Cross-Modal Benchmark: Biology & AI

Updated 30 November 2025

The paper establishes a standardized suite to assess models that integrate and reason across multiple biological data modalities.
It utilizes rigorous data curation, standardized formats, and expert adjudication to ensure high-quality, reproducible evaluations.
The benchmark bridges biological discovery with AI advancements, enhancing model transferability and cross-modal inference.

A cross-modal benchmark for biology and AI operationalizes the assessment of computational systems that integrate, analyze, or reason across distinct data modalities—such as images, sequences, tables, and text—rooted in complex biological problems. Recent years have seen the emergence of specialized benchmarks that span the conceptual and technical divide between real biological inference tasks and advanced AI methodologies, mobilizing research at the interface of biomedical data curation, multimodal learning, and systems neuroscience.

1. Definitions, Scope, and Overarching Motivation

A cross-modal benchmark for biology and AI constitutes a standardized suite—dataset(s), tasks, protocols, and metrics—explicitly constructed to evaluate the ability of computational models to align, relate, or reason across at least two distinct modalities inherent in biological science. Modalities may include but are not limited to: microscopy or radiology images, gene expression matrices, protein interaction networks, DNA or protein sequences, and free-form or structured natural language. Such benchmarks operationalize the task of joint representation learning, retrieval, classification, generation, or functional annotation in multimodal settings—demanding that models integrate biological domain knowledge with state-of-the-art computational architectures and inference strategies (Fahsbender et al., 14 Jul 2025).

Motivations for these benchmarks derive from both the needs of the biological sciences—where the integration of imaging, sequencing, and textual knowledge is fundamental to discovery—and the drive within AI to build generalizable models capable of robust transfer and zero/few-shot problem solving across scientific domains.

2. Canonical Benchmarks and Exemplary Modalities

Recent cross-modal benchmarks target distinct axes of biological complexity and AI integration:

BioSage-CDQA: A multiple-choice question-answering suite requiring integration of domain-specific biological facts (e.g., multi-omics, cellular image segmentation) with the application of proper AI/ML methodologies (e.g., deep neural networks, graph-based fusion). While currently text-only, the construction explicitly aims for "cross-disciplinary multimodal reasoning," with future releases targeting images, tables, and experimental protocols (Volkova et al., 23 Nov 2025).
MicroVQA: A visual question answering benchmark using microscopy images (bright-field, fluorescence, electron) with tasks spanning visual interpretation, hypothesis generation, and experimental proposal. Each item consists of an image set, a research-grade question, and multiple distractors, with a two-stage construction pipeline removing language-only shortcuts (Burgess et al., 17 Mar 2025).
HESCAPE: A large-scale benchmark in spatial transcriptomics for evaluating image–gene expression alignment and prediction, comprising hundreds of thousands of paired histology image patches and locus-matched gene expression profiles from human tissue. Tasks include cross-modal retrieval, mutation classification, and gene expression regression, with explicit paper of batch effects and representational alignment (Gindra et al., 2 Aug 2025).
BioTalk Enzyme Function: A benchmark coupling millions of DNA sequences (prokaryotic genes with EC labels) and templated natural-language descriptions of enzymatic function, supporting classification, contrastive embedding, and few-shot prompting tasks (Zhang et al., 21 Jul 2024).
MedGEN-Bench: A contextually entangled benchmark for open-ended multimodal medical reasoning and generation, integrating medical images (CT, MRI, ultrasound, X-ray, pathology, etc.) with complex, instruction-driven tasks—VQA, image editing, and clinical report generation. The framework extends to text–image coherence, pixel-level similarity, and holistic clinical relevance scoring (Yang et al., 17 Nov 2025).

These benchmarks reflect a growing attention to realism (actual data distributions, research use-cases), complexity (multistep or open-ended reasoning), and modality coverage (beyond uni- or bimodal to multi-dimensional settings).

3. Construction Protocols, Data Curation, and Annotation Standards

Cross-modal benchmarks demand rigorous construction pipelines to prevent shortcut exploitation and ensure biological validity.

Data Sources & Curation: Primary biological data are sourced from large-scale public consortia (e.g., ENA, human spatial transcriptomics banks), expert-curated image repositories, and structured knowledge bases (UniProt, KEGG). Annotation protocols enforce domain-specific correctness, often requiring multi-pass expert adjudication (e.g., ≥90% inter-annotator agreement in BioSage-CDQA (Volkova et al., 23 Nov 2025)).
Formatting & Balancing: Standard formats (OME-TIFF, AnnData, mzML, VCF) and ontologies (Gene Ontology, Cell Ontology) are mandated to support interoperability. Datasets are stratified by batch, tissue, or functional class to mitigate confounder leakage and overfitting risks (Fahsbender et al., 14 Jul 2025).
Shortcut Removal: State-of-the-art pipelines—such as the RefineBot agent in MicroVQA—iteratively identify and eliminate language-only reasoning shortcuts by probing models in image-absent scenarios and rewriting distractors to require genuine cross-modal inference (Burgess et al., 17 Mar 2025).
Split Design: Patient-level, class-level, or batch-level splits ensure evaluation protocols reflect the generalization desiderata, with explicit in-distribution and out-of-distribution test cohorts when feasible (Zhang et al., 21 Jul 2024).

4. Evaluation Metrics and Methodological Frameworks

Formalisms for evaluating cross-modal benchmarks are driven by the modality and downstream task.

Classification and Retrieval: Standard metrics—accuracy, precision, recall, F₁—are used for MCQA or label assignment, with hierarchical variants (e.g., Hi-P, Hi-R, Hi-F for enzyme function) when dealing with ontologically structured labels (Zhang et al., 21 Jul 2024). For retrieval tasks, Precision@k, mAP, and alignment scores (e.g., CLIP-based cosine similarity) are employed (Fahsbender et al., 14 Jul 2025).
Regression and Correlation: Pearson correlation and MSE are standard for continuous gene expression prediction. For joint representation, methods such as Spearman correlation between representational dissimilarity matrices (RSA), or ridge regression under cross-validation for encoding models, set upper bounds via noise ceilings (Cichy et al., 2019, Gindra et al., 2 Aug 2025).
Multimodal Generation: MedGEN-Bench establishes a three-tier framework: pixel-level metrics (PSNR, SSIM, IoU), semantic alignment (embedding cosine similarity), and expert-guided clinical relevance scores rated across coherence, visual-textual alignment, and factual accuracy (Yang et al., 17 Nov 2025).
Batch Effects and Robustness: Explicit measurement and ablation of batch effects are central in spatial transcriptomics and any setting with significant technical or biological stratification, employing metrics such as SilhouetteBatch and manifold visualization (Gindra et al., 2 Aug 2025).

5. Impact, Insights, and Technical Recommendations

Cross-modal benchmarks have enabled incisive evaluation of AI architectures and methodologies in biologically grounded settings:

Model Performance Patterns: In MicroVQA, expert-level multimodal reasoning remains bottlenecked by perception errors, with up to 50% of failures in visual understanding versus <30% due to knowledge gaps (Burgess et al., 17 Mar 2025). Tuning with domain-specific literature yields quantifiable gains, but large models do not dominate over smaller architectures by a wide margin.
Representation Alignment: In HESCAPE, contrastive cross-modal pretraining substantially enhances mutation classification (up to +100% relative improvements in F1), but paradoxically impairs direct gene expression regression—suggesting current alignment objectives may inject extraneous technical variance rather than pure biological structure (Gindra et al., 2 Aug 2025).
Benchmark Construction Principles: The CZI Virtual Cells Workshop provides a formalization of necessary protocols—standard formats, ontologies, batch handling, stratified splitting—to ensure fair, reproducible benchmarking. Aggregated modality-agnostic performance indices and multi-tiered ablation studies are recommended for rigorous cross-domain assessment (Fahsbender et al., 14 Jul 2025).
Richness of Biological Discovery: Layer-wise analysis in the Algonauts Project reveals tight quantitative correspondences between model layers and regions/timing of human visual cortex activity, with the strongest alignment between early layers and early visual regions and late layers with high-level inferotemporal regions (Cichy et al., 2019).

6. Limitations and Future Directions

Several limitations are noted in the current generation of cross-modal benchmarks:

Modality Coverage: Many benchmarks label themselves "cross-modal" while only supporting text inputs or not fully integrating mapping across non-textual data forms. Large-scale multimodal (image–text–omics) standards are still nascent (Volkova et al., 23 Nov 2025).
Dataset Scale and Generalization: The sample sizes of domain-specific benchmarks (e.g., BioSage-CDQA with 116 items) constrain statistical power. Expansion toward ≥1,000 items per modality and inclusion of balanced train/val/test splits are active directions (Volkova et al., 23 Nov 2025).
Technical Limitations: Batch effect entanglement, restricted species or panel representation, and lack of end-to-end contrastive embedding training limit biological realism and model robustness (Gindra et al., 2 Aug 2025, Zhang et al., 21 Jul 2024).
Assessment Depth: Open-ended generative tasks with contextually entangled prompts (as in MedGEN-Bench) expose fundamental weaknesses in current VLMs, which often fail to maintain cross-modal coherence without explicit modular design (Yang et al., 17 Nov 2025).

Proposed extensions involve: integrating true visual and tabular inputs; standardizing and reporting batch handling strategies; harmonizing ontologies and annotation depth; and developing modular, mix-and-match evaluation pipelines applicable to a variety of biological and AI-focused settings (Fahsbender et al., 14 Jul 2025, Yang et al., 17 Nov 2025).

7. Community and Ecosystem Implications

The movement toward open, collaborative platforms—hosted benchmarks, leaderboards, reference implementations (e.g., OpenProblems, Polaris)—is central for the maturation of cross-modal AI-biology benchmarks. Reproducibility is enforced via containerization, versioned workflows, and continuous bias audits. Recognition structures and interdisciplinary working groups incentivize high-quality benchmark creation and annotation, accelerating not only technical progress but also the translation of AI-driven modalities into robust, hypothesis-generating tools for real biological and medical investigation (Fahsbender et al., 14 Jul 2025, Cichy et al., 2019).

References:

(Cichy et al., 2019, Zhang et al., 21 Jul 2024, Burgess et al., 17 Mar 2025, Fahsbender et al., 14 Jul 2025, Gindra et al., 2 Aug 2025, Yang et al., 17 Nov 2025, Volkova et al., 23 Nov 2025)