End-to-End Foundation Models

Updated 11 July 2025

End-to-end foundation models are large-scale neural networks that learn directly from raw data, unifying the entire processing pipeline.
They eliminate handcrafted preprocessing by integrating dynamic segmentation and tokenization, which enhances adaptability across multiple modalities.
Empirical results demonstrate that these models achieve strong performance and efficiency in diverse domains such as healthcare, autonomous driving, and robotics.

End-to-end foundation models are large-scale neural architectures pretrained on immense and diverse datasets, designed to serve as universal function approximators for a broad spectrum of tasks across modalities such as language, vision, audio, or combinations thereof. These models are distinguished from more traditional pipelines by their integration of the entire learning, inference, and, in some settings, even segmentation or tokenization processes into a single differentiable framework, learning directly from minimally processed raw data. As such, end-to-end foundation models strive to subsume not only feature extraction and task-specific mapping, but also upstream processes previously considered external or heuristic—ranging from dynamic segmentation (e.g., learned chunking in language) to domain-specific adaptation (e.g., pathology slide-level learning). Their deployment and ongoing evolution are leading to major shifts in decision-making, generation, and control systems in domains including autonomous driving, healthcare, robotics, and multimodal translation.

1. Conceptual Principles and Design Aims

The defining objective of end-to-end foundation models is to maximize generalization, adaptability, and efficiency by unifying as many components of the inference pipeline as possible into a single, trainable model. They are typically instantiated as deep neural networks (often transformers or transformer variants), pretrained on broad data using self-supervised or supervised learning, and then specialized for downstream tasks via transfer learning, prompting, distillation, or further fine-tuning.

A key distinction for end-to-end architectures is their focus on eliminating the need for handcrafted preprocessing steps or manual feature engineering. For instance, H-Net dispenses with static tokenizers in LLMing by dynamically learning chunk boundaries during training, thus removing the requirement for hand-designed segmentation even in challenging modalities like code or DNA (2507.07955). Analogously, end-to-end approaches in digital pathology such as EXAONE Path 2.0 allow direct backpropagation of slide-level supervisory signals to patch-level representations, bypassing the need for patchwise self-supervised pretraining (2507.06639).

Another central tenet is the integration of perception, reasoning, and action. In decision-making contexts, foundation models are leveraged to support end-to-end learning from raw observations (e.g., sensor data) to action selection, sometimes incorporating planning or world-model components that are fully differentiable and trainable in concert with policy or control modules (2303.04129). The latent world modeling and intention-aware planning in end-to-end autonomous driving exemplifies this aspiration (2507.00603).

2. Methodologies and Architectural Innovations

Designs of end-to-end foundation models vary by application but share certain characteristic methods:

Unified Loss Functions: Training objectives are constructed to encompass supervision or self-supervision at multiple hierarchical levels, often with additional cross-modal alignment or reconstruction components, as in EXAONE Path 2.0 (where slide-level and auxiliary patch-level losses propagate through all model stages) (2507.06639).
Dynamic Preprocessing Integration: The dynamic chunking in H-Net replaces handcrafted tokenization with a learned boundary-detection mechanism operating on raw input streams (2507.07955). The routing module calculates cosine similarity-based boundary probabilities, and a smoothing module ensures gradients can flow through discrete chunking choices:

$p_t = \frac{1}{2}\left(1 - \frac{q_t^\top k_{t-1}}{\|q_t\|\|k_{t-1}\|}\right)$

where $q_t$ , $k_{t-1}$ are projections of the encoder output at $t$ and $t-1$ , respectively.

Hierarchical Architectures: Many end-to-end models organize processing into hierarchical levels—patch, region, slide; chunk, sequence; or camera view, scene, trajectory—where each level receives supervisory signals, and aggregation occurs in a learned, differentiable manner.
Self-Supervised Latent World Modeling: In autonomous driving, models such as World4Drive jointly learn latent spatial-semantic scene representations and multi-modal trajectory planning, enforcing self-supervised alignment between predicted and actual evolution of future world states (2507.00603). Loss components often include terms for semantic alignment, reconstruction, and behavior imitation:

$\mathcal{L} = \alpha \mathcal{L}_{sem} + \beta \mathcal{L}_{recon} + \gamma \mathcal{L}_{score} + \eta \mathcal{L}_{traj}$

Plug-in Modules for Data-Efficient Distillation: End-to-end knowledge distillation frameworks (e.g., for speech recognition) introduce two-stage processes: first, a student matches the foundation model ("teacher") embeddings from raw data, then fine-tunes on the main task with optional alignment losses (e.g., n-best alignment), all in a unified optimization (2303.10917).

3. Evaluation and Empirical Findings

End-to-end foundation models have yielded strong empirical results across diverse domains:

Sequence Modeling: H-Net with dynamic chunking shows superior scaling and data efficiency compared to BPE-tokenized Transformers, especially in data-scarce regimes or non-English modalities. It demonstrates increased robustness to noise and achieves nearly a fourfold increase in data efficiency for DNA sequence modeling (2507.07955).
Speech and Text Integration: Two-stage adapters for speech-to-text (e.g., LST) leveraging LLMs achieve new state-of-the-art BLEU scores on speech translation benchmarks, with careful modality alignment between speech representations and text embedding spaces being essential (2310.02050).
Medical and Scientific Imaging: EXAONE Path 2.0 achieves state-of-the-art AUROC across ten biomarker prediction tasks using only 37,000 whole-slide images, a significantly smaller dataset than required by previous self-supervised approaches (2507.06639). Similarly, the CardX ECG foundation model demonstrates superior weighted F1 and robustness across multiple clinical tasks while being orders of magnitude more efficient in terms of FLOPs (2503.13570).
Autonomous Driving and Robotics: Open-set, out-of-distribution robustness has been reported for patch-level transformer features used together with language-based latent space simulation, allowing both data augmentation and policy debugging in end-to-end frameworks (2310.17642). End-to-end planning models that assemble intention-aware latent world models achieve marked reductions in L2 trajectory error and collision rate without labeled perception data (2507.00603).
Hypernetwork Integration: Transformer-based hypernetworks using pre-trained foundation model backbones (e.g., DINO, CLIP) achieve higher PSNR, SSIM, and FID values on implicit neural representation tasks than randomly initialized counterparts and are more data-efficient (2503.00838).

4. Comparative Advantages and Practical Trade-offs

Empirical benchmarks provide nuanced insights into when and why end-to-end foundation models succeed:

Data Efficiency: End-to-end supervised models tuned directly on downstream labels (as in EXAONE Path 2.0 or end-to-end ResNet50 in mitosis classification) frequently surpass models using linear probing over fixed foundation model embeddings, offering better adaptation to specialized tasks and fewer data requirements (2507.06639, 2412.06365).
Domain and Modal Robustness: Particularly for languages and modalities without strong tokenization heuristics—Chinese, code, biological sequences—dynamic, content-dependent preprocessing (as in H-Net) brings marked improvements (2507.07955).
Unified Representation: The capacity to propagate supervisory signals through all levels (e.g., patch to slide, image to trajectory) enhances the alignment between model objectives and clinically or operationally relevant outputs, as seen in pathology and ECG models (2507.06639, 2503.13570).
Limitations: Despite their strengths, end-to-end architectures can be resource-intensive, may require sophisticated memory or curriculum learning strategies for high-resolution inputs, and sometimes underperform compared to cascaded or modular systems in extremely low-data settings or where explicit intermediate factors (such as edit types in GEC) are required (2311.05550).
Replaceability of Preprocessing: While end-to-end learning is beneficial for domains where preprocessing is a bottleneck or source of suboptimal heuristics, conventional models may remain competitive or even superior in cases with well-understood, high-information handcrafted pipelines, or limited computation (as shown in medical imaging with small linear probes) (2412.06365, 2501.14685).

5. Applications Across Modalities and Domains

The unification and flexibility of end-to-end foundation models have facilitated adoption in a broad swath of high-impact applications:

Decision Making and Control: Prompted reinforcement learning, policy gradient integration, and hybrid planning (combining chain-of-thought with low-level policies) are being used in decision-making systems across dialogue, healthcare, robotics, and education (2303.04129).
Speech, Language, and Translation: Foundation model-based two-stage and adapter approaches enable modality transfer in speech translation, disfluency removal, and spoken GEC, outperforming traditional cascaded pipelines in compactness and propagation of uncertainty (2310.02050, 2311.05550).
Autonomous Vehicles: Use of spatially granular transformer features in driving policies, latent world modeling, and self-supervised trajectory selection directly from raw input data yield systems robust to open-set and out-of-distribution driving environments (2507.00603, 2310.17642, 2402.01105).
Bio/Medical Signal Analysis: ECG and pathology foundation models integrated into end-to-end platforms support privacy-preserving, on-premise fine-tuning, facilitating clinical deployment and improving diagnostic performance with fewer computational resources (2503.13570, 2507.06639).
Robotics and Embodied AI: Unified vision-language-action models support instruction-following, skill transfer, and dynamic manipulation with direct grounding in both perception and control, highlighting data hunger and generalization trade-offs vs modular pipelines (2505.15685, 2410.13002).

6. Open Challenges and Research Directions

Several persistent challenges and future research directions for end-to-end foundation models have emerged:

Reliability and Hallucination: Errors in end-to-end models—including hallucinated outputs—can have severe consequences in safety-critical environments such as autonomous driving and medicine. Mechanisms for uncertainty quantification and robust calibration are essential (2303.04129, 2402.01105).
Efficiency and Scalability: Training and deploying large end-to-end models is resource-intensive. Innovations in memory management, curriculum learning, model quantization, and parameter-efficient tuning (e.g., LoRA, soft prompt tuning) are required for widespread use (2507.06639, 2311.05550).
Joint Learning of Preprocessing: The dynamic integration of operations once considered "external" to model training—such as learned segmentation rather than static tokenization or manual patch extraction—promises richer and more adaptable representations, but also introduces optimization complexity (2507.07955).
Sim-to-Real and Domain Transfer: Bridging the gap between simulation-trained models and real-world deployments, especially in robotics and autonomous driving, remains challenging. Approaches leveraging multimodal pretraining, data augmentation via text/latent simulation, and strong world modeling are under active exploration (2310.17642, 2410.13002).
Interpretability and Intermediate Feedback: The lack of explicit intermediate variables in end-to-end systems hampers error tracing and user feedback (e.g., in spoken GEC). Hybrid designs or auxiliary decoders to extract interpretable intermediary signals are a prospective avenue (2311.05550).

7. Taxonomies and Model Comparison Strategies

Rigorous evaluation and comparison of foundation models, particularly in end-to-end settings, are facilitated by systematic frameworks that move beyond traditional aggregate metrics:

Embedding Space Geometry: Model comparison via embedding space geometry (constructing data kernels and random dot product graphs) enables per-datum hypothesis testing and induces manifolds of models, supporting both diagnostic and taxonomy-building efforts (2305.05126).
Unified Benchmarking: Efforts such as E3D-Bench in 3D geometric foundation models provide unified, task-diverse benchmarks assessing models across core tasks—including sparse/dense view depth estimation, reconstruction, pose estimation, and novel view synthesis—using standardized protocols and efficiency metrics (2506.01933).
Population-level Science: The emergence of model manifold analysis enables a “taxonomic science” of foundation models, where models are categorized and selected according to global properties of their latent representations, facilitating reproducible and generalizable research (2305.05126).

End-to-end foundation models represent a paradigm shift in machine learning, characterized by unification of the entire processing pipeline, flexible and often dynamic data representation, and robust adaptation across modalities and domains. Their ongoing development is reshaping best practices in deployment, model selection, and evaluation, driving future directions in scaling, efficiency, robustness, and interpretability.