Patch-wise & Token-based Processing

• Patch-wise and token-based processing are complementary methods that segment and represent input data into manageable units for efficient encoding across multiple modalities.
• Patch-wise processing divides data into fixed or adaptive patches essential for feature extraction in vision models, while token-based methods recast these patches into tokens for transformers.
• Integrating these paradigms enhances computational efficiency, scalability, and representational fidelity, making them valuable for tasks in vision, NLP, and multimodal AI.

Patch-wise and token-based processing are complementary approaches widely used to segment and represent input data for modern AI architectures. Patch-wise processing refers to partitioning inputs (such as images, videos, or time-series) into fixed or adaptive spatial/temporal regions (“patches”), each typically handled as an atomic unit for encoding, prediction, or downstream reasoning. Token-based processing, often used in transformer and LLM frameworks, recasts sequential or patch-embedded entities as tokens in a discrete or continuous representation space with flexible semantic or structural meaning. Recent research demonstrates that integrating patch-wise and token-based paradigms can yield superior computational efficiency, scalability, and representational fidelity across modalities.

1. Definitions and Core Concepts

Patch-wise processing divides data (images, videos, text, time-series) into fixed or adaptively sized segments, termed “patches.” These patches are treated as the basic unit for feature extraction, encoding, or prediction. In vision models, this often means forming non-overlapping (or overlapping) blocks (e.g., 16×16 image patches); in time-series, patches correspond to temporal segments.

Token-based processing, as found in transformers and LLMs, encodes input sequences—including patch embeddings—as discrete tokens. Each token may correspond to a word, subword, image patch, video patch, or other atomic semantic unit. These tokens serve as input to attention or autoregressive modules; their organization and relationship structure, along with any learned or engineered embedding, directly impacts the model’s capacity for compositional reasoning and long-range dependency modeling.

Patch-wise processing is foundational for vision transformers (ViTs) and multimodal models, where patches become image tokens. Token-based approaches in NLP, especially for morphologically complex or high redundancy domains, increasingly integrate patch-wise techniques (e.g., hybrid morphological tokenization, patch-level training) to improve vocabulary and computational efficiency.

2. Patch-wise Processing in Vision and Sequential Modalities

Patch-wise processing is ubiquitous in vision models. In ViT architectures, images are split into patches, and patch embeddings are constructed via linear projection, forming the full set of image tokens for transformer attention (Shi et al., 2021). Adaptive strategies such as dynamic patch merging (dCTS) create “superpatches,” merging neighboring low-variability patches to reduce token count and computational burden without compromising representational integrity (Szczepanski et al., 17 Sep 2025). Early pruning further accelerates inference by halting high-confidence supertokens during encoding.

For video, coordinate-based patch reconstruction via factorized triplanes enables memory-efficient encoding and training, as in CoordTok, which reconstructs patches from randomly sampled spatial-temporal coordinates rather than decoding full frames (Jang et al., 22 Nov 2024). In time-series anomaly detection, patches represent local segments, which are embedded and then projected as patch-wise tokens for input to LLMs (Yu et al., 31 Jul 2025).

Patch-wise partitioning is also exploited for efficient whole slide image (WSI) encoding in computational pathology: the WISE-FUSE framework selects diagnostically relevant patches using similarity scoring and knowledge distillation, drastically reducing encoding time and resource consumption (Shin et al., 20 Aug 2025).

3. Token-based Processing: NLP, Compression, and Unified Multimodal Models

Token-based approaches are central in natural language processing and multimodal reasoning. In hybrid tokenization frameworks, rule-based morphological analysis and statistical subword segmentation (BPE) combine to produce linguistically coherent tokens; phonological normalization and root-affix dictionaries map variant forms to shared IDs, reducing vocabulary redundancy while enhancing semantic integrity (Bayram et al., 19 Aug 2025).

Patch-level training for LLMs aggregates multiple tokens into high-density units (“patches”), allowing models to process shorter sequences during training and predict on aggregated representations—halving training cost for large models without degrading performance (Shao et al., 17 Jul 2024). After patch-level training, a fine-tuning stage at token granularity aligns the model with standard inference mode.

Multimodal LLMs increasingly employ unified token-based paradigms—PaDT introduces dynamically generated Visual Reference Tokens (VRTs), corresponding to image patches, and interleaves them with text tokens in the decoder output. This integrated patch-token approach enables dense prediction tasks (e.g., instance segmentation, open-vocabulary detection) that cannot be achieved through coordinate serialization (Su et al., 2 Oct 2025).

4. Compression, Masking, and Efficiency

Patch-wise and token-level approaches are leveraged for efficient compression and reduced inference or training time. Token-level correlation-guided compression adaptively samples the most informative image tokens by pattern redundancy measurement (patch-patch correlation) and CLS-patch attention; less informative or highly redundant tokens are dropped, with aggregation techniques used to maintain representation richness (Zhang et al., 19 Jul 2024).

Adversarial robustness and transferability are enhanced with learnable patch-wise masks, which prune model-specific regions during attack generation. The mask optimization via differential evolution uses feedback from simulated models to ensure preserved gradients are generic across models, leading to markedly improved transferability (Wei et al., 2023). Patch-wise adversarial removal leverages region-wise noise sensitivity to efficiently compress adversarial noise in ViTs (Shi et al., 2021).

Patch merging modules, such as PatchMerger, reduce transformer compute by learning to aggregate input tokens into a fixed smaller set for subsequent layers, with direct performance and efficiency benefits observed in both upstream and downstream tasks (Renggli et al., 2022). STEP applies both adaptive patch merging and token pruning to gain up to a 4× reduction in computational complexity with minimal accuracy loss at high resolutions (Szczepanski et al., 17 Sep 2025).

5. Applications Across Modalities and Tasks

Patch-wise and token-based architectures are deployed in a wide range of tasks:

• In video representation, PS-NeRV maps temporal and spatial patch coordinates into neural representations, achieving state-of-the-art video compression and inpainting results via efficient neural decoding and AdaIN-enhanced normalization (Bai et al., 2022).
• Satellite imagery road extraction employs patch-wise keypoint prediction and link probability estimation, constructing topologically accurate road graphs in a single network pass—substantially improving both accuracy and inference speed over prior pixel-based or iterative graph methods (Xie et al., 2023).
• Patch-wise auto-encoders reconstruct image subregions directly from spatially distributed feature vectors, enhancing anomaly sensitivity and delivering robust defect detection surpassing classic AE architectures (Cui et al., 2023).
• Patch-wise graph contrastive learning for image translation leverages a graph constructed from patch similarities to enforce topological consistency and semantic alignment between input and output images; node features are compared under a contrastive loss to maximize mutual information, achieving improved FID/KID benchmarks (Jung et al., 2023).

6. Architectural Integration and Training Strategies

Integration of patch-wise and token-based procedures is increasingly modular and adaptive:

• TriP-LLM employs a tri-branch architecture for time-series anomaly detection: patching branch extracts local features, selection branch emphasizes semantically relevant segments via attention, and global branch captures long-range context. Fused patch-wise tokens are processed by a frozen LLM, with a lightweight decoder reconstructing the input and evaluating anomalies via threshold-free metrics (Yu et al., 31 Jul 2025).
• In patch-wise CNN-based sentence compression, the input sequence is segmented into token-level features, passed through a U-Net inspired CNN with mixed kernel sizes to capture multi-gram context. Pooling and up-sampling blocks, skip connections, and token-wise softmax enable fast, expressive deletion modeling—outpacing RNN-based models in speed and matching performance when enriched with multilayer BERT embeddings (Hou et al., 2020).
• Adaptive patch selection, knowledge distillation, and cross-modal fusion (visual-text embedding) in WSI encoding integrate domain-specific VLMs and LLMs for scalable medical image analysis (Shin et al., 20 Aug 2025).
• Patch-level and token-level processing is foundational for efficient training, inference, grounding, and dense vision tasks, with modularity enabled by dynamic merging, selective compression, and unified codebook designs.

7. Comparative Analysis and Future Prospects

Patch-wise and token-based processing yield clear advantages in efficiency, scalability, and semantic fidelity. Adaptive patch selection, merging, and dynamic codebook expansion successfully mitigate the quadratic compute burden inherent in plain token approaches (e.g., ViT attention). Empirical studies across domains consistently report reduced FLOPs, lower memory usage, faster throughput, and maintain or exceed baseline performance metrics.

Misconceptions may arise regarding the trade-off between patch-wise abstraction and loss of fine-grained structure; results indicate that careful design (adaptive merging, multi-scale normalization, topological consistency enforcement) compensates for these risks, even enhancing robustness to adversarial perturbations, anomaly detection, and multimodal fusion. A plausible implication is that future scaling of LLMs, generative models, and perception systems across domains will increasingly rely on hybrid patch-token frameworks that balance context fidelity with computational tractability.

Patch-wise and token-based paradigms will continue to converge via dynamic, context-aware mechanisms—ranging from unified output decoding (PaDT) to efficient memory-saving architectures (STEP, TriP-LLM) and linguistically informed tokenization for cross-lingual NLP (hybrid tokenization). The ongoing release of open-source frameworks and empirical validation across modalities signals a modular and extensible future for high-performance AI systems leveraging finely tuned patch-token strategies.