and linguistic variation—key for robust downstream performance.
Instruction Tuning and Supervised Fine-Tuning: Datasets such as Valley-instruct-73k (Luo et al., 2023) and multimodal conversation datasets (e.g., Video-Chat, MiniGPT-4) are used to instruction-tune LLMs for better question answering, conversational reasoning, and follow-up responses grounded in video context.
Side-Channel and Multi-Modal Adaptation: Lightweight adapters (“patches”) such as those in PAVE (Liu et al., 25 Mar 2025) allow Video LLMs to be flexibly extended to handle novel modalities (audio, 3D, high frame-rate, multi-view), often by cross-attention fusion with negligible parameter overhead and without modifying the pre-trained backbone.

3. Performance, Efficiency, and Benchmarking

Video LLMs have demonstrated marked progress on standard multimodal benchmarks, with empirical evaluations spanning:

Model	Benchmark(s)	Standout Result
LaViLa (Zhao et al., 2022)	EGTEA, Epic-Kitchens-100	+10.1% EGTEA classification; +5.9% MIR (Epic100)
Valley (Luo et al., 2023)	MSVD-QA, MSRVTT-QA, ActivityNet-QA	SOTA in both short- and long-video QA tasks
LongVLM (Weng et al., 4 Apr 2024)	VideoChatGPT, ANET-QA, MSRVTT-QA, MSVD-QA	State-of-the-art on VideoChatGPT and VQA accuracy
VideoStreaming (Qian et al., 25 May 2024)	LongVideoBench, GQA, MLVU	Superior efficiency and accuracy for long videos
AdaTP (Sun et al., 26 May 2025)	VideoMME, LongVideoBench, MLVU	Up to 72.7% FLOPs reduction without loss in perf.
SF-LLaVA-1.5 (Xu et al., 24 Mar 2025)	LongVideoBench, MLVU	SOTA, including at 1B/3B model scales

These results emerge in both zero-shot and fine-tuned evaluations, with performance gains attributed to denser textual supervision, explicit temporal or spatial modeling, and more efficient token handling.

Model efficiency is a critical focus: token-efficient architectures and pruning methods enable Video LLMs to process extended or high-resolution content within tractable compute budgets, while modular adapters and patching support scalable adaptation to new tasks and devices.

4. Analysis of Internal Mechanisms and Interpretability

Recent interpretability studies provide insight into how Video-LLMs internally process multimodal content:

Two-Stage Processing: Empirical evidence demonstrates a division of labor among model layers: lower layers perform perceptual encoding (video feature extraction), while higher layers handle abstract, language-mediated reasoning (Gou et al., 21 Aug 2025).
Layer Contribution: Analysis by attention knockouts reveals that a small subset of intermediate layers ("critical outliers") disproportionately affect video question answering accuracy, while most other layers have minimal impact.
Attention Dynamics: Video-LLMs generally rely more heavily on language-to-video attention (cross-modal retrieval) than on intra-frame spatial or cross-frame temporal self-attention, despite the latter’s high computational cost. Disabling language-guided attention pathways in upper layers rapidly degrades performance, underscoring their importance for semantic grounding (Gou et al., 21 Aug 2025).
Efficiency Implications: These insights enable practical early exit and windowed attention strategies, substantially decreasing total attention FLOPs without significant accuracy loss.

5. Robustness, Failure Modes, and Safety

Although Video-LLMs have advanced open-ended reasoning, empirical studies have identified notable deficiencies:

Temporal Reasoning and Grounding: There is a persistent failure to correctly model temporal order, reason causally, or ground answers to the relevant video segments. Perturbation studies, such as Temporal Exchange and Temporal Description probes, induce significant accuracy drops (e.g., >30%) and high flip rates (>70%) across leading models (Xiao et al., 8 Aug 2024, Feng et al., 2023).
Over-Reliance on Language Priors: Video-LLMs commonly default to language priors, with prediction flips under paraphrased or slightly reworded questions, and insensitivity to visual perturbations (e.g., frame shuffling minimally impacts outputs; language changes greatly affect predictions) (Xiao et al., 8 Aug 2024).
Omission of Harmful Content: Design flaws such as sparse temporal sampling, aggressive token downsampling, and weak encoder-decoder fusion result in omission rates over 90% for harmful video content in various black-box attack settings (Cao et al., 14 Aug 2025). Content in unsampled frames or small spatial regions is often undetected.
Answerability and Oversharing: Without explicit alignment for answerability, models fail to refuse questions that exceed perceptual scope, producing speculative or hallucinated answers. Alignment frameworks train models to correctly respond “unanswerable” with explicit rationale, using custom metrics for excessive refusal, permissiveness, and discretion (Yoon et al., 7 Jul 2025).

6. Applications, Benchmarking, and Emerging Directions

Video LLMs have been evaluated on and applied to a breadth of real-world tasks:

Question Answering and Retrieval: Tasks include multiple-choice and open-ended video question answering (MC-VQA, OE-VQA), temporal or exact moment localization, and fine-grained segment retrieval, across benchmarks like VideoMME, LongVideoBench, ActivityNet-QA, CharadesSTA, and NExT-QA.
Captioning and Summarization: The use of LLMs to generate detailed, context-aware video descriptions and to summarize long videos via language-guided frame importance scoring has shown clear gains over visual-only approaches (Lee et al., 15 Apr 2025). The LLM-based summarization pipeline leverages both local and global context via in-context learning and self-attention.
Automated Creative Content Generation: Systems such as VC-LLM (Qian et al., 8 Apr 2025) demonstrate fully automated, multi-modal video advertisement generation, harnessing high-resolution spatial and temporal representations with robust hallucination reduction via supplementary text augmentation.
Adaptation and Extension: Adapters (“patches,” (Liu et al., 25 Mar 2025)) offer efficient, parameter-light pathways to leverage side-channel information (audio, 3D, multi-view), enabling significant multi-task gains without retraining or architecture modification.
Evaluation and Standardization: Unified evaluation frameworks that combine GPT-based scoring, precision/coverage metrics, and retrieval-based evaluation anchor progress and enable model comparisons (VLM-Eval (Li et al., 2023)).

7. Limitations and Prospective Research

Despite the progress, significant challenges remain:

Fine-Grained and Long-Range Reasoning: Current models have limitations in spatial-temporal granularity, especially for long videos; local details and event boundaries are sometimes lost in high-level pooling or token merging (Tang et al., 2023, Weng et al., 4 Apr 2024).
Robustness and Interpretability: Deficiencies in temporal reasoning, content grounding, and hallucination-prone response generation imply a need for improved architectures (e.g., memory-augmented LLMs, rationalized response chains) and more interpretable fusion mechanisms.
Safety and Semantic Coverage: Ensuring reliable detection and reporting of all salient (especially harmful) content requires denser and adaptive sampling, improved token compression, and decoding mechanisms that guarantee semantic alignment over speed-focused designs (Cao et al., 14 Aug 2025).
Task and Modal Diversity: Ongoing development is necessary to handle additional modalities (audio, 3D, multi-view), more complex temporal tasks (e.g., video grounding, anticipation), and open-domain human–computer interaction.
Resource and Implementation Standardization: Code repositories, training recipes, and modular benchmark datasets (e.g., https://github.com/yunlong10/Awesome-LLMs-for-Video-Understanding) play an increasingly crucial role in reproducibility and field-wide evaluation.

In sum, Video LLMs are emerging as core systems for open-ended, multi-granular, and multimodal video understanding. Through visually conditioned LLMs, efficient token processing, and instruction tuning, they offer unified frameworks for semantic video–language tasks. Despite significant advances, addressing robustness, fine-grained reasoning, and alignment for safety remains an active area of research.