Structure-Aware Tokenization and Mixing

• Structure-aware tokenization and mixing are advanced methods that retain intrinsic spatial, semantic, and morphological patterns during data preprocessing.
• They implement specialized grouping and encoding mechanisms to preserve local details while enabling efficient global context fusion in neural models.
• These methods improve performance across domains—vision, NLP, graphs, molecules, and 3D data—demonstrating gains in accuracy and computational efficiency.

Structure-aware tokenization and mixing refers to a family of methodologies in which latent or observable structure—spatial, semantic, morphological, topological, or otherwise—is systematically preserved and leveraged across the tokenization, encoding, and mixing (information fusion) stages of neural models. Unlike frequency-based or uniform chunking approaches, structure-aware designs encode intrinsic relationships within each token and across the token set, enabling models to exploit both local and global contextual dependencies more effectively. Structure-aware tokenization and mixing has gained prominence in vision, language, molecular, graph, and multimodal domains.

1. Foundational Principles of Structure-Aware Tokenization

Traditional tokenization methods, such as those employed in standard Vision Transformers (ViTs) for vision or BPE/WordPiece for NLP, typically flatten input data into 1D sequences of tokens, discarding or fragmenting local structure (spatial layout, morphology, semantics). This flattening leads to the loss of fine-grained relationships—such as the 2D spatial arrangement of image patches or the morpheme boundaries in agglutinative languages—potentially hindering downstream mixing and modeling stages.

Structure-aware tokenization introduces specific mechanisms to preserve and encode such local or hierarchical arrangements. In vision, this may involve dividing image patches into smaller spatial subpatches or maintaining 2D grid information within each token channel (e.g., via $(\alpha \times \alpha)$ grouping as in SWAT (Kahatapitiya et al., 2021)). In NLP and molecules, this might entail explicitly demarcating morpheme, syllable, or substructure boundaries instead of using frequency-only merges (Atuhurra et al., 26 Mar 2024, Bayram et al., 19 Aug 2025, Kim et al., 30 Aug 2025).

A key theoretical underpinning is that tokenization can be understood in formal, information-preserving terms. For instance, via stochastic maps and inverse homomorphisms, tokenization and detokenization can be designed to guarantee that structural properties (such as grammar or local adjacency) are preserved in the mapping between input and token space (Gastaldi et al., 16 Jul 2024, Geng et al., 4 Dec 2024).

2. Methodological Approaches Across Modalities

Vision

In models such as SWAT, images are first decomposed into a dense grid of subpatches, retaining the spatial arrangement within tokens. Specifically, for an image of shape $H \times W \times 3$ and patch size $p \times p$, instead of flattening, a convolutional kernel with smaller size $(p / \alpha) \times (p / \alpha)$ and stride $p/\alpha$ extracts overlapping subpatches, which are then grouped spatially and concatenated channel-wise. Each resulting token thus retains an embedded $2$D structure, summarized as $B \times N \times (c \times h \times w)$ where $h \times w$ encodes the preserved subpatch grid (Kahatapitiya et al., 2021).

NLP

Structure-aware tokenization in NLP can rely on linguistically-motivated splitting:

• Syllable tokenization, where words are split at vowel boundaries to yield tokens closely aligned with spoken or morphological units, as in Swahili (Atuhurra et al., 26 Mar 2024).
• Hybrid rule-based/statistical tokenization, where morphological analyzers segment words into roots and affixes (with phonological normalization and shared identifiers for variants), while statistical BPE covers out-of-vocabulary cases (Bayram et al., 19 Aug 2025).
• Token mixing strategies that align token boundaries with meaningful atomic units to preserve morphemes or characters, in sharp contrast to pure frequency-based BPE or Unigram LM (Zhang et al., 20 May 2025, Bayram et al., 19 Aug 2025).

Graph and Molecules

In dynamic graphs, structure-aware tokenization is realized by running localized message-passing neural networks (MPNNs) on dynamically patched subgraphs, with tokens encoding patch-specific temporal and structural information. These are then globally mixed via Transformer blocks with temporal positional encoding (Biparva et al., 2 Feb 2024).

Molecular structure-aware tokenization can employ SE(3)-invariant representations where 3D molecules are decomposed into chemically meaningful fragments. Each fragment’s local geometric context (expressed as spherical coordinates and rotation vectors relative to the molecule’s canonical frame) is encoded as part of the token (Fu et al., 19 Aug 2024). Contextual or motif-level tokenization, where motifs such as rings or functional groups are tokens, further exploits chemical semantics and enables subsequence-level importance weighting during training (Kim et al., 30 Aug 2025).

3D and Multimodal

In 3D scenes, tokenization adopts superpoint-based grouping—segmenting point clouds into geometrically consistent, semantically meaningful groups (superpoints)—with local coordinate normalization to ensure scale invariance. Such methods robustly preserve spatial and semantic detail across domains (Mei et al., 24 May 2025).

Content-aware vision tokenization in document understanding detects regions-of-interest (ROIs) and generates tokens only for those regions, preserving semantic structure while avoiding redundant splitting and computational waste. This pipeline maintains explicit spatial layout (by using both spatial and semantic token pools) and supports efficient, accurate information extraction (Nguyen et al., 13 Jul 2025).

3. Structure-Aware Mixing: Local and Global Fusion

Structure-aware mixing exploits intra-token structure and inter-token relations in information aggregation layers (attention, MLP blocks, convolutions):

• In image models, token mixing is enhanced by 2D convolutional operations parallel to linear projections within self-attention: tokens are reshaped into $c \times h \times w$, and local $2$D Conv kernels perform fine-grained mixing between subpatch structures. The analogous strategy is applied across tokens: after reshaping into a token layout grid, depthwise convolutions mix tokens within spatial neighborhoods, capturing locality beyond what non-structured MLPs or attention provide (Kahatapitiya et al., 2021).
• In graph architectures, local mixing is performed by repeated message passing within each patch (structure-aware tokenization), while global mixing is realized via Transformer layers with temporal positional encoding, alternating to mitigate over-smoothing and over-squashing (Biparva et al., 2 Feb 2024).
• Zero-shot document and multimodal models, such as VDInstruct, decouple spatial region detection from semantic mixing: content-rich regions are detected and tokenized according to localized modality-aware pooling, then fed into dual encoders specialized for either semantic or spatial layout (mixing of detected structures) (Nguyen et al., 13 Jul 2025).
• For semantic-aware text compression, tokenization incorporates clustering in embedding space and attention-based mechanisms for dynamically adjusting granularity, ensuring dense, semantically rich spans are fine-grained while repetitive regions are coarsely represented (Liu et al., 21 Aug 2025).

4. Statistical, Computational, and Theoretical Underpinnings

A rigorous mathematical framework models tokenization as a pair of stochastic maps $(\tau, \kappa)$, with $\tau$ encoding text into tokens and $\kappa$ decoding tokens back to text. Necessary and sufficient conditions for estimator consistency require that $\kappa \circ \tau$ preserves the original distribution $p^{\infty} = p^{\infty} \circ (\kappa \circ \tau)$ (Gastaldi et al., 16 Jul 2024). The category-theoretic perspective enables clear distinctions between injectivity, ambiguity, exactness, and regularization in tokenization schemes.

In addition, the inverse homomorphic property formalizes how detokenization restores original sequence structure: for any token sequences $n_1$, $n_2$, $$f_{\rm detok}(n_1 \circ n_2) = f_{\rm detok}(n_1) \circ f_{\rm detok}(n_2)$$. Therefore, properly designed tokenization does not impact the recognition of context-free or regular languages by neural architectures (Geng et al., 4 Dec 2024).

These theoretical guarantees support the development of structure-aware mixing, where tokens can be rearranged, merged, or reweighted without loss of underlying structure if proper (unambiguous) tokenization is preserved.

5. Performance Implications and Empirical Findings

Experiments demonstrate that integrating structure-aware tokenization and mixing generally yields substantial empirical benefits:

• In computer vision, SWAT achieved up to +3.5% accuracy improvement on ImageNet-1K and +0.7 mIoU improvement on ADE20K (segmentation) compared to standard flattening baselines, with negligible extra computational overhead (Kahatapitiya et al., 2021).
• Hybrid morphologically-informed tokenizers in Turkish yielded 90.3% Turkish Token Percentage and 85.8% Pure Token Percentage, exceeding LLaMA, Gemma, and GPT tokenizers, and correlated strongly ($r=0.90$) with downstream MMLU performance (Bayram et al., 10 Feb 2025, Bayram et al., 19 Aug 2025).
• In text-to-molecule generation, substructure-level tokenization (CAMT5) achieved comparable or better generation scores using only 2% as many tokens as atom-level baselines, demonstrating the expressive and computational benefits of motif-based structuring (Kim et al., 30 Aug 2025).
• For log-compressed semantic tokenization (SemToken), token counts were reduced by $2.4\times$ with a $1.9\times$ speedup and negligible perplexity loss (Liu et al., 21 Aug 2025).
• In symbolic language reasoning, aligning tokenization with computational atoms (e.g., digits, characters) enabled dramatically higher accuracy and allowed smaller models to outperform larger systems otherwise hampered by over-merged, coarse-grained tokenization (Zhang et al., 20 May 2025).
• In 3D understanding, superpoint + scale-invariant grouping led to +9–12 mIoU gains over baseline kNN tokenizers in cross-domain segmentation (Mei et al., 24 May 2025).
• In protein structure modeling, structure tokenization with improved codebook utilization (AminoAseed) offered +6.31% average performance improvement across 24 supervised structure tasks and 124% higher codebook utilization (Yuan et al., 28 Feb 2025).

6. Challenges, Limitations, and Directions for Future Research

Despite clear efficacy, substantial challenges remain:

• Tokenization is inherently lossy and often irreversible; many pre-processing steps (normalization, segmentation) can obliterate fine-grained structure, with residual ambiguity or inconsistency (Mielke et al., 2021, Gastaldi et al., 16 Jul 2024).
• Designing general-purpose, task-agnostic, and language-universal structure-aware tokenizers is likely infeasible due to intense language and domain dependence (Mielke et al., 2021, Bayram et al., 10 Feb 2025).
• Overly aggressive or coarse tokenization may erase crucial atomic units, deeply constraining symbolic reasoning and preventing generalization even with sophisticated decoding or external computation (Zhang et al., 20 May 2025, Chai et al., 17 Jun 2024).
• Balancing vocabulary size and coverage, computational tractability, and preservation of structure—especially in agglutinative languages or complex chemical/graph data—remains a difficult optimization (Lim et al., 8 Jan 2025, Bayram et al., 19 Aug 2025).
• In practice, open questions remain as to optimizing for both estimator consistency (theoretical fidelity) and practical neural efficiency or interpretability, especially as models scale and hybridize across modalities (Gastaldi et al., 16 Jul 2024, Fu et al., 19 Aug 2024).

Research in this domain is advancing toward dynamic, context- or structure-aware tokenization schemes that can adapt granularity based on semantic entropy or structural density (Liu et al., 21 Aug 2025), integrate learning of tokenization within end-to-end pipelines (Mielke et al., 2021), or yield representations invariant under transformations (e.g., SE(3)) for chemistry and 3D (Fu et al., 19 Aug 2024, Mei et al., 24 May 2025).

7. Summary Table: Core Structure-Aware Tokenization Techniques

Domain	Structure-Aware Tokenization Design	Structure-Aware Mixing/Fusion
Vision (SWAT)	Subpatch grouping, 2D structure per token	Convolutional token/channel mixing
NLP	Morpheme-, syllable-, hybrid semantic tokens	Token alignment, entropy-driven fusion
Graphs	Patchified subgraph, MPNN tokenization	Local MPNN + global Transformer fusion
Molecules	Fragment-level, SE(3)-invariant motif tokens	Cross-attention with protein embedding
3D Point Clouds	Superpoint grouping, scale normalization	Feature propagation, 2D/3D distillation
Documents	ROI-aware content tokenization	Dual (spatial/semantic) encoder fusion

These approaches demonstrate that structure-aware tokenization and mixing methods operate under the principle of preserving, leveraging, and fusing task- and modality-specific organization within the tokenized representation, yielding notable gains in efficiency, expressiveness, and generalization. The field continues to explore new architectures in which structural biases are aligned at every stage of the modeling pipeline.