Generative Foundation Models

Updated 5 December 2025

Generative foundation models are large-scale neural architectures trained to learn high-dimensional data distributions across modalities such as vision, language, and audio.
They employ diverse methodologies including autoregressive transformers, diffusion models, VAEs, and GANs to achieve synthesis, reconstruction, and robust transfer learning.
These models drive innovations in privacy-preserving, decentralized, and multi-modal applications, powering advances in scientific simulation, image synthesis, and anomaly detection.

Generative foundation models are large-scale, general-purpose neural architectures trained via generative objectives to model complex data distributions across domains such as vision, language, audio, scientific time series, and structured artifacts. Unlike traditional discriminative models—which focus on predicting supervised labels—generative foundation models learn to synthesize, reconstruct, complete, or simulate entire data modalities, often enabling strong transfer to downstream tasks via their universal representations, zero-shot adaptation, or data augmentation capacity.

1. Theoretical Foundations and Model Classes

At their core, generative foundation models (GFMs) aim to learn a probability distribution $p_\theta(x)$ over high-dimensional data $x$ , parameterized by $\theta$ at massive scale (often billions of parameters). To achieve this, current GFMs employ training schemes from four principal families:

Autoregressive transformers: Model $p_\theta(x)$ as a product of conditionals $p_\theta(x) = \prod_{t} p_\theta(x_t | x_{<t})$ , handling tokens for images, text, 3D shapes, or sensor data. DALL-E, MeshXL, and MusicGen exemplify this class (Liu et al., 2023, Chen et al., 31 May 2024).
Diffusion models: Learn to denoise gradually perturbed data through a reverse Markov process, with loss objectives of the form

$\mathcal{L}_{\mathrm{diff}} = \mathbb{E}_{t,x_0,\epsilon}[\|\epsilon_\theta(x_t, t) - \epsilon\|^2],$

where $\epsilon_\theta$ is trained to estimate the noise at step $t$ (Abdi et al., 30 Jul 2025, Ji et al., 4 Sep 2025, Cheng et al., 3 Feb 2025).

Variational autoencoders (VAEs) and hybrids: Encode data into a latent variable $z$ via $q_\phi(z|x)$ , and optimize evidence lower bounds to reconstruct samples, often augmented with diffusion or flow-matching losses in latent space (Chen et al., 23 Sep 2025).
GANs and adversarial models: Less dominant at the foundation scale but still present in some hybrid architectures; focus on adversarial learning of sample realism.

A defining property of GFMs is their pretraining regime: vast, diverse, and often unlabelled or weakly-labelled datasets, sometimes comprising millions to billions of samples and spanning modalities or domains. The target is not only high-fidelity synthesis and faithful data reconstruction, but also a generalizability that enables cross-domain adaptation, generative data augmentation, and transfer to downstream discriminative and generative tasks (Liu et al., 2023, Zhou et al., 13 Jun 2025).

2. Core Architectures and Training Methodologies

Architectures

Decoder-only transformers are prevalent in text, image, and mesh generative settings; they facilitate next-token prediction and scale efficiently to long sequences (Chen et al., 31 May 2024, Zhou et al., 13 Jun 2025).
U-Net backbones are widely used in diffusion models for vision, medical imaging, and scientific data, often coupled with latent-space VAEs for computational efficiency (Ji et al., 4 Sep 2025, Cheng et al., 3 Feb 2025).
MoE (Mixture-of-Experts) transformers are employed for parameter-efficient specialization and adaptive routing in domains exhibiting subpopulation heterogeneity, such as human mobility (Yuan et al., 7 Jun 2025).

Generative Objectives

Across domains, GFMs utilize generative training signals:

Masked signal reconstruction (e.g., masked video modeling, masked seismic patch prediction) (Wang et al., 2022, Cheng et al., 3 Feb 2025).
Denoising score matching (diffusion), both in pixel/voxel space and latent space (Abdi et al., 30 Jul 2025, Ji et al., 4 Sep 2025, Chen et al., 23 Sep 2025).
Contrastive or hybrid generative–discriminative pretraining, as in video–language alignment (Wang et al., 2022).
Auto-regressive sequence modeling for time series (text, motion, 3D mesh vertices, IMU streams), often combined with compression schemes like VQ-VAE (Chen et al., 31 May 2024, Leng et al., 2023).

Domain-Specific Innovations

Domain-specific mechanisms increase generality or privacy:

Generative continual learning for privacy-preserving lifelong adaptation—replaying synthetic samples, distilling knowledge to prevent forgetting, and architectural modularity through MoE routing (Yuan et al., 7 Jun 2025).
Physics-based inductive bias—ectending GANs/VAEs/diffusion to scientific and operator learning tasks, e.g., flow-matching for PDE-governed systems (Chen et al., 23 Sep 2025), uncertainty-aware seismic imaging (Cheng et al., 3 Feb 2025).
Reinforcement learning for data acquisition, where prompts to the generative model are actively selected to optimize downstream classifier performance with minimal labeling effort (Schiavone et al., 25 Apr 2024).

3. Applications Across Domains

Generative foundation models have achieved broad adoption as universal data synthesizers, pretrained representational backbones, and privacy-respecting data generators.

Domain	Application Modality	Key Contributions
Vision	Text-to-image, inpainting, anomaly detection	Stable Diffusion, zero-shot OOD with DDMs (Abdi et al., 30 Jul 2025, Liu et al., 2023)
Video	Masked tube modeling, action recognition	VideoMAE and multimodal contrastive VFM fusion (Wang et al., 2022)
Audio	Text-to-audio, audio augmentation, speech modeling	AUDIOGEN, AudioLDM 2, MusicGen (Feng et al., 13 Jun 2024)
3D Graphics	Text/image→mesh, mesh completion, editing	MeshXL (Chen et al., 31 May 2024)
Recommender Systems	Multi-task generative + embedding foundation	RecFound—joint contrastive and generative objectives (Zhou et al., 13 Jun 2025)
Mobility	Continual, privacy-preserving trajectory modeling	MoveGCL: MoE, generative replay, distillation (Yuan et al., 7 Jun 2025)
Human Activity	Synthetic IMU, activity summarization	LLM-to-motion autoregressive generation (Leng et al., 2023)
Scientific Modeling	Weather/PDE simulation, uncertainty quantification	Flow Marching for PDEs, generative neural operators (Chen et al., 23 Sep 2025)
Medical Imaging	Multimodal chest radiograph synthesis, bias mitigation	ChexGen: LDM + ControlNet, fairness analysis (Ji et al., 4 Sep 2025)

These models routinely support tasks such as downstream fine-tuning/transfer, sample-efficient generalization, cross-modal data synthesis, supervision of discriminative models, privacy-centric federated or decentralized model adaptation, uncertainty quantification, and large-scale simulation with built-in aleatoric and epistemic uncertainty propagation.

4. Generative Models for Privacy, Scalability, and Decentralization

A persistent theme is the application of generative foundation models to privacy-sensitive or decentralized data regimes.

Generative continual learning, as in MoveGCL (Yuan et al., 7 Jun 2025), forgoes raw trajectory sharing and instead replays synthetic pseudo-trajectories for each previously seen partition (e.g., city), with each student model distilled from the frozen teacher’s output distribution on these samples. Order-invariant performance and strong privacy metrics (uniqueness, membership-inference, $\epsilon$ -privacy) are demonstrated.
Federated generative learning (FGL) (Zhang et al., 2023) replaces gradient or parameter sharing with semantic prompt collection and centralized synthetic data generation via foundation models (e.g., Stable Diffusion). Experiments show FGL approaches or exceeds the performance of 200-round FedAvg with order-of-magnitude lower communication and improved privacy.
RL-guided prompt selection (Schiavone et al., 25 Apr 2024) enables the adaptive, compact selection of synthetic support sets to maximize classifier utility at negligible data cost, outperforming random prompt augmentation, especially in low-data regimes.

These frameworks demonstrate that generative foundations can be scaled without central access to raw, sensitive data and are robust to heterogeneous, non-IID, or evolving data distributions.

GFMs have been extended to cover structured and scientific modalities beyond conventional vision and language:

PDE-governed time series: Flow Marching (Chen et al., 23 Sep 2025) models the entire conditional distribution $p(x_{s+1}\mid x_s)$ across families of dynamical systems, with flow matching to unify deterministic operator learning and SDE-based generative sampling. Physics-pretrained VAEs and token-efficient temporal pyramids support tractable scaling, and the model achieves state-of-the-art long rollout stability and uncertainty stratification.
Seismic signal processing: GSFM (Cheng et al., 3 Feb 2025) is a diffusion-based, multi-task framework enabling denoising, backscatter suppression, interpolation, and low-frequency extrapolation with one-step target-oriented sampling, supporting both synthetic and fine-tuned real-world field data, and providing a built-in uncertainty map for each reconstruction.
Circuit design: GenEDA (Fang et al., 13 Apr 2025) aligns graph-based circuit encoders (NetTAG) with LLM decoders via latent-space adapters or gate-type annotation for both open and closed decoders, supporting generative reverse reasoning from low-level netlists to functionally accurate RTL code and formal specifications.

6. Unified Generative–Discriminative Paradigms and Future Directions

Recent developments indicate convergence of generative and discriminative modeling within foundation-scale architectures. Several survey works (Liu et al., 2023) stress the ongoing unification, as seen in:

Hybrid objectives: Models simultaneously learn to generate data and align with supervised or contrastive labels (VideoMAE+VLC in InternVideo (Wang et al., 2022), RecFound jointly optimizing contrastive and token-level generative losses (Zhou et al., 13 Jun 2025)).
Discriminative use of generative models: Diffusion models pretrained purely for synthesis have been shown to serve as universal perceptual templates for anomaly detection or open-set classification, outperforming specialized discriminative architectures on hard OOD separation tasks (Abdi et al., 30 Jul 2025).
Unified multi-modal architectures: Foundation models (e.g., ChexGen (Ji et al., 4 Sep 2025), MeshXL (Chen et al., 31 May 2024)) integrate text/image/mask/3D inputs through cross-modal conditioning, with evidence for scaling laws analogous to LLMs.

Emerging challenges include efficient sampling on edge devices, bridging high-dimensional modalities (video, 3D, audio), robust bias mitigation, and the further theoretical paper of generalization, transfer, and uncertainty quantification in generative foundation models.

Key References:

General architecture and survey: (Liu et al., 2023, Wang et al., 2022, Chen et al., 31 May 2024)
Privacy-preserving and federated domains: (Yuan et al., 7 Jun 2025, Zhang et al., 2023, Schiavone et al., 25 Apr 2024)
Multi-modal vision/language/3D: (Ji et al., 4 Sep 2025, Chen et al., 31 May 2024, Cheng et al., 3 Feb 2025)
Scientific/dynamical systems: (Chen et al., 23 Sep 2025, Cheng et al., 3 Feb 2025)
Human activity, recommender systems: (Leng et al., 2023, Zhou et al., 13 Jun 2025)
Anomaly detection: (Abdi et al., 30 Jul 2025)
Circuits/EDA: (Fang et al., 13 Apr 2025)
Audio: (Feng et al., 13 Jun 2024)

These works establish GFMs as the backbone of next-generation, universally capable AI models, charting a path toward open, scalable, cross-domain, and privacy-respecting deployment.