Visual Language Models (VLMs)

• Visual Language Models (VLMs) are neural architectures that integrate visual and textual inputs to perform tasks like image captioning, visual question answering, and open-vocabulary classification.
• They employ techniques such as contrastive alignment, generative objectives, and masked modeling on large-scale multimodal datasets to achieve robust zero-shot and few-shot performance.
• Recent advances improve performance on benchmarks like ImageNet and VQA, yet challenges remain in handling low-level perceptual failures and modality dominance in real-world applications.

Visual LLMs (VLMs) are classes of neural architectures that combine computer vision and natural language processing to model, align, and reason over visual (e.g., images, video) and textual modalities. Formally, a VLM instantiates a conditional distribution $p_\theta(Y \mid V, T)$ for output $Y$ (often a textual sequence or multimodal action) given visual input $V$ and optional prompt or question $T$, where $\theta$ are the learnable parameters. VLMs underpin a broad spectrum of tasks, including but not limited to image captioning, visual question answering, open-vocabulary image classification, and embodied agent control. Since 2021, advances in architectures, pretraining objectives, and scalability have made VLMs the core enabler for robust zero-shot and few-shot multimodal inference, surpassing monomodal counterparts on numerous benchmarks (Li et al., 4 Jan 2025, Zhang et al., 2023, Bordes et al., 27 May 2024).

1. Architectures and Computational Patterns

VLM architectures can be categorized by how they handle cross-modal information flow, fusion, and alignment. The principal patterns are:

• Dual-Encoder (Contrastive) Models: Two independent encoders—typically a vision transformer or CNN for the image and a transformer for text—embed images and texts into a shared $d$-dimensional space, learning alignment via a contrastive loss ($L_\text{InfoNCE}$). At inference, tasks such as zero-shot classification reduce to nearest-neighbor search by similarity in embedding space (Li et al., 4 Jan 2025, Zhang et al., 2023). CLIP and its derivatives exemplify this class.
• Encoder-Decoder (Seq2Seq) Models: A visual encoder outputs a sequence of tokens pooled or attended into a decoder—often a transformer—that autoregressively generates output text. Training minimizes the language modeling loss over sequences conditioned on visual tokens (Li et al., 4 Jan 2025, Bordes et al., 27 May 2024).
• LLM-Backbone (Frozen LLM) Models: These architectures project visual tokens (e.g., by a trainable linear or MLP adapter) into the embedding space of a large, frozen LLM. Vision and prompt tokens are concatenated and processed by a unified transformer decoder. Examples include Flamingo, LLaVA, and most commercial frontier models (e.g., GPT-4V, Claude-3Vision) (Li et al., 14 Oct 2025, Kaduri et al., 26 Nov 2024).
• Single-Stream or Unified Models: A single transformer models a mixed sequence of image patches and text tokens, allowing arbitrary interleaving and flexible cross-modal attention (e.g., VisualBERT, ViLT, OneLLM) (Sharshar et al., 11 Feb 2025).

Hybrid variants combine contrastive and generative objectives, or introduce explicit fusion modules such as cross-attention layers or Q-formers (Fan et al., 30 Jan 2024).

2. Training Objectives and Data Regimes

VLMs achieve their zero-shot strength through large-scale pretraining over web-scale multimodal datasets. Core objectives include:

• Contrastive Alignment: Maximizes similarity between appropriately paired image/text representations and minimizes it for non-paired examples. The InfoNCE loss is widely adopted:

$$L_\text{InfoNCE} = -\frac{1}{N}\sum_{i=1}^N\log\frac{\exp(\operatorname{sim}(f_\text{vis}(V_i),f_\text{txt}(T_i))/\tau)}{\sum_{j=1}^N \exp(\operatorname{sim}(f_\text{vis}(V_i),f_\text{txt}(T_j))/\tau)},$$

where $\operatorname{sim}(\cdot, \cdot)$ typically denotes cosine similarity and $\tau$ is a learnable temperature (Li et al., 4 Jan 2025, Zhang et al., 2023).

• Generative (Captioning) Objectives: Maximize the likelihood of correct textual outputs conditioned on visual features, i.e., language modeling loss (Bordes et al., 27 May 2024):

$$L_\text{LM} = -\sum_{t=1}^M\log p_\theta(y_t | y_{<t}, z_V)$$

• Masked Modeling: Masked language modeling (MLM), masked image modeling (MIM), or cross-modal masked modeling (MCM) provide auxiliary supervision, helping models internalize fine-grained correspondences.
• Instruction Tuning: Supervised fine-tuning on instruction–response pairs, sometimes with reinforcement learning from human or LLM feedback, to align VLM outputs with generic prompts (“generate a caption,” “answer a question”) (Li et al., 4 Jan 2025).
• Auxiliary Losses: May include region–word matching, contrastive local alignment, consistency constraints, or explicit disentanglement losses to reduce spurious multimodal associations (Li et al., 14 Oct 2025).

Data scale, coverage, and curation are critical. Pretraining typically relies on datasets such as LAION-400M/5B, CC12M, RedCaps, and WIT, which provide hundreds of millions to billions of image–text pairs (Li et al., 4 Jan 2025, Bordes et al., 27 May 2024).

3. Advances, Capabilities, and Empirical Benchmarks

VLMs have achieved state-of-the-art results in several settings:

• Zero-shot ImageNet Classification: CLIP (ViT-L/14) achieves approx. 75–76% top-1; newer models (COCA, LiT) exceed 85% (Li et al., 4 Jan 2025).
• VQA and Open-ended Tasks: Decoder-based models (e.g., GPT-4V, Claude-3Vision) reach ≈87% accuracy on VQAv2 few-shot and >78% on complex MM-Vet/Multimodal QA suites (Li et al., 4 Jan 2025).
• Captioning: SDict-VLM (spectral dictionary mixer) closes ~85% of the gap to large baselines (BLIP-2) while using 60% fewer parameters and much less memory (Kiruluta et al., 22 Jun 2025).
• Robustness: Empirical evaluations show that VLMs are generally robust to synthetic image corruptions such as blur or color inversion, yet can be brittle under structured perturbations (e.g., large occlusions, out-of-domain text) (Li et al., 14 Oct 2025).

Tables summarizing representative VLMs and their main benchmarks are provided in (Sharshar et al., 11 Feb 2025):

Model	Fusion	Params	Key Benchmark	Metric
CLIP	Dual	400M	ImageNet zero-shot	68.3% top-1
ViLBERT	Dual	110M	VQA 2.0	70.6%
LXMERT	Dual	115M	RefCOCO+	60.8%
VisualBERT	Single	110M	COCO Captioning	CIDEr 117.5
ViLT	Single	86M	VQA	68.3%
MobileVLM-V2	Single	1.1B	Captioning (RT, SoC)	25ms latency
LightCLIP	Dual	2.45M	Image classification	60.1% top-1

VLMs continue to show gaps in language grounding, compositionality, and grounding for non-Western/underrepresented languages (Atuhurra et al., 29 Mar 2024). Analytical works have demonstrated that visual embeddings acquire geometry-aware compositional structure, which can be exploited for more robust classification and debiasing (Berasi et al., 21 Mar 2025).

4. Failure Modes and Diagnostic Methodologies

Despite their high-level capabilities, VLMs are prone to specific and sometimes systematic errors:

• Hallucination: VLMs may emit textual content not substantiated by the image, especially on symbolic or culturally iconic stimuli such as pure-symbol logos. Hallucination rates on pure-symbol logos exceed 50% (“Vision LLMs Map Logos to Text via Semantic Entanglement in the Visual Projector” (Li et al., 14 Oct 2025)). These errors persist after occlusion and robustness tests, indicating reliance on symbolic priors rather than genuine glyph perception, especially for iconic shapes (e.g., circles for automakers).
• Entanglement in Visual Projectors: Embedding-level analysis reveals that logo hallucination is tied to a small subset of visual→text projector dimensions. Targeted ablation of these subspaces (zeroing the top-32 activated projector dimensions) nearly halves the hallucination rate, with only minor loss in genuine OCR performance (Li et al., 14 Oct 2025).
• Fundamental Visual Deficits: Neuropsychological battery tests indicate widespread deficits in VLMs on low- and mid-level perceptual tasks (e.g., orientation, perceptual grouping, hidden contours), with Z-scores frequently below human clinical impairment cutoffs. High-level object naming remains strong, likely due to data-driven co-occurrence biases (Tangtartharakul et al., 15 Apr 2025).
• Modality Dominance & Model Routing: Integrating large LLMs confers no advantage for basic recognition tasks (scene/object classification); in fact, LLM-enhanced VLMs may underperform simpler, contrastively trained vision-language pipelines. Task-specific routing (even with small LLMs) that selects the appropriate expert per query outperforms static model combinations and approaches the accuracy of the best monolithic models at much lower cost (Cooper et al., 3 Oct 2024).

5. Robustness, Deployment, and Edge Considerations

VLMs targeted at deployment in edge environments encounter resource constraints (compute, memory, energy) necessitating model optimization:

• Compression Techniques: Structured pruning, quantization (especially 8/4-bit inference), and knowledge distillation have each been empirically shown to reduce model footprint with minimal accuracy loss (Sharshar et al., 11 Feb 2025). Adapter modules and prompt-based tuning further enable parameter-efficient adaptation.
• Specialized Hardware: Efficient use of edge TPUs and NPUs is achievable with quantized, structurally pruned networks.
• Privacy and Security: Federated fine-tuning with privacy-preserving aggregation and adversarial defenses (e.g., differential privacy, robust aggregation) are employed when VLMs are deployed in distributed or sensitive settings.
• Multisensor and Multimodal Fusion: For applications requiring RGB, depth, thermal, or audio, unified compact architectures and automated architecture search are important avenues of research (Sharshar et al., 11 Feb 2025, Dixit et al., 18 Nov 2024).

Example applications and their resource profiles are summarized in “Vision-LLMs for Edge Networks: A Comprehensive Survey” (Sharshar et al., 11 Feb 2025):

Domain	Model	Task	Accuracy	Latency	Memory (MB)
Healthcare	ViLMedic EDGE	VQA & captioning	78.4%	85 ms	512
Env. Monit.	ChangeCLIP	Change detection	85.2%	150 ms	1024
Auto-Vehicles	NuPrompt	Driving-scene prompts	72.1%	90 ms	512
Surveillance	UrbanTrack	Scene description	CIDEr 95.3	75 ms	256

6. Special Topics: Compositionality, Internal Structure, and Future Directions

Recent empirical and theoretical analysis of VLMs has illuminated several advanced properties:

• Compositional Structure: Visual embedding spaces in contrastive VLMs (e.g., CLIP) exhibit geodesic compositionality: embeddings for compound concepts can be approximately constructed by traversing Riemannian tangent directions corresponding to primitive factors. Geometry-aware decomposition (GDE) enables robust, generalizable zero-shot classification and group-robust debiasing (Berasi et al., 21 Mar 2025).
• Layerwise Multimodal Reasoning: Decoder-based VLMs encode global image summaries in prompt/query tokens at early layers, while fine-grained, spatially localized visual details are retrieved via attention only in mid-network layers. This suggests that only ≈25% of transformer depth is necessary for cross-modal transfer, and that both spatial and computational redundancy is present (Kaduri et al., 26 Nov 2024).
• Sequential Visual Understanding: VLMs process images via serialization (ViT patching), yet internal structure naturally decomposes via Gestalt and dual dorsal/ventral analogies—early attributes (“fur,” “red”), late semantics (“bear”), and positional/topological geometry (captured via RoPE). This motivates the development of instruction-agnostic token compression and new approaches to positional encoding (Li et al., 23 Sep 2025).
• Failure on Low-Level Perception: Despite achieving human-level or superhuman performance on high-level semantic tasks, VLMs lack competence in basic perceptual judgments (parallelism, aspect ratio, hidden-contour grouping), suggesting a disconnect between web-scale training and the developmental sequence of human vision (Tangtartharakul et al., 15 Apr 2025).
• Expanding Modal Coverage: VLMs can classify audio spectrograms and leverage in-context, few-shot learning to rival or exceed dedicated audio LLMs and match human experts on small datasets (Dixit et al., 18 Nov 2024).

Open challenges include: (1) robust handling of symbolic visual priors and projector entanglements (essential for logo/text reliability (Li et al., 14 Oct 2025)); (2) scalable edge deployment with dynamic resource budgeting; (3) effective, geometry-aware compositional embedding structures; (4) instruction-tuned and cross-modal compositionality; (5) explicit modeling for low-level perception; and (6) multilingual and multi-sensor expansion beyond the bias of English-centric or RGB data (Li et al., 4 Jan 2025, Atuhurra et al., 29 Mar 2024).

7. Recommendations and Prospective Directions

Researchers chart several concrete avenues for advancing VLM reliability and scope:

• Projector Disentanglement: Encourage orthogonalization or regularization in the vision-to-text projector to prevent symbolic visual features from mapping onto canonical brand or label tokens, as in logo hallucination (Li et al., 14 Oct 2025).
• OCR-Guided Decoding: Integrate specialized OCR modules to constrain LLM outputs, gating textual predictions by detected glyphs or using vocabulary restriction (Li et al., 14 Oct 2025).
• Flexible Multimodal Routing: Employ LLM-based or lightweight expert routers to select optimal sub-models per query, avoiding unnecessary fusion and mitigating performance regression on core visual tasks (Cooper et al., 3 Oct 2024).
• Hardware/Edge Integration: Co-design architectures to exploit structured pruning, quantization, and adaptive fusion layers, while leveraging federated learning and privacy-preserving updates for deployment in distributed scenarios (Sharshar et al., 11 Feb 2025).
• Geometry-Aware Regularization: Promote compositional geodesic structures in embedding spaces during pretraining to enhance group robustness and compositional generalization (Berasi et al., 21 Mar 2025).
• Explicit Perceptual Training: Augment pretraining with synthetic or curated data targeting basic visual concepts such as spatial relations, part-continuity, and occlusion to bridge the gap with human perceptual skills (Tangtartharakul et al., 15 Apr 2025).

By integrating findings from rigorous taxonomy, perturbation, and embedding-level analysis, the field is converging on a more interpretable, reliable, and efficient class of multimodal systems, with design principles informed by empirical diagnostics and theoretical advances across vision, language, and geometry (Li et al., 4 Jan 2025, Li et al., 14 Oct 2025, Bordes et al., 27 May 2024, Kaduri et al., 26 Nov 2024).