Video-LMMs: Advanced Multimodal Video Models

• Video-LMMs are advanced systems that integrate vision encoders and autoregressive language models to perform spatiotemporal reasoning, editing, and retrieval on video data.
• They leverage innovations such as spatiotemporal feature projection, plug-and-play temporal modules, and unified interleaved input techniques to fuse visual, audio, and textual signals.
• Recent techniques including supervised fine-tuning, reinforcement learning, and test-time scaling have improved robustness, temporal localization, and pixel-level grounding.

Video-Large Multimodal Models (Video-LMMs) are systems that integrate video perception modules with large-scale autoregressive LLMs to enable sophisticated spatiotemporal reasoning, description, retrieval, editing, and assessment of complex video data. They extend the capabilities of image-based LMMs by addressing unique challenges inherent in video—such as temporal dynamics, object tracking, multimodal fusion (including audio), and global context comprehension over potentially long sequences. Recent advancements have led to a proliferation of foundational architectures, datasets, training protocols, post-training mechanisms, and application-specific benchmarks, which collectively define this rapidly evolving field.

1. Architectural Innovations and Multimodal Fusion

The core of a Video-LMM is typically a high-capacity vision encoder (often ViT or CLIP-derived) capable of processing video as a sequence of frames, a temporal aggregation and resampling pipeline, cross-modal projection layers, and an LLM backbone for reasoning and response generation. Key architectural advances include:

• Spatiotemporal Feature Projection: Feature extraction pipelines now routinely leverage frame-level embeddings, spatiotemporal pooling, and projection MLPs to transform raw input $V_i \in \mathbb{R}^{T \times H \times W \times C}$ into token representations $Q_v$ aligned with $Q_t$ text tokens, subsequently fused and processed by the LLM (Munasinghe et al., 2023, Huang et al., 18 Apr 2024).
• Plug-and-Play Temporal Modules: Systems such as RED-VILLM introduce lightweight temporal adaptation components to plug into established image LLM backbones, allowing for the retention of strong spatial alignment with minimal modification. The resulting tokens $Q_v = [Q_t, Q_z]$ preserve both temporal and spatial cues (Huang et al., 18 Apr 2024).
• Unified Interleaved Input: Methods like LLaVA-NeXT-Interleave propose treating a video as an interleaved stream of text and frame tokens, $X = [T_1, I_1, ..., T_N, I_N]$, which generalizes multimodal reasoning and enables transfer across single-image, multi-image, and video scenarios (Li et al., 10 Jul 2024).
• Audio and Multimodal Integration: Contemporary models incorporate a full audio pipeline—using VAD for segment extraction and ASR models such as Whisper for transcript generation—followed by fusion with visual features to enrich contextual reasoning, especially for temporal or dialog-centric tasks (Munasinghe et al., 2023, Team et al., 22 Apr 2025).
• Token Compression and Dynamic Recognition: The Quicksviewer model partitions videos into nonuniform "cubes" via semantic change detection and Gumbel Softmax, then resamples these using a unified 3D positional encoder, attaining drastically improved efficiency for long videos (Qi et al., 21 Apr 2025).

2. Training Paradigms and Post-Training Methodologies

Once the base multimodal architecture is established, Video-LMMs undergo training and refinement with increasingly sophisticated supervision:

• Supervised Fine-Tuning (SFT) with Chain of Thought (CoT): SFT is typically performed on curated video-text datasets, often using a structured reasoning signal format (> , <answer>) to induce stepwise, interpretable reasoning. Visual evidence is bound to reasoning steps via timestamps, keyframes, or spatial indices (Tang et al., 6 Oct 2025). > > - Reinforcement Learning with Verifiable Objectives: Algorithms such as PPO, DPO, and especially Group Relative Policy Optimization (GRPO) introduce objective, verifiable supervision aligned with answer correctness, temporal localization (tIoU), and region IoU. The DPO objective is formulated as: > > $$L_{\text{DPO}}(\pi_\theta; \pi_{\text{ref}}) = -\mathbb{E}_{(V, x, y_w, y_l) \sim \mathcal{D}_{\text{DPO}}} \left[ \log \sigma \left( \beta \log \frac{\pi_\theta(y_w | x, V)}{\pi_{\text{ref}}(y_w | x, V)} - \beta \log \frac{\pi_\theta(y_l | x, V)}{\pi_{\text{ref}}(y_l | x, V)} \right) \right]$$ > > where $\sigma$ is the logistic function and $\beta$ is a scaling parameter (Zhang et al., 1 Apr 2024, Tang et al., 6 Oct 2025). > > - Test-Time Scaling (TTS) and Enhanced Inference: Techniques such as beam search, video-specific CoT prompting (e.g., Video-of-Thought), self-consistency decoding, and confidence-based iterative refinement increase computational allocation at inference for better answer reliability. Dynamic tool invocation (e.g., object detectors, frame selectors) is emerging as a test-time extension (Tang et al., 6 Oct 2025). > > ## 3. Grounding, Localization, and Spatiotemporal Reasoning > > Advancements in Video-LMMs focus not only on comprehension but also on pixel-level grounding and spatiotemporal precision: > > - Pixel-Level Grounding and Tracking: Systems like PG-Video-LLaVA perform multi-step noun phrase extraction, scene segmentation, and matching visual tags with segmentation masks and tracking IDs using ensembles of GroundingDINO, DEVA Tracker, and SAM. Intersection over Union (IoU) metrics evaluate spatial grounding performance (Munasinghe et al., 2023). > > - Temporal Localization & Retrieval: Specialized architectures (such as Vidi) leverage decomposed attention mechanisms—balancing visual, auditory, and textual modalities with fixed coefficients—along with efficient diagonalized V2V self-attention to enable localization of temporal video segments in multi-hour videos. Precision is quantified by intersection-over-union metrics over predicted and true time ranges (Team et al., 22 Apr 2025). > > - Multi-Modal Scene and Character Reasoning: Unified frameworks process both raw frames and extracted audio features, fusing them early in the pipeline. This approach is critical for questions demanding recognition of temporal sequences, action continuity, and emotional or social context (Khattak et al., 6 May 2024, Sun et al., 4 Aug 2025). > > ## 4. Evaluation Benchmarks, Datasets, and Metrics > > Video-LMMs are now assessed against a wide and increasingly specialized set of benchmarks: > > | Benchmark/Dataset | Primary Focus / Unique Features | Example Metrics | > |---------------------|--------------------------------------------------------------------|-------------------------------| > | ActivityNet-QA, MSVD-QA, MSRVTT-QA, TGIF-QA | Zero-shot video QA, temporal reasoning | Accuracy, 1–5 human scoring | > | VidSTG, HC-STVG | Pixel-level spatial grounding, IoU | Intersection over Union (IoU) | > | InfiniBench | Very long video understanding, movies/TV, 9 reasoning skills | MC accuracy, open-ended GPT-4 score | > | CVRR-ES | Complex reasoning, robustness, real-world prompt ambiguity | 11-dim average accuracy | > | VANE-Bench | AI-generated & real anomaly detection/localization | VQA accuracy | > | S2I-Bench, Q-Bench-Video | Video quality scoring and justification, AIGC artifacts | SRCC, PLCC, VCG, open QA | > | ViMUL-Bench | Multilingual/cultural QA, 14 languages, 8 cultural domains | Multilingual accuracy | > > Performance gaps remain substantial between even the best open-source models and closed-source systems (e.g., GPT-4V, Gemini-1.5 Pro), especially in the domains of long video reasoning, fine-grained anomaly detection, open-ended video quality explanation, and multilingual/cultural context understanding (Bharadwaj et al., 14 Jun 2024, Shafique et al., 8 Jun 2025). > > ## 5. Addressing Hallucination, Robustness, and User-Centric Evaluation > > Reliability and factuality in Video-LMMs are major research foci, with several benchmarking and mitigation strategies proposed: > > - Benchmarking Hallucinations: HAVEN evaluates hallucinations along causes (prior-knowledge conflicts, in-context contradiction, capability gaps), aspects (object, scene, event hallucination), and question formats. Models benefit from chain-of-thought reasoning and fine-tuning with ground-truth-corrected rationales (SRFT + TDPO), reducing error rates by over 7% (Gao et al., 25 Mar 2025). > > - Robustness and Prompt Sensitivity: Models frequently display over-affirmative behavior—responding "yes" even to misleading prompts—signalizing a need for negative training examples and advanced prompting (e.g., Dual-Step Contextual Prompting) for self-rectification (Khattak et al., 6 May 2024). > > - User-Centric and Scalably Automated Evaluation: Automatic, open-ended evaluation protocols such as VideoAutoArena use persona-based user simulation, adaptive complexity escalation, and ELO rating, closely tracking human judgments for continuous and cost-effective model ranking (Luo et al., 20 Nov 2024). This approach reveals subtle context and background deficiencies missed by conventional MC evaluation. > > ## 6. Domain-Specific Applications and Advancements > > Targeted Video-LMM applications are rapidly emerging: > > - Video Coding and Compression: CMVC leverages LMMs to encode keyframes and motion as text/image surrogates, enabling ultra-low bitrate video coding using either text-text-video (TT2V) or image-text-video (IT2V) decoding modes. Frame interpolation employs LoRA-tuned diffusion UNet modules (Zhang et al., 15 Aug 2024). > > - Lecture and Discipline Reasoning: Video-MMLU focuses on multi-discipline lecture QA (notably mathematics), requiring both precise perceptual OCR/tokenization and complex symbolic reasoning. Performance is highly sensitive to visual token detail (16–300/frame is optimal) and backbone LLM capabilities (Song et al., 20 Apr 2025). > > - Editing and Animation: Vidi achieves high-precision temporal retrieval for video editing by exploiting decomposed multimodal attention and efficient token scaling, significantly outperforming closed-source competitors. Anim-Director demonstrates LMM-driven controllable video generation, integrating external creative tools autonomously via pipeline-driven self-reflection and consistent visual reasoning (Team et al., 22 Apr 2025, Li et al., 19 Aug 2024). > > - Multilingual and Culturally Informed Reasoning: ViMUL introduces both training and evaluation resources spanning 14 languages and culturally rich domains. Training is augmented by high-quality translation and cycle consistency checks, achieving improved performance on both high- and low-resource languages (Shafique et al., 8 Jun 2025). > > ## 7. Future Challenges and Research Directions > > Persistent technical challenges and new research opportunities shape the agenda: > > - Reward Function Robustness: Crafting reward schemes that generalize across tasks and prevent perverse incentives during RL remains open; reward density, verifiability, and capability for process-based shaping are under active exploration (Tang et al., 6 Oct 2025). > > - Scaling and Efficiency: Token and cube-based dynamic compression strategies (e.g., Quicksviewer) are pivotal for long-context processing at practical costs; further work is required to maintain robustness and fine-grained reasoning as context and sequence length scales increase (Qi et al., 21 Apr 2025). > > - Benchmark Evolution: Newly released and continually expanding datasets (e.g., VUE-TR, S2I, ViMUL-Bench, InfiniBench) are refining how Video-LMMs are assessed, with a trend toward emphasizing open-ended, multilingual, long-form, high-fidelity, and culturally diverse capabilities (Ataallah et al., 28 Jun 2024, Xie et al., 26 Jun 2025). > > - Hallucination and Factuality: "Video-thinking" models combining supervised CoT reasoning with direct preference optimization show promise; yet fully overcoming hallucination, especially under adversarial prompt and long-context scenarios, is unresolved (Gao et al., 25 Mar 2025). > > - Integration of External Tools and Inference-Time Strategies: The rise of tool-augmented inference—dynamic invocation of object detectors, segmenters, or external databases—marks a distinct direction for enhancing efficiency and reliability without entailing full model retraining (Tang et al., 6 Oct 2025). > > In summary, Video-LMMs have progressed from simple frame-wise alignment toward sophisticated systems capable of pixel-level grounding, long-context temporal reasoning, robust multimodal fusion, open-ended and multilingual understanding, and high-efficiency inference. Continued research across architecture, training, robustness, efficiency, and community benchmarks is essential for approaching human-level video reasoning and actionable deployment in diverse real-world scenarios.