Unified Token Sequence Framework

• Unified token sequence is a framework that represents diverse data types and tasks as token sequences, unifying fixed and learned token mixing strategies.
• The approach enables scalable long-range dependency modeling using distributed attention and adaptive token compression, overcoming quadratic complexity.
• It integrates cross-modal and multi-task learning in domains such as vision-language, protein modeling, and reinforcement learning through consistent tokenization.

A unified token sequence refers to a representational framework or computational paradigm in which disparate data types, tasks, modalities, or supervisory signals are consistently encoded, processed, and predicted as sequences of tokens. This concept traverses architecture design, algorithmic formulation, and scalability strategies for neural models—particularly in contexts requiring long-range dependency modeling, multi-task learning, multimodal fusion, and efficient computation.

1. Mathematical Unification of Token Mixing

The unified token sequence framework finds its theoretical underpinning in the general abstraction of token mixing. In long-sequence modeling, the transformation of input tokens $X \in \mathcal{X}$ to output representations $\tilde{\mathcal{X}}$ can be formally described by:

• Learned mixing: $$(X|\theta): \mathcal{X} \rightarrow \tilde{\mathcal{X}}$$.
• Fixed mixing: $(X): \mathcal{X} \rightarrow \tilde{\mathcal{X}}$.

This yields four principal paradigms:

1. Learned mixing independent of input (e.g., convolutional mixing once parameters are trained).
2. Learned mixing dependent on input (e.g., canonical self-attention, where $Q= XW^{\mathcal{Q}}$, $K= XW^{\mathcal{K}}$, $V= XW^{\mathcal{V}}$, and mixing weights are input-conditioned).
3. Fixed mixing independent of input (e.g., Fourier mixing with pre-set Vandermonde matrices).
4. Fixed mixing dependent on input (e.g., certain state-space models).

A unified template for token mixing is thus established. For example, self-attention can be expressed as:

$\tilde{\mathcal{X}}_{\text{attn}} = \text{softmax}(Q K^\top) V = [X (W^{\mathcal{Q}} W^{\mathcal{K}}^\top)] X W^{\mathcal{V}} = X G^{\mathcal{W}} X^\top \cdot XW^{\mathcal{V}}$

Here, $G^{\mathcal{W}}$ encodes learned token mixing weights; in Fourier-based mixing, it reduces to fast, fixed matrix operations. This mathematical perspective unifies models such as convolutional, attention, MLP-mixer, and Fourier-based networks, clarifying their shared token aggregation structure (Hè et al., 2023).

2. Scaling to Million-Scale Dependencies

The unified token sequence abstraction facilitates compositional scalability, particularly for sequences exceeding millions of tokens—e.g., in high-resolution images, extended textual documents, or long audio/video streams. The architecture leverages distributed multi-head attention and conditional computation:

• Distributed attention: Partitioning token sequences across $N$ devices, each computes partial attention locally. Efficient communication (COSTA algorithm, NCCL MPI API) enables recombination, supporting up to $\sim$1M tokens per context.
• Selector modules: Analogous to Mixture-of-Experts routing, selectors prune and distribute tokens, focusing computation on informative subsequences ("concentrated learning").
• Empirical outcomes: The distributed system on four RTX 4090 GPUs achieves a $\approx 40\times$ speed-up in attention computation compared to vanilla implementations.

This design circumvents quadratic time and memory complexities ($O(L^2)$ in standard attention), instead favoring $O(L\log L)$ or linear resource scaling where approximation is viable (Hè et al., 2023).

3. Unification Across Tasks and Modalities

Unified token sequences extend beyond efficiency—they enable integration across multiple data granularities, tasks, and modalities:

• Few-shot sequence labeling: CDAP network fuses token-level and span-level supervisions. Joint training and consistency loss (bidirectional KL divergence between token- and span-derived outputs) ensure cross-granularity learning and error mitigation. Inference employs a consistency-adjusted greedy algorithm for reliable span selection (Cheng et al., 2023).
• Vision-language tracking: MMTrack serializes both natural language descriptors and bounding box coordinates (quantized tokens), concatenating them into an auto-regressively decoded sequence. A single cross-entropy loss suffices for end-to-end optimization, obviating multi-head, multi-loss designs. This reduces model complexity and mitigates the misalignment seen in earlier approaches (Zheng et al., 2023).
• Generative recommendation: UTGRec applies a universal tokenizer, encoding multimodal item content (text and image) into a shared code sequence via tree-structured codebooks, supporting cross-domain transfer and collaborative signal infusion (Zheng et al., 6 Apr 2025).
• Multi-task protein modeling: Prot2Token autogenerates predictions for classification, regression, and structure tasks as token sequences, guided by task tokens and autoregressive decoding, improving throughput and facilitating multi-task generalization (Pourmirzaei et al., 26 May 2025).

4. Optimization and Compression within Unified Sequences

Unified token sequences underpin efficient compression strategies for vision transformers and large multimodal models:

• Progressive Visual Token Compression (PVC): All visual inputs—images and videos—are treated as video sequences. Progressive encoding supplements or refines spatial and temporal details over repeated static frames; adaptive compression accommodates temporal redundancy, allowing state-of-the-art performance with token budget constraints (e.g., 64 tokens/frame). Temporal attention (T-MHA) and adaptive layer normalization (AdaLN) provide fine-grained control over information extraction (Yang et al., 12 Dec 2024).
• Token Transforming: Compression is formulated as an explicit matrix transformation, $Y = W \cdot X$, subsuming token pruning (diagonal $W$) and merging (block-wise $W$), and generalizing to many-to-many mappings. Informative tokens are selected via attention-derived scores, with assignment and attention scaling normalization ensuring that information loss is minimized, and accuracy is preserved—even without retraining (Zeng et al., 6 Jun 2025).

5. Bridging Pretraining and Multimodal Representation

Unified token sequences enable new pretraining strategies, particularly in domains where canonical autoregression breaks down:

• TokenUnify pretraining amalgamates random token prediction, next-token, and next-all token prediction. This mixture controls cumulative error for ultra-long and spatially correlated sequences (e.g., electron microscopy images partitioned into thousands of patches). The next-all loss, $$L_{\text{next-all}} = - \sum_{i=1}^K \sum_{j=i}^K \log p(x_j | x_{<i})$$, bounds per-token error, thus stabilizing long-range dependencies. Experiments report a 45% improvement in neuron segmentation performance on downstream EM datasets and scalable pretraining up to 1B parameters (Chen et al., 27 May 2024).
• UniGenX: Unified autoregressive-diffusion sequence generation for scientific domains. Discrete symbolic and continuous numerical tokens (with domain-specific indicators like <bos>, <boc>) are interleaved; conditional diffusion heads yield high-precision outputs for numerical tokens, while AR heads maintain global sequence dependencies. This hybrid framework achieves state-of-the-art in crystal prediction, molecular generation, and protein modeling, with up to 120% match-rate improvements on structural tasks (Zhang et al., 9 Mar 2025).
• Token communication and multimodal LLMs leverage generative information bottleneck (GenIB) objectives for cross-domain transmission. GenIB learns tokens maximizing generative informativeness while compressing input, with $\sigma$-GenIB addressing variance collapse in autoregressive modeling. All received modality tokens are unified into a sequence consumed by a causal transformer (MLLM) for next-token prediction—proven effective under dynamic communication channels (Wei et al., 2 Jul 2025).

6. Unification in Multi-Role and Reinforcement Learning Paradigms

Unified token sequence design is also pivotal for multi-task and reinforcement learning frameworks:

• RoleRAG: All modules (query decomposition, retrieval, sub-answering, summarization, reasoning) are activated in a single LLM instance via role-specific token optimization (embedding-only fine-tuning of task tokens). Input concatenation with role tokens enables efficient "soft prompting," modularity, and dynamic graph-based query resolution, yielding 16–64% empirical gains over baseline RAG variants on open-domain QA tasks (Zhu et al., 21 May 2025).
• Unified reward shaping: GTPO and GRPO-S, in reinforcement learning for LLMs, exploit token-level and sequence-level entropy to assign granular, performance-informative rewards—overcoming the failures of undifferentiated sequence-wide credit. Higher-entropy tokens in successful reasoning chains receive incentives, guiding models toward deeper decision-making. Empirical measures confirm longer, higher-entropy responses and mean-reward improvements over DAPO-guided baselines (Tan et al., 6 Aug 2025).

7. Broader Impact and Future Directions

The unified token sequence paradigm provides a foundation for broad efficiency, scalability, and generalizable modeling:

• It synthesizes heterogeneous task formulations (classification, regression, structure prediction) and modalities (text, vision, audio) under a tractable, token-level interface.
• Scalability methods—such as distributed attention, adaptive compression, hybrid AR-diffusion—enable modeling of previously intractable long-range or high-dimensional data.
• Integration with universal tokenization, multitask prompting, and dynamic reward assignment signals a shift toward foundation models capable of cross-domain reasoning and interaction.

Future research is likely to extend these abstractions to broader multimodal fusion tasks, even more efficient large-context scaling, and increasingly general-purpose frameworks for scientific discovery, communication systems, and dynamic task optimization. The unified token sequence concept will remain central in harmonizing the structural, computational, and learning-theoretic advances across model architectures, task domains, and application scales.