Unified Autoregressive LLM

• Unified autoregressive LLMs are models that predict the next token in serialized sequences across varied modalities, including text, images, audio, and structured data.
• They leverage transformer-based architectures with techniques like MoE and hybrid decoding to jointly optimize heterogeneous tasks.
• This unified approach enhances cross-modal transfer, streamlines deployment, and delivers empirical gains in domains such as speech recognition, SQL parsing, scientific generation, and time-series forecasting.

A unified autoregressive LLM is a modeling paradigm in which a single autoregressive model is designed to handle multiple, potentially heterogeneous tasks by generating serialized sequences of tokens that may include text, structured data, image, audio, or modality-specific attributes. In this framework, all outputs—regardless of the underlying task—are represented as token sequences and predicted autoregressively, facilitating flexible joint modeling, end-to-end optimization, and seamless transfer across tasks. Unified autoregressive LLMs have been proposed for domains as diverse as speech recognition with speaker attribute estimation, structure-aware semantic parsing, multimodal and scientific data generation, and time-series forecasting. They typically leverage transformer-based architectures with specialized strategies to simultaneously address the challenges posed by different data types, modalities, or generation goals.

1. Autoregressive Formulation and Unified Output Serialization

The core principle underlying unified autoregressive models is next-token prediction across an appropriately serialized output sequence. For standard LLMing and single-talker ASR, the joint probability of a token sequence $W = \{w_1, …, w_N\}$ given input $X$ is modeled as:

$$P(W | X; \Theta) = \prod_{n=1}^{N} P(w_n | w_{1:n-1}, X; \Theta).$$

Unified models extend this factorization to more complex settings:

• Multi-talker overlapped ASR and attribute estimation: The output sequence $$Z = \{g^1, a^1, w_1^1, …, w_N^1, [sep], g^2, a^2, …\}$$ interleaves attribute tokens (gender $g^t$, age $a^t$) and word tokens for each speaker, leading to:

$$P(W^{(1:T)}, g^{(1:T)}, a^{(1:T)} | X; \Theta) = \prod_{l=1}^{|Z|} P(z_l | z_{1:l-1}, X; \Theta).$$

This joint serialization allows the model to use previously generated attribute and text tokens as context, providing rich, speaker-specific information for each utterance (Masumura et al., 2021).

• Text-to-SQL and structured data: A sequence is formed by serializing the database schema, conversation history, and the user query; decoding proceeds autoregressively, potentially including structure marks or identifiers (Dou et al., 2022).
• Multimodal, time-series, visual, or scientific domains: Text, image, audio, numerical series, and modality attributes are cast into a token sequence (discrete or continuous), making the entire modeling process compatible with a single unified autoregressive backbone.

This serialization enables joint optimization and the potential for context-aware generation, crucial for tasks exhibiting ambiguity, overlapping structure, or requiring auxiliary information (e.g., speaker attributes, schema constraints, spatial or temporal tags).

2. Model Architectures and Extensions

Most unified autoregressive LLMs employ a transformer-based encoder-decoder or decoder-only architecture, customized in various ways for different input types or tasks:

• Transformer-based End-to-End Models: As in overlapped speech recognition, where a speech encoder processes acoustic features and a transformer decoder predicts an output serialization of text and attribute tokens. Encoder blocks use multi-head self-attention and convolutional layers for downsampling, enabling the capture of long-range temporal dependencies (Masumura et al., 2021).
• Multimodal Integration: In visual or scientific generation tasks, models process both discrete tokens (e.g., text, symbolic sequences) and continuous tokens (e.g., image latent patches, atomic coordinates) using specialized heads within the unified model (Fan et al., 17 Mar 2025, Yuan et al., 11 Jul 2025). MoE (mixture-of-experts) structures can allocate token-specific computation for distinct modalities or sub-tasks, with deterministic routing schemes selecting appropriate experts (Li et al., 3 Sep 2025).
• Attribute and Structure Awareness: Output tokens can represent non-textual information such as speaker gender/age, database schema properties, or spatiotemporal positions, allowing the autoregressive model to embed auxiliary signals within the output sequence and guide subsequent generation steps.
• Hybrid and Extended Decoding: Some approaches, such as LLM-to-SLM hybrids, decouple expensive prompt encoding (by a frozen large model) from efficient autoregressive decoding (by a small, fine-tuned model), achieving substantial latency gains while maintaining high performance (Bergner et al., 26 Feb 2024).
• Diffusion and Autoregressive Integration: For tasks requiring high numerical precision (e.g., scientific structure generation), diffusion heads are conditioned on the hidden state of the autoregressive model and invoked for continuous-value predictions, thus combining the sequence modeling strengths of AR transformers with iterative denoising for enhanced precision (Zhang et al., 9 Mar 2025).

3. Domain-Specific Strategies and Applications

Unified autoregressive LLMs have demonstrated effectiveness across diverse applications by employing task- and domain-specific strategies:

• Speech Recognition with Speaker Attributes: Attribute tokens provide explicit supervision, reducing speaker confusion in multi-talker overlapped ASR and improving metrics such as character error rate, speaker counting accuracy, and attribute estimation accuracy (Masumura et al., 2021).
• Text-to-SQL Semantic Parsing: Structure mark encoding, constrained decoding (O(1) per token via prefix tries), and SQL completion (graph-based post-processing for JOIN prediction) enable off-the-shelf seq2seq architectures to robustly handle multi-domain, multi-table, and multi-turn SQL generation (Dou et al., 2022).
• Scientific Sequence and Structure Generation: A unified word-to-word/number-to-number strategy allows long-range symbolic dependencies and precise continuous prediction (e.g., atomic coordinates in materials, molecules), with significant gains in structure prediction accuracy over previous SOTA (Zhang et al., 9 Mar 2025).
• Multimodal Generation and Understanding: Pure decoder-only models with hierarchical, multi-scale autoregressive mechanisms achieve strong performance for high-resolution image and video synthesis as well as cross-modal understanding, without depending on external vision encoders or image tokenizers at inference (Li et al., 3 Sep 2025, Yuan et al., 11 Jul 2025).
• Time-Series Forecasting: By encoding numerical data as text and leveraging the intrinsic AR capabilities of LLMs, competitive forecasting is possible even in multivariate, noisy, and highly dynamic datasets (Madarasingha et al., 3 Jun 2025).

A summary table illustrates architectural and domain diversity:

Model/Domain	Serialization/Modality	Special Techniques
Multi-talker ASR (Masumura et al., 2021)	Text + attribute tokens	Token augmentation, recursive attr.
Text-to-SQL (Dou et al., 2022)	Schema + NL question + context	Structure marks, constrained decoding
Scientific Gen (Zhang et al., 9 Mar 2025)	Words + continuous numbers	Diffusion head, joint AR-training
Multimodal Unified (Li et al., 3 Sep 2025)	Text + visual tokens	Decoder-only, MoE, multi-scale AR
Video Gen (Yuan et al., 11 Jul 2025)	Text + vis. tokens (spatiotemporal)	MM-RoPE, AR-DF, tube masking

4. Performance Metrics and Empirical Outcomes

Unified autoregressive LLMs have demonstrated strong empirical results across modalities:

• Speech and Attribute Estimation: Joint modeling of text and attributes in overlapped speech improved character error rate (CER) and speaker attribute metrics; e.g., lower CER and higher speaker counting and attribute accuracy (Masumura et al., 2021).
• Structured Data Parsing: Achieves logical form and execution accuracies on par with or surpassing highly specialized models on WikiSQL, Spider, SParC, and DuSQL (Dou et al., 2022).
• Multimodal Generation: State-of-the-art models like OneCAT outperform encoder-based and previous unified systems on multimodal understanding (TextVQA, MMbench), text-to-image (GenEval), and image editing (DPG-Bench), while providing substantially reduced inference latency and computational overhead due to a pure decoder and multi-scale AR mechanism (Li et al., 3 Sep 2025).
• Time-Series Forecasting: Achieves up to 26.8% MSE reduction versus baselines in univariate settings, with a further 17.4% improvement in multivariate settings, using zero-shot LLMs (Madarasingha et al., 3 Jun 2025).
• Scientific Domains: In material generation, match rate improvements of 10–120% and dramatic RMSD reductions on MPTS-52, which includes long sequences, highlight gains over prior approaches (Zhang et al., 9 Mar 2025).

5. Benefits, Challenges, and Limitations

Benefits:

• Unified Optimization: Jointly modeling multiple modalities or tasks leverages cross-task and cross-modal signals, often yielding improved generalization and transferability.
• Flexible Contextualization: Autoregressive serialization allows dynamic conditioning on prior tokens, facilitating contextual disambiguation (e.g., speaker attributes in ASR, table/column context for SQL).
• Simplicity and Modularity: A single model can replace multiple task-specific systems, streamlining deployment and reducing maintenance overhead.

Challenges and Limitations:

• Modality Interference: Naive joint training can increase perplexity or degrade performance due to conflict between modalities or tasks; solutions like progressive vocabulary activation (Tang et al., 27 Mar 2025), careful loss balancing (Fan et al., 17 Mar 2025), or architectural specialization (e.g., MoE (Li et al., 3 Sep 2025)) are often needed.
• Latency: Purely autoregressive decoding can impose inference bottlenecks, especially for long outputs; hybrid schemes (LLM-to-SLM (Bergner et al., 26 Feb 2024)), multi-scale or coarse-to-fine generation (Li et al., 3 Sep 2025), and parallelized sampling/distillation (Deschenaux et al., 28 Oct 2024) mitigate this.
• Numerical Precision: AR models struggle with precise continuous outputs; diffusion heads or hybrid AR-diffusion training provide high-precision quantitative prediction while leveraging sequential context (Zhang et al., 9 Mar 2025).

6. Emerging Trends and Future Directions

Current research highlights several open topics:

• Further Modalities: Work extends unified AR frameworks to audio, speech, video, and time-series domains, incorporating spatiotemporal embeddings and dynamic scaling (Yuan et al., 11 Jul 2025, Fang et al., 5 May 2025, Madarasingha et al., 3 Jun 2025).
• Hybrid and Efficient Decoding: Approaches such as LLM-to-SLM (Bergner et al., 26 Feb 2024) and simultaneous generation with distillation through time (Deschenaux et al., 28 Oct 2024) are promising for alleviating sequential bottlenecks while maintaining or improving generation fidelity.
• Fine-Grained Control and Safety: Fine-tuning strategies for utility-preserving concept erasure (e.g., Windowed Gradient Accumulation, Thresholded Loss Masking (Fan et al., 25 Jun 2025)) enable safety and compliance without sacrificing overall utility in AR models.
• Progressive Vocabulary and Loss Approaches: Stage-wise vocabulary activation, curriculum learning, and dynamic loss scaling (resolution-aware schedules) enhance stability and reduce interference in multimodal and cross-modal training (Tang et al., 27 Mar 2025, Wang et al., 5 Aug 2025).
• Open-Source and Hardware Direction: Models such as FBI-LLM demonstrate that extreme quantization (fully binarized transformers) can be trained from scratch with autoregressive distillation loss to match full-precision capabilities, paving the way for hardware-efficient, scalable unified models (Ma et al., 9 Jul 2024).
• Unified Evaluation: Novel benchmarks for concept erasure (ECGVF (Fan et al., 25 Jun 2025)), multimodal comprehension (GenEval, DPG-Bench (Wang et al., 5 Aug 2025)), and cross-domain reasoning are being developed to systematically measure unified AR model capabilities.

7. Practical Implications and Impact

Unified autoregressive LLMs have broad implications for the design of future intelligent systems:

• Holistic Foundation Models: The serialization-based AR approach provides a common substrate for handling text, speech, images, video, scientific data, and structured data, supporting the vision of all-purpose multimodal LLMs.
• Efficiency and Deployment: Advances in architectural efficiency (MoE, progressive curriculum, binarized weights) and latency (LLM-to-SLM, multi-scale AR) make deployable, real-time, and resource-conscious models feasible on commodity hardware (Wang et al., 5 Aug 2025).
• Transfer and Versatility: A single model can be adapted to new tasks or domains with minimal additional engineering, supporting rapid development and broad applicability in research, industry, and applied sciences.
• Control and Alignment: Innovations in training objectives and fine-tuning strategies support robust alignment, controllable content generation, and safe deployment in sensitive application domains.

Unified autoregressive LLMs thus represent a foundational paradigm for future foundation models, enabling scalable, versatile, and controllable systems that natively integrate context, structure, modalities, and auxiliary information within a single, autoregressive framework.