Analogical reasoning has long been a cornerstone of human intelligence, allowing knowledge transfer from one domain to another by identifying underlying relationships. In the field of artificial intelligence, multimodal large language models (MLLMs) have shown impressive advances in tasks combining text and images, but their ability to compose rules from multiple sources remained an unsolved challenge. The recent CARV (Compositional Analogical Reasoning in Vision) benchmark fills this gap by introducing a dataset of 5,500 samples specifically designed to evaluate compositional analogical reasoning. This article explores what CARV means for AI development, why current MLLMs fall short compared to human performance (100% vs. 40.4% accuracy from the best model), and how companies like Q2BSTUDIO are applying these insights to build smarter and more robust software solutions.
The core task of CARV extends the classic analogy from a single pair to multiple pairs. Instead of simply mapping relationships between two objects, models must extract symbolic rules from each pair and then compose new transformations. This reflects a level of abstraction closer to human cognition, where not only superficial similarity is recognized, but the rules governing visual transformations are understood. For example, an MLLM must observe how a geometric shape changes in a pair of images, identify the rule (such as rotation, color change, or scaling), and apply it to a new pair in a combined manner. The benchmark results are revealing: even the most advanced model, Gemini-2.5 Pro, barely reaches 40.4% accuracy, far from the human 100%. Furthermore, diagnostic analysis points to two systematic failures: difficulty in decomposing visual changes into symbolic rules and loss of robustness under diverse or complex settings.
From a technical and business perspective, this gap has profound implications. Companies seeking to integrate AI into their processes need models that not only classify images or generate text but reason flexibly in unfamiliar situations. In sectors such as industrial automation, cybersecurity, or business analysis, a model that cannot compose rules from multiple visual sources risks failing in critical scenarios. This is where the value of custom software development comes in. At Q2BSTUDIO we understand that AI is not a one-size-fits-all solution; every business challenge requires a tailored approach that combines language models, computer vision, and symbolic logic to achieve reliable compositional reasoning.
The CARV benchmark also highlights the need for more sophisticated AI agents. Autonomous agents operating in complex environments—such as cloud infrastructure management on AWS or Azure, cybersecurity threat monitoring, or generating Business Intelligence reports with Power BI—depend on the ability to interpret visual and compositional rules. For instance, a security system analyzing surveillance footage must infer behavioral patterns from multiple visual sources, something CARV shows current MLLMs do poorly. The AI industry is moving toward hybrid models combining neural networks with symbolic reasoning, a trend that Q2BSTUDIO supports by developing AI agents that integrate logic and deep learning to overcome the limits detected in benchmarks like CARV.
Moreover, the cybersecurity component cannot be overlooked. An MLLM that fails to understand compositional rules can be vulnerable to adversarial attacks where image manipulation deceives the model. Companies adopting cloud-based AI solutions need to ensure their systems are robust against such failures. That is why at Q2BSTUDIO we offer specialized cybersecurity services for cloud environments, ensuring that AI models are not only accurate on benchmarks but secure in production. Integration with AWS/Azure cloud allows scaling these systems, while business analytics with Power BI benefits from models that can interpret complex visualizations and correlate data from multiple sources.
Another key aspect is process automation through custom software. CARV results suggest current MLLMs are not suitable for tasks requiring real-time compositional reasoning, such as visual inspection in production lines or surgical assistance through medical image analysis. However, companies can overcome this limitation by combining pre-trained models with custom logic layers developed by expert teams. At Q2BSTUDIO we design architectures that integrate computer vision, generative AI, and business rules to create applications that do understand complex visual compositions. Our approach uses benchmarks like CARV as diagnostic tools to identify weaknesses in generic models and strengthen them with tailored solutions.
The reference to Power BI and Business Intelligence is especially relevant. In many organizations, visual reports are the basis for decision-making. A model that cannot reason analogically about charts, diagrams, or dashboards could misinterpret trends and relationships. Q2BSTUDIO helps companies implement BI solutions that leverage AI for predictive analytics, but always with a critical approach: we know MLLMs have limitations, so we combine the power of tools like Power BI with custom compositional logic to ensure the reliability of insights.
In summary, CARV is not just an academic benchmark; it is a warning and an opportunity. It warns about current limitations of multimodal models, but also opens the door to innovations in AI architecture design that are closer to human reasoning. For businesses, the conclusion is clear: generic AI is not enough; custom software solutions that integrate symbolic reasoning, computer vision, and intelligent agents are needed, all on secure cloud infrastructures with advanced business analytics. At Q2BSTUDIO we offer precisely that: a comprehensive approach that turns compositional reasoning challenges into competitive advantages for our clients. Whether developing multiplatform applications, deploying AI agents in the cloud, or strengthening the cybersecurity of their systems, we are ready to take artificial intelligence to the next level, beyond benchmarks.
