# Auto-Comp: An Automated Pipeline for Scalable Compositional Probing of Contrastive Vision-Language Models

arXiv: https://arxiv.org/abs/2602.02043 · HTML: https://mlsanity.com/paper/2602.02043/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 21 of its 23 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 21 of 21)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [When and why vision-language models behave like bags-of-words, and what to do about it?](https://mlsanity.com/paper/2210.01936/) (2022)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality](https://mlsanity.com/paper/2204.03162/) (2022)
- [What's "up" with vision-language models? Investigating their struggle with spatial reasoning](https://mlsanity.com/paper/2310.19785/) (2023)
- [SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality](https://mlsanity.com/paper/2306.14610/) (2023)
- [CREPE: Can Vision-Language Foundation Models Reason Compositionally?](https://mlsanity.com/paper/2212.07796/) (2022)
- [VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena](https://mlsanity.com/paper/2112.07566/) (2021)
- [TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives](https://mlsanity.com/paper/2411.02545/) (2024)
- [Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding](https://mlsanity.com/paper/2312.00081/) (2023)
- [SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations](https://mlsanity.com/paper/2406.11171/) (2024)
- [CIVET: Systematic Evaluation of Understanding in VLMs](https://mlsanity.com/paper/2506.05146/) (2025)

## Abstract (arXiv)

Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referents. Existing benchmarks either rely on noisy real images that conflate confounding visual variables with the reasoning failure, or use simplistic synthetic scenes lacking the realism modern VLMs are tuned for. We introduce Auto-Comp, a fully automated, concept-driven pipeline that bridges this gap by generating photorealistic compositional benchmarks at scale. Its core innovation is a textitparallel A/B construction: for each concept, the pipeline emits a textitMinimal sample (template caption, isolated objects on a white background) and a textitContextual sample (LLM-rewritten caption, objects embedded in a realistic scene), isolating core binding ability from visio-linguistic complexity. We instantiate textitfour task families spanning the two canonical axes of compositional binding: textitColor and textitShape-Color (attribute binding), and textitPosition and textitRelative Size (relational binding). We evaluate over 25 VLMs spanning CLIP, SigLIP, hard-negative-trained, and frontier generative models. The findings are consistent across architectures and scales: every model exhibits a large Swap-vs-Confusion gap, with low-entropy distractors (e.g., repeated objects or colors) exposing failures textitbeyond the known bag-of-words limitations. We further uncover a task-dependent trade-off: visio-linguistic context aids relational reasoning but hinders attribute binding through visual clutter. We publicly release the pipeline and benchmarks.