Memory-V2V: Memory Mechanisms in V2V Systems

• Memory-V2V is a family of frameworks that leverages explicit memory caching, dynamic tokenization, and attention-based reasoning to improve video editing and V2V communication prediction.
• It employs efficient retrieval and compression techniques, such as learnable compressors and adaptive blending, to significantly reduce computational cost while maintaining cross-iteration consistency.
• Empirical evaluations show improvements like up to 3–10 dB MSE gains in CSI prediction and dramatic reductions in latency and memory usage in video-to-video translation tasks.

Memory-V2V refers to several state-of-the-art frameworks that explicitly exploit memory mechanisms for efficient and consistent processing in both video-to-video (V2V) synthesis/editing and multidimensional Vehicle-to-Vehicle (V2V) communication channel prediction. These frameworks introduce innovations in memory caching, adaptive tokenization, attention-based spatio-temporal reasoning, and model compression, targeting either generative video workflows or predictive learning for wireless communication. Their shared theme is leveraging explicit, learnable memory and redundancy reduction to improve cross-iteration consistency and computational efficiency.

1. Memory-Augmented Video-to-Video Diffusion Models

Memory-V2V (Lee et al., 22 Jan 2026) defines a memory-augmented architecture for multi-turn video editing tasks. Built on top of pretrained video-to-video diffusion transformers (e.g., ReCamMaster, LucyEdit), this approach addresses the problem of cross-iteration consistency: when users refine a video through multiple, interactive editing steps, standard pipelines often accumulate drifts in appearance, geometry, and motion. Memory-V2V maintains an external latent cache of all previously edited states and conditions each round of editing on a dynamically selected and compressed subset of these states.

The framework operates as follows:

• After each editing turn $j$, the output video $v_j \in \mathbb{R}^{F \times H \times W \times 3}$ is encoded via a pretrained VAE to a latent representation $E(v_j)$, which is stored in an external cache $\Omega$ along with per-frame camera extrinsics.
• At the next editing round $i$, the $k$ most relevant entries from $\Omega$ are retrieved by relevance scores (defined for view synthesis by geometric field-of-view overlap and containment, and for long-form editing by DINOv2 descriptor similarity).
• Retrieved latents undergo dynamic tokenization: the user-input video is tokenized at the finest stride, the top-3 retrieved videos at an intermediate stride, and less relevant videos at the coarsest stride. Tokenization is implemented by 3D convolutional encoders trained jointly with the base model.
• Within specified layers of the DiT backbone, a learnable compressor $C_\theta$ merges low-responsiveness tokens, with the merging process guided by frame-level responsiveness scores computed inside self-attention. The reduction ratio $r$ is sampled during training, and compressor parameters are jointly optimized.

Each new edit thus leverages a memory-rich, compressed representation of the editing history, enforcing strong consistency across iterations while keeping computational costs controlled.

2. Temporal Redundancy Reduction for Video-to-Video Translation

The Shortcut-V2V (Chung et al., 2023) framework, sometimes described as “Memory-V2V” in the literature, focuses on computational and memory efficiency for generic video-to-video translation models (e.g., vid2vid, RecycleGAN). These models typically process each frame independently through deep encoder-decoder networks, ignoring temporal redundancy. Shortcut-V2V inserts a learned shortcut block $S$ between chosen encoder ($\ell_e$) and decoder ($\ell_d$) layers:

• For frame $t=0$ or every $\alpha$ frames, full teacher inference is used, storing intermediate reference features.
• For other frames, only the encoder up to $\ell_e$ is executed; intermediate decoder features are approximated by $S$ using the previous frame's stored features. $S$ comprises a coarse-to-fine alignment (via deformable convolutions) and adaptive blending, formalized in the AdaBD block.
• AdaBD fuses the current encoder features with spatially warped reference features from the prior frame via learned masks and offsets.

This approach yields a $3.2$–$5.7\times$ reduction in MACs and $7.8$–$44\times$ reduction in memory across tested models, with only minor degradation in quality metrics (e.g., FVD) (Chung et al., 2023). The general principle is to exploit frame-to-frame similarity in video for both memory and computational savings.

3. Spatiotemporal Memory for 4D V2V Channel Prediction

In the domain of predictive wireless, Memory-V2V (Chu et al., 2024) refers to a context-conditioned spatiotemporal predictive learning framework for reliable 4D channel state information (CSI) prediction in vehicle-to-vehicle (V2V) communications. The problem is to forecast multidimensional channel states $H_t \in \mathbb{C}^{M \times N \times N}$ (with $M$ delay bins, $N \times N$ MIMO antennas) over future time steps given a historical window.

The architecture:

• Employs a causal ConvLSTM with both temporal and spatial memory tracks. Temporal memory $C_t^k$ and spatial memory $\mathcal{M}_t^k$ are updated via standard and spatial gate equations with convolutional operators, capturing temporal, delay-domain, and antenna correlations.
• Attention mechanisms are layered atop both memories: temporal attention (TA) focuses updates on the most informative delay-domain channels; spatiotemporal attention (STA, CBAM-style) adaptively prioritizes the spatial/angular domain.
• A Gradient Highway Unit (GHU) is inserted for efficient gradient propagation across long prediction horizons.
• Adaptation to new, unlabeled domains is realized by an adaptive meta-learning scheme that constructs “meta pseudo-labels” using a teacher-student setup, minimizing accumulative prediction error (APE) that commonly afflicts RNN-based sequence predictors.

Empirical results on 5.9 GHz V2V datasets show Memory-V2V achieves up to $3$–$10$ dB improvements in MSE over established ConvLSTM and ST-ConvLSTM baselines in challenging mobility scenarios, and robust adaptation to new geometries with minimal performance drop when using weighted meta pseudo-labeling (Chu et al., 2024).

4. Memory Mechanisms: Retrieval, Tokenization, and Compression

Central to Memory-V2V (video editing) (Lee et al., 22 Jan 2026) is sophisticated memory utilization. External memory caches store per-edit latent representations, indexed by view trajectory or semantic embedding. Retrieval strategies optimize relevance—not simply recency—using geometric overlap (“VideoFOV” for view synthesis) or DINOv2/CLIP-F embeddings (for content-based retrieval).

Dynamic tokenization assigns variable numbers of spatiotemporal tokens depending on video relevance, controlled via jointly trained 3D encoders. The learnable compressor operates at set depths of the DiT, guided by frame responsiveness computed in self-attention:

• For frames with low responsiveness ($R_t$ below threshold), their tokens are compressed via $C_\theta$ to prevent redundancy.
• Token merging is always favored over outright token dropping, preserving essential visual cues and avoiding qualitative loss.

This memory framework contrasts with pure FIFO or segmental reuse and is empirically validated to outperform such baselines in both consistency and efficiency.

5. Quantitative Evaluation and Comparative Analysis

The effectiveness of Memory-V2V stems from quantitative and qualitative improvements across tasks:

Task/Setting	Metric (Best Model)	Baseline(s)	Speedup/Memory Reduction
Multi-turn view synthesis	MEt3R: 0.1357 vs 0.1485 (lower=better), subject consistency: .9494	ReCamMaster (AR): 0.1485, .9483	≈34% latency reduction
Long video editing	Subject consistency .9326 vs .8683/.8737	LucyEdit (independent/FIFO)	DINO-F/CLIP-F improves >16%/>5%
V2V translation	FVD rise <0.02 at 3.2–5.7× compute cut	vid2vid, RecycleGAN, Fast-Vid2Vid	3.2–5.7× MACs, 7.8–44× memory cut
CSI prediction (wireless)	MSE improves 3–10 dB vs ST-ConvLSTM	ConvLSTM, ST-ConvLSTM	39M params, 0.66s inference (20f)

For video editing, dynamic tokenization alone yields >90% reduction in FLOPs/latency over naive full-resolution memory encoding; learned token merging adds ≈30% further speedup with negligible visual quality change (Lee et al., 22 Jan 2026). In Shortcut-V2V, framewise intermediate feature approximation via AdaBD yields dramatic reduction in parameter and activation memory—e.g., 365M→8.29M params (44×) for Edge→Face translation—at marginal generative quality penalty (Chung et al., 2023).

In V2V wireless prediction, explicit dual memory and meta-trained pseudo-labeling mitigate accumulative error, yielding robust future prediction even across urban/campus/highway geometries (Chu et al., 2024).

6. Research Context and Broader Implications

Memory-V2V as a family of approaches demonstrates the critical role of explicit memory, redundancy reduction, and adaptive resource allocation in scaling V2V workflows. In video-to-video synthesis, it enables user-interactive, iterative editing with strong cross-turn consistency and practical resource usage. In communication prediction, it allows spatiotemporally local channel modeling and rapid generalization to new domains, addressing key limitations of RNN-based predictors.

A significant finding is that retrieval-guided conditioning—whether using geometric, semantic, or attention-based signals—offers principled control over information propagation across long horizons, directly improving generation or prediction stability. Dynamic tokenization and learnable token compression highlight an emerging trend: compressing memory not by outright deletion but by relevance-guided merging, preserving critical cues while relieving computational bottlenecks.

The lesson shared across domains is that leveraging explicit, structured memory—via latent caches in video generation, feature recycling in translation, and spatiotemporal attention in sequence prediction—considerably advances the trade-off between quality, cross-step consistency, and resource cost. This suggests broader applicability to other domains where iterative refinement or temporal propagation is central.