Codec Primitives for Token-Efficient Video Modeling

• The paper introduces codec primitives that compress video tokens using a unified rate–distortion–semantic framework to balance token efficiency and semantic fidelity.
• It leverages both training-free methods (like patch clustering and motion-based grouping) and learnable modules for adaptive, query-aware token allocation.
• The work demonstrates practical reductions in token count and compute via spatiotemporal merging, dynamic rate control, and entropy-based token selection.

Codec primitives for token-efficient video language modeling are algorithmic building blocks—deriving from classical video coding theory and modern deep learning—that transform, discretize, and compress frame-wise visual token representations to enable efficient ingestion and reasoning by LLMs over long video sequences. These primitives span hand-designed, training-free algorithms (such as patch clustering, saliency-guided selection, and motion-based grouping) as well as learnable, reconstructive coding modules. Their design aims to maximize semantic information retention under strict token and compute budgets, enabling large-context video-language understanding with bounded complexity.

1. Unified Theoretical Foundations of Codec Primitives

The formalization of codec primitives in video language modeling is grounded in the unification of classical rate–distortion theory and the information bottleneck principle, extended here with an explicit semantic loss term to align with downstream video–language tasks such as video QA or captioning (Jin et al., 28 Jan 2026). This generalized objective is represented as:

$\mathcal{L} = R + \lambda D + \gamma S$

where

• $R$ is the rate (token or bit budget, e.g., $I(X;Z)$),
• $D$ is pixel- or feature-level distortion ($\mathbb{E}[d_{pixel}(X, g(Z))]$), and
• $S$ is semantic loss, directly measuring task performance ($\mathbb{E}[\ell_{task}(Y, h(Z))]$), with Lagrange multipliers $\lambda, \gamma$ tuning the trade-off.

Under this framework, the central challenge is to identify primitives that compress the visual sequence $X$ into a token sequence $Z$ of length $K \le K_{max}$, such that semantic fidelity $S$ is minimally impaired while the rate $R$ is strictly controlled.

2. Transform, Quantization, and Entropy Coding as Token Operations

Codec primitives can be explicitly mapped onto three computational operations in the video tokenization pipeline (Jin et al., 28 Jan 2026):

• Transform coding: A learnable or fixed mapping (e.g., ViT patchification, linear projection, or convolutional encoding) that decorrelates spatial and temporal visual features to produce compact, high-variance representations.
• Quantization/discretization: Vector quantization assigns high-dimensional features to codebook indices, often via $K$-means or learned codebooks, reducing a continuous space to a compact, symbolic sequence. E.g., Token Dynamics (Zhang et al., 21 Mar 2025) clusters the $n$ frame–patch tokens into $K \ll n$ token centroids for extreme compression.
• Entropy coding/probabilistic modeling: Token sequences may be further compressed under entropy-based criteria, with token selections guided by their information density or predicted marginal probability. The probability $p(z_i|context)$ under an autoregressive prior enables entropy-aware rate allocation.

This operational mapping establishes the basis for both training-free and learnable codec modules that can be adapted to the specifics of visual token representations required by transformer-based LLMs.

3. Algorithmic Realizations of Token-Efficient Codec Primitives

Recent literature describes a diverse set of primitive algorithms for compressing video token streams prior to or within LLM backbones:

(A) Spatiotemporal Merging and Pruning

Approaches such as PruneVid (Huang et al., 2024) apply a two-stage codec: first, spatial–temporal token clustering and merging that exploits both static–dynamic splits and token affinities; second, attention-guided pruning, where LLM cross-attention identifies and retains only tokens most relevant for the task prompt. The formal pipeline is:

• Temporally segment frames by feature similarity.
• Split each frame’s tokens into static and dynamic sets.
• Merge temporally redundant static tokens and spatially cluster remaining tokens.
• Prune visual tokens via maximal cross-attention with question tokens.

(B) Event-Aware and Hierarchical Compression

METok (Wang et al., 3 Jun 2025) introduces multi-stage event-based compression:

• Event-aware reduction leverages frame-to-frame cosine similarity for temporal segmentation, semantic alignment with text prompts to prioritize key events and frames, and adaptively pools spatial features by event importance.
• Hierarchical token pruning in prefilling applies retention rules at transformer layers, adapting pruning ratios by event and key status.
• KV cache optimization discards visual memory beyond critical transformer depths.

(C) Motion-Primitives and Codec-Aware Encoding

CoPE-VideoLM (Sarkar et al., 13 Feb 2026) uses video codec primitives—motion vectors (τ) and residuals (δ) from standard P-frame coding—to synthesize token representations for non-keyframes ("Δ-tokens").

• Lightweight transformer encoders process (τ, δ) into a small number of tokens per P-frame, drastically reducing the token count compared to full-frame RGB encoding.
• Pre-training aligns Δ-token embeddings with standard RGB-token representations using patchwise alignment loss.

(D) Tree-based and Diversity Primitives

FlashVID (Fan et al., 8 Feb 2026) chains frame-wise selection (ADTS) maximizing the diversity and attention-weighted informativeness of tokens, with a spatiotemporal token merging tree (TSTM) that hierarchically fuses temporally redundant tokens based on inter-frame similarity.

(E) Dynamic Rate Control and Query-aware Allocation

DyToK (Li et al., 7 Dec 2025) introduces a dynamic, query-conditioned rate control primitive by extracting a keyframe prior from LLM attention maps, then allocating variable per-frame token retention ratios according to query-conditioned saliency, enforced via existing compression methods (e.g., VisionZip, FastV).

(F) Reconstructive and Learnable Compression

Video-XL-Pro’s ReCoT (Liu et al., 24 Mar 2025) tightly couples a self-supervised, reconstructive encoder–decoder (built from a Dynamic Token Synthesizer and Semantic-Guided Masking) with a Query-aware Selector that prunes compressed tokens depending on prompt relevance, providing an adaptive, semantics-preserving token budget.

Primitive Type	Key Operation	Example
Spatiotemporal merge/prune	Static–dynamic split, clustering	PruneVid (Huang et al., 2024)
Event-aware comp.	Cosine-based segmentation	METok (Wang et al., 3 Jun 2025)
Motion-primitive	Motion vector encoder	CoPE (Sarkar et al., 13 Feb 2026)
Tree-based merge	Framewise diversity + tree grouping	FlashVID (Fan et al., 8 Feb 2026)
Dynamic rate ctrl.	Attention-derived token budgets	DyToK (Li et al., 7 Dec 2025)
Reconstructive	MAE-style, semantic masking	ReCoT (Liu et al., 24 Mar 2025)

4. Rate–Distortion–Semantic Trade-off and Compression-Accuracy Analysis

The design and evaluation of codec primitives center on the $R$–$D$–$S$ Pareto frontier, where aggressive reductions in rate (token count) must be balanced against both low-level distortion and semantic utility. Several empirical findings across methods include:

• PruneVid achieves token retention ratios as low as 16.2% with negligible loss in QA accuracy and a $0.23\times$ FLOPs reduction (Huang et al., 2024).
• METok reports 80.6% FLOPs reduction and 93.5% KV cache savings at equivalent or improved accuracy vs. base LongVA-7B (Wang et al., 3 Jun 2025).
• FlashVID demonstrates that retaining only 10% of vision tokens preserves $>99\%$ of baseline model accuracy on LLaVA-OneVision, with a $6\times$ prefill and $2\times$ TTFT speedup (Fan et al., 8 Feb 2026).
• Token Dynamics exemplifies “extreme short token reduction” to 0.07% of original tokens with only a 1.13% absolute performance loss on NextQA-MC (Zhang et al., 21 Mar 2025).
• Video-XL-Pro, through stacked reconstructive and query-selective coding, permits processing $8\,$K+ frames at practical efficiency, delivering up to 64$\times$ reduction with improved accuracy on MLVU (Liu et al., 24 Mar 2025).

Empirical ablations consistently reveal the importance of token selection guided by semantic or cross-modal relevance, as opposed to uniform random or spatially naive pooling.

5. Integration Architectures and Deployment Considerations

Codec primitives are integrated as modular preprocessing stages (prior to the LLM), intermediate pruning layers (e.g., within transformer depth), or as plug-and-play modules requiring minimal retraining. Noteworthy architectural points:

• Primitives are compatible with standard ViT–LLM pipelines, generally requiring only access to vision encoder outputs and/or LLM cross-attention activations.
• Streaming and causal scenarios (e.g., StreamingTOM (Chen et al., 21 Oct 2025)) necessitate strictly per-frame, non-anticipatory compression (Causal Temporal Reduction) and bounded-memory post-processing (Online Quantized Memory).
• Codec-aware encoding (e.g., CoPE-VideoLM) benefits from pre-alignment between motion/residual representations and the LLM’s internal embedding space, accelerating convergence and improving motion sensitivity.

Deployment guidance emphasizes that:

• Compression ratios and per-frame token budgets are highly tunable and should be validated against semantic performance metrics for the target application.
• For motion-sensitive tasks, including explicit temporal cues—either via codec-derived primitives or dynamic attention—yields significant gains.
• Primitives benefit from adaptive, event- or query-aware schedules (DyToK), and are compatible with both fixed- and variable-rate regimes.

6. Advancements, Theoretical Insights, and Future Directions

Recent research has revealed several guiding principles and avenues for advancement (Jin et al., 28 Jan 2026, Wang et al., 3 Jun 2025, Li et al., 7 Dec 2025):

• Decorrelating features prior to quantization (i.e., transform coding) sharply reduces redundancy and enhances compressibility.
• Entropy- and information-density-aware token selection maximizes the rate–semantic efficiency, especially when dynamically adjusted per query.
• Hierarchical, event-aware, and spatiotemporal-aware merging schemes demonstrably outperform naive spatial or temporal only approaches.
• Hybrid discrete/continuous coding (mixing vector quantization with residual correction) enables high compression with high semantic retention.
• Learned, task-adaptive proxies for “rate control” (via RL or attention-based scheduling) promise further gains, as shown by CaCoVID (Ma et al., 2 Feb 2026).
• There is active exploration into standardizing a general token technology for AI, analogous to MPEG/H.264 video standards, blending codec and LLM architectures.

The field continues to expand toward unifying codec and transformer design, aiming for universally efficient, semantics-preserving token representations deployable on massive, variable-length videos. Implementations increasingly leverage modularity, task-driven supervision, and cross-modal feedback to push the frontier of token-efficient video language understanding.