Multi-modal Scientific Representations

• Multi-modal scientific representations are integrated frameworks combining diverse data types, such as text, images, tables, and arrays, to encode and analyze scientific phenomena.
• They employ advanced techniques like contrastive learning, unified transformer architectures, and hierarchical latent variable models to achieve seamless modality alignment.
• These approaches enhance STEM assessments, scientific retrieval, and 3D scene understanding while addressing challenges in data annotation and cross-disciplinary reasoning.

Multi-modal scientific representations refer to the coordinated use and machine interpretation of heterogeneous information channels—such as images, diagrams, tables, text, and numerical arrays—to encode, retrieve, and reason about scientific phenomena, discoveries, and artifacts. This paradigm underpins both AI for Science research and the development of robust STEM educational tools, bridging the cognitive and technical gap between symbolic, visual, and linguistic data. Contemporary work spans the design of benchmarks, learning architectures, datasets, and evaluation protocols for joint reasoning across these diverse modes of scientific content.

1. Core Concepts and Taxonomies of Scientific Modalities

A multi-modal scientific representation incorporates at least two distinct modalities—textual, visual/diagrammatic, tabular, mathematical, experimental, or high-dimensional array-based—within a unified computational or assessment framework. In the context of learning progressions, such as NGSS-aligned classroom science, modalities include:

• Diagrams/drawings: capturing spatial or structural aspects (e.g., charge arrangements, force vectors in physics tasks).
• Short written explanations: expressing causal relationships or domain-specific concepts explicitly, complementing implicit diagrammatic reasoning (Kaldaras et al., 16 Sep 2025).
• Tables: structured data with hierarchical headers, metadata, and cell-level values; represented as images, LaTeX, HTML, XML, or dictionary objects (Borisova et al., 30 Jun 2025).
• Figures and experimental images: results plots, microscopy, apparatus photos, etc. (Wu et al., 2024, Wang et al., 1 Jan 2026, Tao et al., 14 Feb 2026).
• Mathematical or physical arrays: high-dimensional gridded data (e.g., Earth system fields, biomedical scans) (Yang et al., 4 Jan 2026).

Taxonomic hierarchies distinguish subtypes, especially in large datasets: for instance, S1-MMAlign groups visual data as 2D/3D plots (60%), experimental setups (15%), heatmaps (10%), microscopy (8%), and schematic diagrams (7%) (Wang et al., 1 Jan 2026); OmniScience uses a tripartite distinction: schematics & flowcharts, charts & plots, and experimental characterizations (Tao et al., 14 Feb 2026).

2. Representation Learning Techniques and Alignment Mechanisms

The central methodological innovation is the alignment or fusion of representations across modalities to create joint embedding spaces where scientific semantics are accessible, comparable, and interpretable.

• Contrastive learning: Training dual-encoder architectures (e.g., CLIP, BLIP) on paired image-text datasets by maximizing intra-pair similarity and minimizing inter-pair similarity in the joint embedding space. InfoNCE is the typical loss function:

$$\mathcal{L}_{\rm ITC} \;=\;-\,\frac{1}{N}\sum_{i=1}^N \Bigl[\log\frac{\exp(\langle v_i,t_i\rangle/\tau)}{\sum_{j=1}^N\exp(\langle v_i,t_j\rangle/\tau)} + \log\frac{\exp(\langle v_i,t_i\rangle/\tau)}{\sum_{j=1}^N\exp(\langle v_j,t_i\rangle/\tau)}\Bigr]$$

(Wu et al., 2024, Wang et al., 1 Jan 2026, Tao et al., 14 Feb 2026).

• Hierarchical latent variable models: MHVAE extends multi-modal VAEs by stacking latent variables at multiple spatial scales and fusing information across incomplete modalities via product-of-experts, robustly inferring shared structure even with missing data (Dorent et al., 2023).
• Unified transformer-based architectures: FuXi-Uni employs a shared transformer over tokens drawn from language, high-dimensional arrays, and images, aligning all via an L2 loss over paired samples and supporting both generation and comprehension tasks (Yang et al., 4 Jan 2026).
• Extensible contrastive mapping: Ex-MCR enables the composition of multiple pre-trained multi-modal contrastive spaces (e.g., CLIP, CLAP, ULIP) via pseudo-pair mining, decoupled projection layers, and a dense InfoNCE loss, preserving base alignments and emergently aligning 3D, audio, text, and vision in a unified representation without requiring new paired data (Wang et al., 2023).
• Gaussian splatting and voxel grounding: Object-X binds multiple modalities (image, point cloud, text) into structured latent grids, compressed into a compact code directly decodable into explicit, geometric 3D representations amenable to downstream scientific tasks (Lorenzo et al., 5 Jun 2025).

3. Dataset Foundations and Benchmarking Initiatives

Large-scale, curated, and semantically enriched datasets are foundational for robust multi-modal scientific representation learning.

Dataset	Modalities	Scale (Pairs/Samples)	Key Features
S1-MMAlign (Wang et al., 1 Jan 2026)	Image–text (figures, charts, microscopy)	15.5M	AI recaptioning, semantic enhancement, cross-discipline
OmniScience (Tao et al., 14 Feb 2026)	Figure–caption–context	1.5M triplets, 5M subfigures	Dense, routed expert recaptioning, subfigure/paragraph hierarchy
Multimodal ArXiv (Li et al., 2024)	Figure–caption, QA	6.4M images, 3.9M captions	Fine-grained, domain coverage, synthetic figure QA
SciMMIR (Wu et al., 2024)	Figure/table–caption	530k	Subset/subcategory annotations, figure/table specificity
TableEval (Borisova et al., 30 Jun 2025)	Table (image, LaTeX, etc.)	3,017 tables, 11,312 Q/A	Multi-format, scientific/non-scientific splits
SciFIBench (Roberts et al., 2024)	Figure–caption pairing	2,000 QA, 94k pool	Adversarial negative mining, reasoning faithfulness

Semantic enhancement—especially recaptioning via vision-LLMs leveraging title, abstract, and in-text context—raises alignment scores between images and text up to 0.18 (S1-MMAlign) or from 0.77 to 0.96 (OmniScience), with gains in downstream QA and retrieval (Wang et al., 1 Jan 2026, Tao et al., 14 Feb 2026). Hierarchical subfigure parsing and context paragraph association provide finer-grained reasoning cues beyond traditional captioning.

4. Model Architectures and Evaluation Protocols

State-of-the-art multi-modal scientific models exhibit diversity in fusion strategies, modality encoders, and evaluation routines.

• Fusion mechanisms: Early fusion (concatenation), cross-attention, and Q-former-style query pooling are common, especially in large models such as LLaVA, Qwen-VL, and IDEFICS (Roberts et al., 2024).
• Specialization for tables: Robustness to LaTeX, HTML, XML, image, and dictionary representations is observed, but scientific tables (dense, jargon-rich) lag in recovery (BertScore.F1 drop from 0.87 → 0.83 compared to non-scientific) (Borisova et al., 30 Jun 2025). OCR integration is essential to elevate table performance (Wu et al., 2024).
• Domain-specific fine-tuning: Performance on vision-to-text captioning and mathematical reasoning (e.g., on MathVista or SciFIBench) rises substantially with in-domain fine-tuning (e.g., +10.4 points in multimodal math reasoning with ArXivQA/ArXivCap in Multimodal ArXiv (Li et al., 2024)).
• Instruction-following and faithfulness: SciFIBench exposes LMMs’ susceptibility to spurious input tags and variable compliance with “ignore this hint” instructions, highlighting non-trivial gaps between human and model alignment in figure–caption QA (Roberts et al., 2024).

5. Applications and Impact: STEM Assessment, Retrieval, and Generation

Multi-modal scientific representations enable benchmarks and workflows for:

• Automated educational assessment: LP-guided ML scoring of multi-modal student artifacts (drawings + texts) supports rubric-based feedback targeting missing or incorrect mechanistic reasoning, validated in NGSS-aligned classrooms (Kaldaras et al., 16 Sep 2025).
• Chemical document retrieval: Unified representations over molecular diagrams (SMILES/graph/fingerprint), reaction tables, and text enable cross-modal search—chemists can input textual, structural, or reaction-based queries and retrieve linked evidence (Shah et al., 24 Feb 2025).
• 3D scene and object understanding: Compact, geometry-grounded latent codes integrate vision, text, point cloud, and attribute information, supporting synthesis, alignment, and localization in scientific imaging contexts (e.g., organ reconstruction, geoscience embedding) (Lorenzo et al., 5 Jun 2025).
• Unified forecasting and analysis: Models such as FuXi-Uni enable both high-fidelity numerical prediction (e.g., global weather or biomedical VQA) and natural language understanding within a common token space (Yang et al., 4 Jan 2026).
• Scientific information retrieval and QA: Dual-encoder models fine-tuned on figure–text pairs (e.g., SciMMIR, S1-MMAlign) or multimodal datasets (OmniScience, Multimodal ArXiv) support cross-modal search, QA, and downstream knowledge extraction (Wu et al., 2024, Wang et al., 1 Jan 2026, Li et al., 2024).

6. Open Challenges and Future Directions

Despite progress, several key challenges and research directions persist:

• Semantic abstraction: Achieving representations that capture high-level shared semantics, discarding modality-specific noise, remains theoretically and practically challenging. Bottlenecked autoencoding can force this abstraction, as argued in the information-theoretic analysis of abstraction via lossy compression (Wilmot et al., 2021).
• Robust multi-modal alignment without paired data: Adapter-based architectures (e.g., Ex-MCR) empirically demonstrate emergent semantic alignment across four or more modalities via pseudo-pair mining and projection, but their efficacy in specialized scientific domains or under severe modality gaps requires further exploration (Wang et al., 2023).
• Faithful scientific reasoning and instruction following: Benchmarks like SciFIBench surface LMMs’ tendency to “cheat” via superficial hints and struggle with truly adversarial negatives; narrowing the gap to human-level robust reasoning is an ongoing challenge (Roberts et al., 2024).
• Scaling, annotation, and generalization: Datasets are rapidly increasing in scale (S1-MMAlign, OmniScience), but generalization across new scientific disciplines and multi-modal formats hinges on further improvements in annotation (context paragraphs, subfigure granularity), quality filtering, and cross-modal reasoning.
• Integration of structured outputs and cross-domain knowledge: Downstream tasks will benefit from explicit schema outputs (e.g., JSON) for tables (Borisova et al., 30 Jun 2025), as well as general-purpose architectures integrating additional domains (materials, astronomy, geosciences) in a plug-and-play fashion (Yang et al., 4 Jan 2026).

Ongoing research continues to refine architecturally unified, data-rich, and semantically grounded multi-modal scientific representations as a backbone for interpretable AI-driven discovery, robust assessment, and cross-disciplinary knowledge integration.