AI Papers Reader

Personalized digests of latest AI research

View on GitHub

AI Gets Smarter: Survey Reveals Breakthroughs in Multimodal Reasoning

Large language models (LLMs) have made impressive strides in recent years, mastering tasks from arithmetic to common-sense reasoning. However, extending these capabilities to the multimodal realm – where AI must integrate both visual and textual information – remains a significant hurdle. A new survey paper titled “Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning” (arXiv:2504.03151) offers a comprehensive overview of recent advancements, challenges, and future directions in this rapidly evolving field.

The paper highlights that multimodal reasoning introduces complexities not present in text-only models. For instance, imagine an AI system analyzing a photo of a cluttered desk and a user query like, “Is my phone charging?” The model must not only identify the phone, but also infer from the image whether it’s connected to a charger, resolving potential conflicts between the visual scene and the textual query. This requires advanced interpretative strategies, a key area of research highlighted in the survey.

A core concept discussed is “Chain-of-Thought” (CoT) prompting, inspired by how humans solve problems step-by-step. CoT encourages the model to articulate its internal reasoning process in natural language. For example, instead of directly answering “yes” or “no” to the phone charging question, the model might say: “First, I identify a phone on the desk. Second, I observe that a charging cable is connected to the phone. Therefore, the phone is charging.” This intermediate reasoning not only improves accuracy but also makes the AI’s decision-making process more transparent and verifiable.

Beyond CoT, the paper explores other prompting strategies, such as “generated knowledge prompting” and “tree search algorithms,” like STAR search, which expands the model’s reasoning capability. This is illustrated with an example of spatial relationships, which allows the models to clear up confusion when inferring.

The survey delves into post-training optimization techniques, where the model’s policy is refined to better align with desired outcomes. This includes methods like fine-tuning and reinforcement learning. Test-time compute strategies, which focus on improving performance during inference without altering the model’s core parameters, are also discussed extensively.

The study reviews numerous datasets and benchmarks crucial for evaluating multimodal reasoning capabilities. These benchmarks are designed to test different facets of reasoning, including spatial reasoning, temporal reasoning, and counterfactual reasoning. The paper points out how some datasets like VisualQA and TemporalVQA, demonstrate that AI models are still far from achieving human-level performance, showing only 15% correct versus 90% correct on spatial relations.

The authors stress that the field is moving towards “creative visual search”, where search capabilities are implemented directly within visual spaces. Another area of development lies with “Multi-Granularity Spatial Search,” allowing exploration across diverse spatial granularities. Also, the rewards system for AI can greatly benefit from deeper visual insights, which can allow for self-reflection based on visual information.

By bridging theoretical frameworks with practical implementations, the survey provides valuable insights for researchers and practitioners alike, and it sets the stage for future research aimed at building more robust, reliable, and human-aligned multimodal AI systems. Figure 1 in the paper shows that the amount of papers on visual reasoning grows steadily, suggesting the growing need of this area.