Generalist Models: Unified Multi-Task AI

• Generalist models are unified multi-task systems that share parameters across diverse modalities, enabling rapid adaptation and efficient transfer learning.
• They employ transformer backbones, modular adapters, and mixture-of-experts to dynamically allocate processing and mitigate gradient interference.
• Empirical evaluations show competitive accuracy in fields such as medical imaging, NLP, and table processing, driving innovation in unified AI applications.

Generalist models are unified, multi-task systems designed to perform a broad spectrum of tasks—often spanning multiple data modalities—within a single parameter-sharing framework. Unlike specialist models, which are separately optimized for narrow domains or task classes, generalist models aim for high transferability, modular update, and competitive accuracy across varied tasks without retraining for each new dataset or problem. Recent advances demonstrate generalist models in language, vision, biomedical AI, algorithmic reasoning, table processing, control, and medical imaging, frequently leveraging architectural innovations such as modular adapters, mixture-of-experts, and instruction interfaces to overcome interference, privacy, and data-sharing hurdles.

1. Architectural Foundations and Design Patterns

Generalist model architectures converge on unification across input modalities and output task types, commonly via parametric sharing and modular adaptation.

• Transformer Backbone: Most recent generalist models rely on a single frozen backbone—often a Vision Transformer (ViT) or sequence-to-sequence Transformer—augmented with lightweight adapters or conditional expert layers. Med-LEGO, for example, adds LoRA-based adapters to specific blocks (Q and V projection matrices), encoding domain expertise for each specialist dataset (Zhu et al., 3 Mar 2025). OFASys and TableLlama use sequence-style Transformers to support slot-wise multi-modal inputs and long-context tables (Bai et al., 2022, Zhang et al., 2023).
• Instruction Interface and Plan Construction: In OFASys, each multi-modal task is defined via a declarative instruction string, parsed into a modular task plan with preprocessors, adapters, compute engines, and postprocessors (Bai et al., 2022).
• Mixture-of-Experts (MoE) and Routing: Uni-Perceiver-MoE and OFA+ Generalist MoE deploy sparsely-gated Conditional Mixture-of-Experts within Transformer layers, mitigating cross-task interference by conditionally routing tokens to sub-networks based on token, task, or attribute-level metadata (Zhu et al., 2022, Bai et al., 2022).
• Adapter-Based Modularity: Med-LEGO incorporates task-specific SVD-LoRA adapters, which are merged arithmetically and recompressed via singular value decomposition for parameter-efficient integration (Zhu et al., 3 Mar 2025).

2. Training Paradigms and Task Scaling

Generalist models are typically pre-trained on multi-domain, multi-task corpora, then adapted via either prompt tuning, adapter merging, or specialist finetuning.

• Multi-Task Training Objectives: Models such as OFA+ minimize a weighted sum of task-specific objectives: $$\mathcal{L}_{\mathrm{total}} = \sum_{i} w_i \mathcal{L}_i(x; \theta)$$ for cross-entropy (classification, generation), MSE (regression), CTC (speech), or DDPM (diffusion) losses (Bai et al., 2022).
• Adapter Merging Without Retraining: Med-LEGO achieves generalist CAD by merging specialist LoRA adapters in a training-free manner, using SVD bottlenecks to preserve critical singular directions in each specialist’s expertise; merged adapters can be further truncated according to explained variance thresholds (Zhu et al., 3 Mar 2025).
• Modality and Domain Extension: OFASys and TableLlama support rapid extension to new modalities/tasks by subclassing adapters and registering them via configuration decorators; models automatically integrate new “slots” without architectural changes (Bai et al., 2022, Zhang et al., 2023).
• Prompt Tuning and Parameter Efficiency: Uni-Perceiver-MoE and Med-LEGO demonstrate that prompt tuning or low-rank adaptation can achieve near-specialist performance with only $\sim 0.18\%$–$0.5\%$ of full model parameters per task or domain (Zhu et al., 3 Mar 2025, Zhu et al., 2022).

3. Empirical Results and Performance Analysis

Empirical evaluations across vision, medical, table, and language tasks consistently highlight the competitive or superior accuracy and generality of generalist models.

Domain	Representative Model(s)	Parameter Efficiency	Cross-Domain Accuracy	Specialist Gap
Medical Imaging	Med-LEGO, Segment Anything	$0.18\%$ per specialist	20–30 pp gains vs. soups (Zhu et al., 3 Mar 2025, Moglia et al., 12 Jun 2025)	Closed/surpassed
Multi-modal NLP/Vision	OFA+, Med-PaLM M	16% parameters / 95% SOTA	Near-SOTA on 14 tasks (Bai et al., 2022, Tu et al., 2023)	Minimal
Tables	TableLlama	Unified LoRA, 7B backbone	5–44 pp gains OOD (Zhang et al., 2023)	Matches/exceeds
Retinal Images	DINOv2/v3 vs. RETFound	Large-scale ViT, contrastive	Specialist RETFound best, but DINOv3 closes gap (Zhou et al., 3 Sep 2025)	Narrowing
Medical NLP	Generalists vs. Clinical	Generalists outperform	20 pp gain exact match semantic search (Excoffier et al., 2024)	Generalist > Specialist

Med-PaLM M reaches above-SOTA on pathology VQA and multi-label CXR, with emergent zero-shot generalization to tasks (e.g., tuberculosis detection) and competitive radiologist preference rates (Tu et al., 2023). TableLlama, trained via multi-task TableInstruct, achieves SOTA on 7/8 in-domain table tasks without table-specific pretraining, and substantially improves zero-shot out-of-domain accuracy (Zhang et al., 2023). Uni-Perceiver-MoE and OFA+ show that routing and MoE can restore specialist accuracy for vision, video, and retrieval with minimal parameter increase (Zhu et al., 2022, Bai et al., 2022).

4. Interference, Modularity, and Privacy

Generalist models naturally confront gradient interference, catastrophic forgetting, and privacy constraints, and recent work offers modular solutions.

• Gradient Interference: Training on diverse tasks causes conflicting gradients, especially in deep shared layers. Conditional MoE routing (attribute-based gating) activates distinct experts for token subsets, sharply reducing interference and preserving generalization (Zhu et al., 2022).
• Privacy and Data-Sharing: Med-LEGO’s adapter-sharing bypasses the need for raw data exchange: only tiny weight deltas ($\sim 0.18\%$) are shared, not source images or medical records, supporting secure cross-institutional generalist model building (Zhu et al., 3 Mar 2025).
• Modular Update/Adaptation: Adapter-based and attribute-level routing allow seamless update or insertion of new capabilities, without degrading performance on existing domains.

5. Trade-Offs and Open Problems

While generalist models offer parameter efficiency, unified deployment, and multi-task transfer, empirical studies reveal enduring trade-offs.

• Prediction vs. Reasoning Decoupling: TSRBench shows that scaling generalist models improves perception and reasoning but does not translate to numeric forecasting on time series; semantic understanding and quantitative extrapolation remain separate (Yu et al., 26 Jan 2026).
• Domain Specialization: In domains like clinical outcome prediction (hospital readmission, mortality), specialized models (pretrained on domain-specific corpora, e.g., Lang1 on EHR tokens) outperform larger generalists, implying that in-domain pretraining and explicit supervised fine-tuning remain essential (Jiang et al., 17 Nov 2025).
• Data, Training, and Compute Cost: Medical image segmentation generalists (e.g., SAM 2) demand prohibitive compute for pretraining, shifting feasibility from academic labs to major industry (Moglia et al., 12 Jun 2025).
• Architectural Limits: Current multimodal fusion (LLMs+VLMs) often fails to create synergistic performance across text and vision, necessitating novel cross-modal attention/assignment strategies (Yu et al., 26 Jan 2026).

6. Practical Applications and Deployment

Generalist models increasingly underpin practical AI systems across medicine, research, industrial automation, and information retrieval.

• Clinical Diagnostics and Triaging: Models like Med-LEGO, Med-PaLM M, and NeuroVFM provide integrated, auditable pipelines for diagnosis and reporting across diverse imaging modalities, sometimes matching or exceeding human experts (Zhu et al., 3 Mar 2025, Tu et al., 2023, Kondepudi et al., 23 Nov 2025).
• Medical Image Segmentation: Generalist models spanning SAM 2, PCNet, and vision-language hybrids achieve SOTA on multi-organ and multi-modality segmentation, frequently surpassing specialist architectures on liver, prostate, and abdominal tasks (Moglia et al., 12 Jun 2025).
• Table Processing and Reasoning: TableLlama extends generalist design to semi-structured table understanding, supporting classification, ranking, QA, and verification with state-of-the-art or better accuracy (Zhang et al., 2023).
• Time-Series and Control: TSRBench and generalist dynamics models (TDMs) demonstrate broad generalization across sequential reasoning, although forecasting quantitative outcomes remains a bottleneck (Yu et al., 26 Jan 2026, Schubert et al., 2023).

7. Future Directions and Research Challenges

Generalist models continue to evolve rapidly. Key directions and open challenges include:

• Scalable Extensions: Incorporating new modalities (e.g., genomics, 3D volumetrics), expanding context windows, and building agentic multi-agent systems for decomposed reasoning and planning.
• Hybrid Models and Specialists Integration: Chimera exemplifies hybrid approaches, fusing domain experts with generalist LMMs via sparse routing and collaboration masking, achieving SOTA on document, chart, table, and math reasoning (Peng et al., 2024).
• Efficiency and Responsible AI: Focus on model distillation, privacy-preserving training, robust calibration, and standardized multi-dimensional evaluation to ensure trustworthy, equitable real-world deployment (Moglia et al., 12 Jun 2025, Vishwanath et al., 1 Dec 2025).
• Algorithmic Reasoning and Foundation Models for Control: Generalist GNNs for algorithmic learning (Ibarz et al., 2022) and transformer-based TDMs for dynamics prediction (Schubert et al., 2023) provide blueprints for foundation models across logic and reinforcement learning.

Generalist models represent a shift from fragmented, specialist silos to unified, modular, and scalable AI systems that, while not universally competitive with narrow SOTA, increasingly enable high transfer, auditable adaptation, and broad deployment across complex domains.