PaperlayerView exact revision on arXiv

Source-linked AI summary

LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations

Majd Alafrange, Samuel Friedman, John Kitonyo, Sana Tonekaboni, Mahnaz Maddah

arXiv:2609.12364v1cs.LGcs.HC

TL;DR

Representation evaluation remains fragmented, and systematic tools for examining multimodal latent structure are scarce. LatentVerse unifies diagnostics and decomposes multimodal embeddings into shared and modality-specific components. Its evaluation covers controlled simulations, biomedical embedding analyses, and a user study, while its multimodal analysis makes signal location within fused representations examinable.

  • Problem

    Practitioners need ways to assess latent-space quality and information structure, but few tools systematically evaluate multimodal representations.

    Source: §1 · p. 2

  • Method

    LatentVerse unifies representation diagnostics and uses information decoupling to analyze shared and modality-specific components independently.

    Source: §1 · p. 2, §2 · p. 3, §2.4 · p. 5

  • Results

    LatentVerse was evaluated through controlled unimodal and multimodal simulations, biomedical embedding analyses, and a user study of practical value in analytical workflows.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Decomposing fused embeddings makes it possible to analyze where predictive or biologically relevant signal resides rather than treating the representation as one opaque vector.

    Source: §6 · p. 11

    The initial user study does not establish how well LatentVerse transfers across the full range of scientific and application domains using latent representations.

    Source: §6.1 · p. 11

Abstract

from arXiv · show

Latent embeddings have become a central data abstraction in modern machine learning, especially in biomedicine, where foundation models are increasingly used to encode multimodal data like clinical text, medical images, omics, and physiological signals. However, the utility and value of these representations depends on understanding their quality, structure, and the information they encode. Existing analysis workflows for evaluating representations remain fragmented across custom scripts, isolated metrics, and most importantly lack multimodal analysis, limiting accessibility and reproducibility. We present LatentVerse, a representation analysis resource that combines a web-based visual analytics platform for accessible, report-driven exploration with a command-line interface for scalable technical workflows. LatentVerse unifies diagnostics for various representation quality metrics and extends to multimodal settings by decomposing embeddings into shared and modality-specific components. We evaluate LatentVerse through controlled unimodal and multimodal simulations, discovery-oriented analyses on real biomedical embeddings, and a user study across diverse use cases. By supporting thorough and interpretable evaluation of latent spaces, LatentVerse makes foundation model representations more understandable in biomedical and data science applications.

1 Introduction

Evaluating latent representations requires looking beyond downstream performance to how information is organized, especially in multimodal spaces. LatentVerse unifies representation diagnostics and extends them to identify shared and modality-specific information; its evaluation spans simulations, biomedical embeddings, and user workflows.

1 Introduction

  • Multimodal embeddings can entangle information, while few tools systematically evaluate latent structure across unimodal and multimodal settings.Representation-centric diagnostics can characterize clusterability, disentanglement, robustness, expressiveness, and predictability.Source: §1 · p. 2
  • LatentVerse combines model-agnostic diagnostics with information decoupling to analyze shared and modality-specific latent components independently.This supports reasoning about where useful information resides and how components contribute to performance and interpretability.Source: §1 · p. 2
  • Its evaluation covers controlled simulations, real biomedical embeddings, and a user study of practical value in analytical workflows.Together, these evaluations address diagnostic recovery of known structure, discovery utility, and practical use.Source: §1 · p. 2

2 The LatentVerse Framework

LatentVerse offers a model-agnostic workflow for evaluating latent representations through a web application and command-line interface. It combines complementary quality diagnostics with multimodal decomposition, allowing shared and modality-specific components to be examined separately.

2 The LatentVerse Framework

  • LatentVerse accepts representation matrices and optional labels, then returns metrics, interactive visualizations, and a descriptive summary through web and command-line interfaces.The architecture uses a shared evaluation pipeline across these access modes.Source: §2 · p. 3, Figure 1 · p. 3

2.1 Representation quality dimensions

  • Five complementary dimensions assess representation quality: clusterability, disentanglement, expressiveness, downstream predictability, and robustness.The diagnostics examine grouping, factor separation, signal distribution, task-relevant information, and stability under perturbations.Source: §2.1 · pp. 3–4

2.2 A unified evaluation workflow

  • A three-step workflow selects an evaluation, uploads representations and optional labels, then renders the analysis as a report.The same pipeline serves both interfaces, with a parity suite checking numerical agreement to a relative tolerance of 10^-9.Source: §2.2 · pp. 4–5

2.3 Visual analytics and report generation

  • Reports coordinate a metrics table, a test-specific interactive visualization, and an on-click natural-language summary.Guidance and contextual explanations support less technical users, while advanced options and exports support detailed workflows.Source: §2.3 · p. 5

2.4 Multimodal analysis

  • Information decoupling separates cross-modal shared factors from modality-specific factors, and LatentVerse applies diagnostics to each component.The decomposition uses MultiLoReFT to isolate shared and private information for independent evaluation.Source: §2.4 · p. 5
  • Component-wise evaluation tests whether clinically relevant information is shared, private to one modality, or differs in compactness or robustness.This turns questions about cross-modal versus single-modality predictive structure into questions answerable from the data.Source: §2.4 · pp. 5–6
  • The framework makes fused embeddings analyzable for comparing fusion strategies and assessing whether modalities contribute complementary information.Figure 2 depicts decomposition into shared and modality-specific components.Source: §2.4 · p. 6, Figure 2 · p. 6

3 Validation on Controlled Data

Controlled simulations test whether LatentVerse diagnostics and visual reports recover known representation structure. Across unimodal and multimodal settings, the reports track the intended organization of information.

3 Validation on Controlled Data

  • The controlled validation was designed to confirm that integrated metrics and visual reports recover intended latent-space properties before discovery analyses.The simulations used dataset families with known structures and varying property levels.Source: §3 · pp. 6–7, §3.1 · p. 7

3.1 Unimodal simulations

  • Silhouette Score fell from 0.8936 to 0.0103, NMI from 1.0000 to 0.0007, and Cluster Learnability from 1.0000 to 0.2280 as class-aligned cluster structure degraded.Across the other unimodal families, reports distinguished directly accessible from decoder-dependent or uninformative labels, factorized from entangled representations, and concentrated from redundant or sparse information.Source: §3.1 · p. 7, Figure 3 · p. 8

3.2 Multimodal simulations

  • In multimodal simulations, shared labels were concentrated in the shared space, while modality-specific labels were most strongly recovered in their corresponding private components.Metrics and visualizations reflected this separation, recovering the simulated ground-truth organization.Source: §3.2 · p. 7

4 Representation Analysis for Real-world Discovery

LatentVerse analyzes pretrained ECG and cMRI embeddings from UK Biobank to examine clinically relevant information across modalities and latent components. The examples show differences in predictive accessibility, signal distribution, and where biological-sex structure is clearest.

4 Representation Analysis for Real-world Discovery

  • The analyses illustrate how diagnostics can reveal which modality carries a phenotype signal and whether that signal is shared or confined to one component.The study examines pretrained ECG and cMRI embeddings derived from UK Biobank data.Source: §4 · p. 8
  • ECG representations achieved substantially higher PQ-interval predictive performance than cMRI representations, with strong R2 values even for simple probes.The ECG representation also showed stronger concentrated target-related structure, while cMRI information was more distributed across latent dimensions.Source: §4 · pp. 8–9
  • For pulmonary-hypertension diagnosis, cMRI representations appeared to provide stronger discriminative signal than ECG, while AUROC was more informative than accuracy under label imbalance.Accuracy remained artificially high across probes in this imbalanced-label example.Source: §4 · p. 9
  • Biological-sex labels showed the clearest separation in the shared latent space, with substantially weaker structure in modality-specific components.This example distinguishes information encoded jointly across modalities from information driven mainly by one modality.Source: §4 · pp. 9–10

5 User Study

The user study explored how biomedical researchers could use LatentVerse in their own analytical workflows. Participants valued its reports and metrics, while identifying needs around verifying findings and reproducing analyses.

5 User Study

  • Participants examined embeddings from varied biomedical applications to assess perceived usefulness, interpretability, and fit with their own workflows.The study emphasized practical analytical use rather than generic usability on one task or dataset.Source: §5 · p. 10

5.1 Results

  • The study gathered workflow-focused feedback from three researchers in genomics, proteomics, and clinical machine learning; responses were formative, not a quantitative evaluation.Participants already evaluated embeddings with Python notebooks and custom visualization scripts.Source: §5.1 · p. 10
  • Participants valued quick access to standard metrics, worked examples, contextual explanations, and job-progress reporting.One participant encountered metrics they had not previously used; both participants who engaged with the rating scale expressed confidence in their conclusions.Source: §5.1 · p. 10
  • Reservations centered on verifiability: participants wanted sample-level traceability, clearer cluster labeling, subgroup analyses, and a scripted, version-controlled workflow.The current deployment adds provenance reporting, per-sample export, cluster-versus-label distinctions, subgroup comparison, and a command-line path for reproducibility.Source: §5.1 · p. 10

6 Discussion

LatentVerse unifies representation diagnostics in a report-driven workflow and supports analysis of both unimodal and multimodal embeddings. The discussion highlights multimodal decomposition and reports simulation, biomedical, and user-study evaluations, while noting deployment and generalizability limits.

6 Discussion

  • LatentVerse combines quantitative diagnostics, visual summaries, comparisons, and descriptive interpretation; simulations recovered synthetic structure, and biomedical analyses characterized real embeddings.The framework is intended for evaluation, debugging, and discovery-oriented analysis across users with different technical expertise.Source: §6 · p. 11
  • Its distinctive multimodal contribution decomposes fused embeddings into shared and modality-specific components to inspect where predictive or biologically relevant signal resides.The discussion frames this capability as relevant to validation and trust as foundation models combine modalities.Source: §6 · p. 11

6.1 Limitations and future directions

  • LatentVerse remains a research prototype; broader deployment on larger datasets, heavier workloads, and more concurrent users requires further engineering and infrastructure.Scaling also requires job scheduling and resource management.Source: §6.1 · p. 11
  • The user study provides an initial assessment among researchers familiar with machine-learning workflows, but does not establish transfer across the full range of scientific and application domains.The authors identify broader domains, user profiles, and analytical settings as future evaluation directions.Source: §6.1 · p. 11

A.1 System architecture and implementation

The implementation combines a web application, backend, and machine-learning library, with file-size-dependent ingestion, concurrent label evaluation, and asynchronous comparison jobs. Deployment uses managed cloud services and provides optional institutional authentication.

A.1 System architecture and implementation

  • The backend validates uploads, joins representation and label files by user-selected IDs, preprocesses data, and dispatches benchmark tests.The web application uses a React frontend, FastAPI backend, and standalone machine-learning library.Source: §A.1 · p. 15
  • Files over 50 MB use Polars with eight threads, files from 10–50 MB use PyArrow, and smaller files use pandas.For large tabular files, Polars loading is reported as 10–100× faster than pandas.Source: §A.1 · p. 15
  • Multiple label columns run concurrently, with BLAS thread caps and bounded worker concurrency to limit oversubscription.Numba accelerates disentanglement statistics by 10–20× over the pure-Python path on large representations.Source: §A.1 · pp. 15–16
  • Long-running comparisons run asynchronously with status polling; multimodal training is off-loaded to Cloud Run Jobs and shares the job-progress pipeline.The deployment uses Cloud Run and Cloud Storage; institutional deployments can enable authenticated access and per-user run history.Source: §A.1 · p. 16

A.2 Web application interface

The web interface guides users through test selection, comparison setup, and report review. It includes contextual guidance, reproducibility controls, and visualizations tailored to each test.

A.2 Web application interface

  • The test selector and guidebook provide metric descriptions, score ranges, interpretation guidance, and common pitfalls.Example mode loads precomputed results for five unimodal tests and the multimodal workflow with interpretive notes.Source: §A.2 · p. 16
  • Comparison cards accept representation files and optional labels, with ID-based or row-order alignment and controls for random seed, standardization, subsampling, and cluster count.The interface warns when label-to-representation matches fall below a configurable overlap threshold.Source: §A.2 · p. 16
  • Cached file fingerprints can prevent an identical file from being uploaded again after validation.Source: §A.2 · p. 17
  • Results combine test-specific interactive charts, a sortable metrics table, and JSON or CSV exports, with an optional AI-generated descriptive summary.The charts include, among other forms, disentanglement heatmaps, probing error bars, and robustness curves.Source: §A.2 · p. 17

A.3.1 Clusterability

LatentVerse evaluates clusterability through intrinsic geometry metrics and, when labels are available, measures alignment with known classes and label recoverability.

A.3.1 Clusterability

  • Silhouette Score and Davies–Bouldin Index assess cluster cohesion and separation directly from representation geometry.For N > 5,000, Silhouette is estimated on a 20% random subsample with a minimum of 1,000 samples.Source: §A.3.1 · p. 17
  • When ground-truth labels are available, NMI measures alignment between discovered clusters and known classes.Source: §A.3.1 · p. 17
  • Cluster Learnability is the balanced accuracy of a logistic-regression probe trained on a stratified 80/20 split; label-free evaluation uses intrinsic metrics alone.Source: §A.3.1 · p. 17

A.3.2 Disentanglement

This section describes LatentVerse’s disentanglement, expressiveness, predictability, and robustness diagnostics, along with synthetic-data and survey protocols and session-specific feedback instruments. It reports controlled metric responses to entanglement, predictive accessibility, and dimension removal, as well as P1’s neutral ratings across eight statements and limited embedding evaluation.

A.3.2 Disentanglement

  • LatentVerse reports DCI, MIG [8], SAP [16], and Total Correlation, estimating mutual information with k-nearest-neighbour methods (k = 3) and approximating TC with a Gaussian proxy.For multiple factors, DCI Disentanglement and Completeness are importance-weighted across dimensions and factors; Informativeness, MIG, and SAP are equally averaged across factors.Source: §A.3.2 · pp. 17–18

A.3.3 Expressiveness

  • Expressiveness analysis removes the highest-variance dimensions in increments of 10% through 50%; compactness summarizes average performance degradation, while intrinsic dimension estimates how many dimensions can be removed before substantial decline.The compactness score is clamped to [0, 1].Source: §A.3.3 · p. 18

A.3.5 Robustness

  • Robustness analysis injects Gaussian noise at σ ∈ {0.1, 0.2, 0.3, 0.4, 0.5} and evaluates clustering or probing metrics; visualizations mark performance at or above 90% of the clean baseline as robust.Clustering metrics include Silhouette Score and Davies–Bouldin Index, with NMI when labels are available; probing uses task-appropriate Accuracy, AUROC, macro-F1, or R2.Source: §A.3.5 · p. 18

A.5.1 Unimodal datasets; A.5.2 Multimodal dataset

  • The simulations include unimodal datasets with 5,000 samples, 64 features, and four evaluation families, plus two modalities generated from two shared and four modality-specific non-Gaussian latent variables.The multimodal joint label is derived from the shared and both modality-specific labels, ensuring it is not predictable from either modality alone.Source: §A.5.1 · p. 19, §A.5.2 · p. 19

A.6 Controlled offline validation supplement

  • Predictability tests distinguished directly accessible labels (linear-probe accuracy = 1.0000) from labels needing more expressive decoders (0.1104 linear-probe to 0.4392 5-layer MLP accuracy) and uninformative representations.Probes use three-fold cross-validation; task-appropriate metrics include Accuracy, AUROC, macro-F1, and R2, and the most complex probe showed a macro-F1 drop indicating instability or overfitting in one case.Source: §A.3.4 · p. 18, §A.6 · p. 20, Figure 5 · p. 20
  • Across controlled disentanglement datasets, easier settings had higher DCI, MIG, and SAP and lower Total Correlation, while harder settings showed weakening scores and increasing cross-dimension dependence.Figure 6 combines these metrics with visual summaries of how label-related information is distributed across latent dimensions.Source: §A.6 · p. 20, Figure 6 · p. 21
  • In the compact setting, performance fell from 0.6248 to 0.1901 after removing 50% of dominant dimensions; the correlated setting changed from 0.7149 to 0.6637, and the sparse setting from 0.6211 to 0.1833.Compactness and intrinsic dimension were 0.4672 and 6 for the compact setting, 0.0335 and 32 for the correlated setting, and 0.3491 and 13 for the sparse setting.Source: §A.6 · pp. 20–21, Figure 7 · p. 22

A.7 User-study protocol and survey instrument; Survey introduction and consent; About the participant and their workflow; Session-specific feedback on LatentVerse

Session-specific feedback on LatentVerse

  • Participants rated LatentVerse’s usefulness, comparison, interpretability, evidence, trust, and workflow value, and compared its speed, clarity, and multimodal utility with their usual workflow.Items used a 1–5 Likert scale; participants were instructed to select neutral for the multimodal comparison item if it did not apply.Source: Session-specific feedback on LatentVerse · p. 26
  • Free-text prompts asked participants about conclusions reached, confusion, useful features, desired additions, important improvements, and workflow fit.Source: Session-specific feedback on LatentVerse · p. 26

A.7.1 Survey Results

  • P1 returned neutral midpoints on all eight statements and reported evaluating embeddings “rarely or never.”Source: Table 2 · p. 27
Loading 2609.12364v1…