# A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges

arXiv: https://arxiv.org/abs/2501.02189 · HTML: https://mlsanity.com/paper/2501.02189/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 145 of its 226 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)
- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)
- LLM alignment / RLHF / preference optimisation / [Diffusion and multimodal model alignment](https://mlsanity.com/alignment/diffusion-and-multimodal-model-alignment/)

## Builds on (most cited first, 30 of 145)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)

## Built on by (most cited first, 30 of 47)

- [Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers](https://mlsanity.com/paper/2506.23918/) (2025)
- [Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills](https://mlsanity.com/paper/2604.05333/) (2026)
- [ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness](https://mlsanity.com/paper/2504.10514/) (2025)
- [DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning](https://mlsanity.com/paper/2505.20241/) (2025)
- [SpinBench: Perspective and Rotation as a Lens on Spatial Reasoning in VLMs](https://mlsanity.com/paper/2509.25390/) (2025)
- [VACoT: Rethinking Visual Data Augmentation with VLMs](https://mlsanity.com/paper/2512.02361/) (2025)
- [Small Drafts, Big Verdict: Information-Intensive Visual Reasoning via Speculation](https://mlsanity.com/paper/2510.20812/) (2025)
- [CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models](https://mlsanity.com/paper/2506.11034/) (2025)
- [ORIGAMISPACE: Benchmarking Multimodal LLMs in Multi-Step Spatial Reasoning with Mathematical Constraints](https://mlsanity.com/paper/2511.18450/) (2025)
- [SemVink: Advancing VLMs' Semantic Understanding of Optical Illusions via Visual Global Thinking](https://mlsanity.com/paper/2506.02803/) (2025)
- [Collaborative Multi-Agent Scripts Generation for Enhancing Imperfect-Information Reasoning in Murder Mystery Games](https://mlsanity.com/paper/2604.11741/) (2026)
- [MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models](https://mlsanity.com/paper/2511.18373/) (2025)
- [Limits and Gains of Test-Time Scaling in Vision-Language Reasoning](https://mlsanity.com/paper/2512.11109/) (2025)
- [HUMORCHAIN: Theory-Guided Multi-Stage Reasoning for Interpretable Multimodal Humor Generation](https://mlsanity.com/paper/2511.21732/) (2025)
- [MARE: Multimodal Alignment and Reinforcement for Explainable Deepfake Detection via Vision-Language Models](https://mlsanity.com/paper/2601.20433/) (2026)
- [VisChainBench: A Benchmark for Multi-Turn, Multi-Image Visual Reasoning Beyond Language Priors](https://mlsanity.com/paper/2512.06759/) (2025)
- [Thinking in Structures: Evaluating Spatial Intelligence in Constraint-Governed Spaces](https://mlsanity.com/paper/2602.07864/) (2026)
- [Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality](https://mlsanity.com/paper/2505.19696/) (2025)
- [FC-MIR: A Mobile Screen Awareness Framework for Intent-Aware Recommendation based on Frame-Compressed Multimodal Trajectory Reasoning](https://mlsanity.com/paper/2512.19107/) (2025)
- [ArchSIBench: Benchmarking the Architectural Spatial Intelligence of Vision-Language Models](https://mlsanity.com/paper/2605.20837/) (2026)
- [BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models](https://mlsanity.com/paper/2506.05440/) (2025)
- [COREVQA: A Crowd Observation and Reasoning Entailment Visual Question Answering Benchmark](https://mlsanity.com/paper/2507.13405/) (2025)
- [FOUNDv2: Learning Unified User Quantized Tokenizers for User Representation](https://mlsanity.com/paper/2508.00956/) (2025)
- [Understanding Space Is Rocket Science -- Only Top Reasoning Models Can Solve Spatial Understanding Tasks](https://mlsanity.com/paper/2509.02175/) (2025)
- [AgentCaster: Reasoning-Guided Tornado Forecasting](https://mlsanity.com/paper/2510.03349/) (2025)
- [AgroCoT: A Chain-of-Thought Benchmark for Evaluating Reasoning in Vision-Language Models for Agriculture](https://mlsanity.com/paper/2511.23253/) (2025)
- [COVLM-RL: Critical Object-Oriented Reasoning for Autonomous Driving Using VLM-Guided Reinforcement Learning](https://mlsanity.com/paper/2512.09349/) (2025)
- [From Building Blocks to Planning: Multi-Step Spatial Reasoning in LLMs with Reinforcement Learning](https://mlsanity.com/paper/2512.24532/) (2025)
- [It's Time to Get It Right: Improving Analog Clock Reading and Clock-Hand Spatial Reasoning in Vision-Language Models](https://mlsanity.com/paper/2603.08011/) (2026)
- [Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models](https://mlsanity.com/paper/2606.05531/) (2026)

## Abstract (arXiv)

Large vision-language models (VLMs) have evolved rapidly from contrastive image-text encoders and adapter-based assistants into natively multimodal foundation models that support long-context reasoning, agentic workflows, and, increasingly, unified perception-generation-action loops. Since 2025, the frontier has consolidated around a small number of large pretraining families, including GPT, Gemini, Claude, Grok, Qwen, Gemma, DeepSeek, Kimi, and MiniMax, while evaluation has shifted from short-form visual question answering toward spatial, temporal, embodied, and calibration-sensitive benchmarks. In this survey, we provide an updated overview of VLMs through 2026 with an emphasis on three developments. First, we trace the architectural evolution from contrastive or bridged two-tower models to LLM-backbone adapter models, native multimodal-input models, omni-modal unified input/output models, and emerging world-action models. Second, we summarize how benchmarks expanded from classical OCR, VQA, and chart understanding toward long-video reasoning, embodied evaluation, reward-model judging, and domain-specific decision making. Third, we review the growing role of post-training, including supervised fine-tuning, preference optimization, and reinforcement learning methods such as GRPO-style multimodal alignment. Rather than exhaustively listing every release, we focus on representative frontier families, influential benchmarks, and the major open challenges that remain in hallucination, safety, efficiency, data quality, and multimodal alignment.