Unified Tokenization Scheme

• Unified tokenization schemes are frameworks that convert multimodal inputs into tokens, balancing compression with semantic preservation.
• They rely on statistical guarantees like injectivity and exactness to ensure unambiguous decoding and efficient processing in large-scale models.
• By leveraging information bottleneck methods, these schemes enhance cross-modal alignment and boost performance on both understanding and generative tasks.

A unified tokenization scheme is a theoretically and practically grounded framework for mapping diverse structured inputs—such as text, images, audio, video, and multimodal signals—into a shared token space, suitable for joint processing in LLMs or multimodal architectures. The defining characteristic of such schemes is the principled, capacity-aware, and often information-theoretic selection of token representations that simultaneously preserve semantic content for interpretation (e.g., question-answering) and generative or reconstructive utility (e.g., image synthesis), all within the budget of a finite, shared vocabulary and downstream model interface (Tang et al., 2 Feb 2026). These schemes address the longstanding challenge of balancing compression with task relevance and facilitate joint optimization of understanding and generation.

1. Theoretical Foundations and Statistical Principles

Unified tokenization schemes are formalized via the lens of stochastic maps between source and token spaces. Specifically, the tokenization process is described as an encoder τ: Σ* ⇝ V* and a decoder κ: V* ⇝ Σ*, with Σ as the source alphabet (e.g., Unicode characters) and V as the token vocabulary. Crucial statistical desiderata include:

• Consistency and Exactness: For statistical consistency, the composition κ∘τ must be the identity on the data distribution p̂, i.e., κ∘τ(p̂) = p̂. Exact tokenizers, where κ∘τ= id_Σ*, guarantee full recoverability and avoid spurious ambiguity(Gastaldi et al., 2024).
• Injectivity and Determinism: Injective and deterministic tokenizers, such as BPE and WordPiece, ensure that distinct inputs always map to distinct token sequences, supporting unambiguous decoding and reliable parameter estimation.
• Multiplicativity, Trivial Kernel, and Boundedness: Efficient online decoding and encoding demand that τ and κ be multiplicative and of finite type—ensuring bounded lookahead and computability in linear time, suitable for autoregressive neural architectures.

Such frameworks generalize classical subword tokenizations and extend to stochastic and hierarchical schemes, providing formal guarantees for estimator consistency, bias-variance trade-offs, and ambiguity handling through marginalization(Gastaldi et al., 2024).

2. Information-Theoretic Capacity Control

Modern unified tokenization schemes increasingly adopt information-theoretic objectives to regulate what and how much information tokens carry under finite capacity. The core approach is the Information Bottleneck (IB) principle, which aims to:

• Compactness: Penalize I(Z;I), the mutual information between input and token set, enforcing compression and removing high-entropy, low-utility (e.g., pixel-level) details.
• Sufficiency: Maximize I(Z;Y), the mutual information between tokens and ground-truth targets, retaining semantically predictive structure necessary for downstream understanding and/or reconstruction.
• Alignment: Enforce I(Z;T), measuring cross-modal coherence (e.g., between visual tokens and text), typically via contrastive or InfoNCE objectives.

In the two-branch setting of InfoTok, these constraints are realized through lightweight projections onto understanding (I2T) and generation (T2I) branches, yielding a unified regularizer:

$$\mathcal{L}_\mathrm{InfoTok} = \mathcal{L}_{\mathrm{IB}}^{(u)} + \mathcal{L}_{\mathrm{IB}}^{(g)},$$

with each branch's loss incorporating compactness, sufficiency, and alignment. Hyperparameters (Lagrange multipliers) are tuned to control the compression-relevance–alignment tradeoff, typically reducing KL (compression) by 10–20% without decreasing sufficiency, while ensuring InfoTok regularization contributes 5–15% of the overall gradient norm (Tang et al., 2 Feb 2026).

3. Practical Instantiation and Integration in Large Models

Unified tokenization can be instantiated within various large language and multimodal models by integrating the information-regularized tokenization layer. In InfoTok, this is achieved through a VIB surrogate:

• Posterior $q_\phi(\tilde Z \mid I)$ is modeled as a diagonal Gaussian with fixed prior $r(\tilde Z)$, facilitating tractable computation of KL divergences.
• Sufficiency is lower-bounded via a variational decoder, and alignment is estimated through InfoNCE on the mean of posterior distributions.
• Two task-facing projections, $f_u$ and $f_g$, produce tokens specialized for understanding and generation, enabling per-task regularization during fine-tuning.

This method has been empirically validated by integrating InfoTok into three representative MLLMs—Harmon (continuous, trainable LLM), OpenUni (frozen VLM backbone), and Show-o2 (discrete, VQ-style tokenizer)—without altering architecture or training data. InfoTok components are used during training but removed at inference, ensuring no inference-time overhead (Tang et al., 2 Feb 2026).

4. Effects on Redundancy, Structure, and Cross-Task Performance

Practical analysis reveals that the unified, information-constrained tokenization mechanism actively reallocates representational budget away from noisy, high-frequency, and hard-to-exploit image details and toward reusable object structure, compositional layout, and semantically transferable cues. The result is:

• Suppression of textural noise and pixel-level redundancy through increased KL regularization.
• Enhanced retention of features predictive for both understanding (captions, QA) and generation (pixel reconstruction).
• Improved cross-modal alignment and stability in multimodal outputs due to alignment regularization.

Ablations demonstrate that compactness plus sufficiency already yield most gains, while explicit alignment further stabilizes cross-modal interactions. In low-capacity regimes (e.g., narrow discrete tokenizers), InfoTok shifts the budget to semantic structure; in high-capacity, continuous encoding, it prunes redundancy to boost perceptual quality(Tang et al., 2 Feb 2026).

5. Empirical Results and Benchmark Impact

Unified tokenization schemes such as InfoTok yield consistent performance improvements across both generative and discriminative tasks, as measured on large-scale multimodal benchmarks:

• Image Generation (FID↓, ImageNet val): Harmon’s FID improves from 14.4 to 12.0; Show-o2’s, from 13.2 to 11.4.
• Text-to-image Reasoning (GenEval Object Acc.): Harmon’s score improves from 0.74 to 0.85; Show-o2’s from 0.60 to 0.71.
• Understanding Benchmarks (MME QA, GQA, POPE, UniBench, etc.): Substantial improvements in both direct accuracy metrics and cross-modal CKA (visual–text dependence).

These gains are realized without expanding token length, increasing training data, or modifying underlying model architectures—the improvement is solely due to the regularization and reallocation of information at the token level. The approach generalizes across architectures and dataset scales due to its foundation in information bottleneck objectives(Tang et al., 2 Feb 2026).

6. Design Principles and Practical Guidelines

The synthesis emerging from unified tokenization research suggests several design principles for constructing practical, robust tokenizers for scalable multimodal and text-based LMs:

1. Determinism and Exactness: Whenever possible, employ deterministic, exact tokenization (BPE, WordPiece) to ensure unambiguous decoding, consistent estimator recovery, and linear-time streaming compatibility (Gastaldi et al., 2024).
2. Explicit Information Control: Use variational information bottleneck surrogates or mutual-information regularization to steer the allocation of representational capacity towards semantically rich, cross-task reusable information, avoiding redundant encoding of stochastic variability (Tang et al., 2 Feb 2026).
3. Per-Task Specialization with Unified Interface: Enable branch-specific projections and/or regularizers for different outputs (e.g., I2T vs. T2I) while maintaining a shared token interface to facilitate both understanding and generation.
4. Hyperparameterization and Budget Tuning: Tune compression (KL), sufficiency (predictive mutual information), and alignment (InfoNCE or contrastive objectives) on held-out data for robust, generalizable performance.
5. Avoiding Statistical Pathologies: Ensure injectivity, multiplicativity, trivial kernel, and bounded lookahead to support consistent, tractable, and efficient decoding (Gastaldi et al., 2024).

By adhering to these principles, unified tokenization modules provide a principled, model-agnostic foundation for end-to-end neural language and multimodal model pipelines, combining theoretical guarantees with practical empirical improvements across generation, understanding, and cross-task transfer.

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• InfoTok: "Regulating Information Flow for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs" (Tang et al., 2 Feb 2026)
• Theoretical Foundations: "The Foundations of Tokenization: Statistical and Computational Concerns" (Gastaldi et al., 2024)