Foundation Model Representations

• Foundation model representations are high-dimensional embeddings that encode transferable semantics across various domains and modalities.
• They are learned via contrastive pre-training, masked modeling, and hybrid tasks, enabling effective anomaly detection and robust domain adaptation.
• Evaluation methods like linear probing and fine-tuning demonstrate that intermediate layers often yield optimal transferability and actionable insights.

Foundation model representations are high-dimensional embeddings produced by large, pre-trained neural networks that aim to encode transferable and general-purpose semantics across a diverse range of domains and modalities. Foundation models—spanning language, vision, time series, geospatial, audio, multi-modal medical data, and even neural network weights—learn such representations via large-scale self-supervised or multi-task objectives. The efficacy, transferability, and robustness of these representations depend critically on model architecture, pre-training corpora, and the alignment between upstream and downstream data distributions. This article surveys the principal mechanisms, evaluation methods, domain-specific phenomena, robustness findings, and future directions pertaining to foundation model representations in contemporary research.

1. Mechanisms of Representation Learning in Foundation Models

Foundation models construct their internal representations through deep parameter sharing, large-scale data exposure, and task-agnostic learning objectives. Notable architectures include transformer-based models (e.g., CLIP, ViT, GPT, MERT, Qwen2-Audio), state-space models (e.g., Mamba, Motor in EHRs), and multimodal or hierarchical designs (e.g., CheXFound's ViT-L+GLoRI, ChaosNexus's ScaleFormer).

Representation learning objectives typically include:

• Contrastive Pre-training (InfoNCE): Used in CLIP, BiomedCLIP, PLIP, CityFM, CLAP, and Qwen2-Audio, maximizing alignment between paired modalities (image-text, audio-text) in a joint embedding space via normalized dot products and temperature scaling.
• Masked/Masked Language/Image Modeling: Employed in MLM for BERT/Cehr-BERT, DINO/DINOv2 for CheXFound and other vision models, and masked acoustic modeling in MERT, reconstructing masked regions from contextual cues.
• Hybrid Tasks: Models such as Motor implement time-to-event (TTE) prediction via custom hazard functions, enhancing downstream event-time prediction by encoding temporal and survival dynamics directly in the patient representation.
• Variational Tokenization: In discrete representation scenarios, e.g., MoFM’s MotionBook, a discrete VAE-style variational encoder maps spatio-temporal heatmaps into discrete units ("tokens") interpretable as human motion primitives.

These learning paradigms yield internal representations that accumulate hierarchical information: early layers capture modality-local or low-level features, while deeper layers abstract increasingly global and task-oriented semantics. For time series and audio, intermediate layers often provide the most anomaly-relevant or physiologically rich signals (Han et al., 16 Sep 2025, Nie et al., 27 May 2025).

2. Protocols for Extracting and Utilizing Representations

Foundation model representations are commonly extracted as:

• [CLS] Tokens or Special Embedding Vectors: Used in BERT-like models (Cehr-BERT, CheXFound, CityFM) and ViT-style architectures; global semantics are pooled into a designated token.
• Patch/Token Grids: Vision and time series models (ViT, TSFM, ChaosNexus) output per-patch or per-patch/token representations, essential for downstream tasks that depend on local or spatially resolved features.
• Intermediate Layers: Recent evidence (Han et al., 16 Sep 2025, Nie et al., 27 May 2025) shows that optimal information for downstream tasks (e.g., anomaly detection, heart-rate estimation) may be concentrated in mid-level layers, not the model top.
• Joint Multimodal Embeddings: Fusion or concatenation of text, vision, and spatial encoders as in CityFM or M4Survive, often with additional normalization steps for cross-modality alignment.

Evaluation and adaptation protocols include:

• Linear Probing: Training a lightweight head on top of frozen representations to gauge inherent transferability (Vargas et al., 2023, Pang et al., 22 May 2025, Papaioannou et al., 20 Jun 2025).
• Supervised Fine-Tuning: Partial or full (1-2 layer) fine-tuning atop frozen features to boost domain-specificity (Papaioannou et al., 20 Jun 2025).
• Coreset Selection: For anomaly detection, reference sets of representative embeddings are selected (often via k-center algorithms) to speed distance computations (Han et al., 16 Sep 2025).
• Causal and Distributional Adjustment: Explicitly adjusting for confounders or shifts (e.g., provenance) using strategies such as backdoor adjustment ensures robustness to domain shift (Ding et al., 2023).

3. Empirical Insights: Performance, Robustness, and Specialization

Empirical studies reveal the nuanced performance of foundation model representations:

Domain	Task/Setting	Foundation Model (FM)	Baseline/Specialist Model	Key Outcomes
Digital Pathology	WSI Retrieval	CLIP, BiomedCLIP, PLIP	KimiaNet, DinoSSLPath	Specialist models outperform FMs by 5–10 F1 points; domain-matched pretraining critical (Alfasly et al., 2023)
EHRs	Prognosis, Phenotyping	Cehr-GPT, Motor	Tabular Baselines	FMs outperform on discrimination, calibration, and fairness; struggle on ultra-rare tasks (Pang et al., 22 May 2025)
Geospatial/Urban	Road speed, Land use	CityFM	Node2Vec, CLIP, SpaBERT	Multimodal FM embedding (text, vision, spatial) outperforms baselines (Balsebre et al., 2023)
Audio	Heart-rate Estimation	CLAP, WavLM, HuBERT	Mel+CNN	Mid-level FM layers match or exceed baseline; domain-mismatch reduces gap (Nie et al., 27 May 2025)
Music	Tagging, World-music FSL	Qwen2-Audio, MERT	VGG-ish	FMs set new SOTA on 5/6 corpora, but generalization to non-Western traditions still lags (Papaioannou et al., 20 Jun 2025)
Time Series	Anomaly Detection	TimeRep (TSFM)	Prior DL/SSL/Forecasting	Intermediate representations plus coreset give SOTA results (Han et al., 16 Sep 2025)

Key empirical findings include:

• Alignment between pre-training data and downstream domain is critical; web-scale but out-of-domain pretraining limits applicability in clinical and scientific settings (Alfasly et al., 2023, Pang et al., 22 May 2025).
• Larger model size increases generalization in some settings (music, chaos forecasting), but can exacerbate domain overfitting if the pretraining distribution is not sufficiently diverse (Liu et al., 26 Sep 2025, Papaioannou et al., 20 Jun 2025).
• Simple linear probes on frozen representations can be as effective as—and occasionally more robust than—full fine-tuning, especially under distribution shift (Vargas et al., 2023).
• Intermediate representations often exhibit greater anomaly saliency and transferability than final representations (Han et al., 16 Sep 2025).
• Robustness to provenance- or institution-induced distribution shift is only modestly improved by FMs; explicit adjustment remains necessary (Ding et al., 2023).

4. Domain-Specific Innovations and Representational Structures

Distinct modalities and application domains necessitate specialized representational schemes:

• Hierarchical/Multi-Scale Processing: ChaosNexus employs U-Net-style transformers to encode both global and local temporal structures, essential in chaotic dynamics (Liu et al., 26 Sep 2025).
• Cross-Attention Query Integration: GLoRI in CheXFound uses disease-specific query vectors to pool spatially relevant patch features, enhancing transferrability in medical imaging (Yang et al., 7 Feb 2025).
• Multimodal Fusion: CityFM concatenates BERT-derived textual, ResNet-based visual, and sinusoidal spatial embeddings; M4Survive fuses radiology and pathology streams in correlated latent space via state-space "Mamba" adapters (Balsebre et al., 2023, Lee et al., 13 Mar 2025).
• Discrete/Learned Tokenization: MoFM’s MotionBook dictionary encodes complex human motion as sequences of semantically meaningful discrete tokens using a variational discrete autoencoder architecture, supporting efficient and scalable representation across spatio-temporal tasks (Baharani et al., 8 Feb 2025).
• Wavelet/Channel-Aware Attention: In multivariate physiological time-series (NormWear), channel-aware self-attention with intra- and inter-sensor token communication enables high generalizability across sensor configurations and downstream applications (Luo et al., 12 Dec 2024).

These structures allow for direct encoding of physical, spatial, or semantic structure, yielding representations that can be compact, compositional, and interpretable.

5. Robustness to Distribution Shift, Confounding, and Provenance

The stability of foundation model representations under distribution shift is a central concern:

• Detecting and Responding to Distribution Shift: Linear probes and PCA/KDE analyses of FM representations readily detect dataset or population mismatches (Sentiment140, provenance shift) (Vargas et al., 2023, Ding et al., 2023).
• Provenance Confounding: Out-of-the-box robustness is limited; optimal stability is achieved only by explicit confounder adjustment—Pearl’s backdoor formula—applied at the representation level (Ding et al., 2023). |m|, the sensitivity of performance to distribution skew, drops by a factor of 2–20 after such adjustment.
• Anomaly/Novelty Detection: Intermediate representations in TSFMs yield sharper separation of normal vs. anomalous inputs than final layers; core-set construction and memory-updating strategies support concept drift adaptation without retraining (Han et al., 16 Sep 2025).
• Block-wise Redundancy and Pruning: TSFMs often exhibit large blocks of redundant layers (CKA ≥ 0.9); pruning these yields up to 58% encoder sparsity with minimal loss in downstream accuracy (Wiliński et al., 19 Sep 2024).

These findings collectively suggest that, while foundation model representations confer certain degrees of robustness, informed statistical adjustment, behavioral diagnostics, and careful pipeline integration remain essential for trustworthy downstream performance.

6. Limitations, Challenges, and Emerging Directions

Current foundation model representations face several open challenges:

• Domain Mismatch and OOD Adaptation: Representations tend to underperform if the upstream data distribution misaligns with downstream requirements, especially for rare classes, non-Western modalities, or clinical/sensor signals without matched pretraining (Alfasly et al., 2023, Papaioannou et al., 20 Jun 2025, Nie et al., 27 May 2025).
• Interpretability: Mechanistic understanding (e.g., localization of semantic, physical, or pathological features) remains incomplete outside specialized architectures such as GLoRI or block-pruned TSFMs (Yang et al., 7 Feb 2025, Wiliński et al., 19 Sep 2024).
• Modality Integration: True universality—handling arbitrary data types with unified representations—is hindered by token-based fragmentation, lack of physical semantics, or biases from skewed data distributions. Outcome-driven digital twin representations have been proposed as an alternative, grounding representations in physical laws and state-space formalisms for causally consistent reasoning (Shen et al., 1 May 2025).
• Parameter and Data Scaling: Model scaling alone does not guarantee generalized representations; cross-system or cross-modal diversity of the pre-training corpus is a more effective axis for broad generalization, as demonstrated in large-scale chaotic system modeling (Liu et al., 26 Sep 2025).
• Task-Specific Tuning and Data Efficiency: In some cases, smaller task-trained or few-shot networks (tabular baselines, VGG-ish in music) remain competitive or even advantageous for rare/select domains or low-data regimes (Papaioannou et al., 20 Jun 2025, Pang et al., 22 May 2025).

A plausible implication is that hybrid pipelines—combining large pre-trained FMs with explicit adjustment, domain-aware architecture design, and judicious prospecting for task-appropriate layers/modality fusions—will be necessary to realize the full promise of foundation model representations.

7. Recommendations for Practice and Research

• Ensure domain-matched pretraining or fine-tuning to avoid significant degradation when deploying FMs in specialized downstream contexts (Alfasly et al., 2023, Pang et al., 22 May 2025).
• Favor pre-training objectives that encode temporal, spatial, or structural constraints where possible; context-aware encoding (e.g., time-to-event in EHR, cross-sensor in wearables) materially increases representation expressivity (Luo et al., 12 Dec 2024, Pang et al., 22 May 2025).
• Leverage intermediate representations, not only top-layer embeddings, when designing tasks such as anomaly detection or bio-signal estimation (Han et al., 16 Sep 2025, Nie et al., 27 May 2025).
• Explicitly account for confounders and distribution shifts, with attention to the causal structure of the data generation process; apply statistical adjustments as part of the predictive pipeline (Ding et al., 2023).
• Adopt block-wise pruning, concept steering, and other introspection/optimization strategies to reduce redundancy and recover controllable latent factors in foundation representations (Wiliński et al., 19 Sep 2024).
• Explore the integration of digital twin representations for physically grounded, causally interpretable foundation modules where domain knowledge is available (Shen et al., 1 May 2025).

Continued research in cross-modal, hybrid, and physically informed foundation representation paradigms will determine the future trajectory of high-capacity, universally applicable models.