{"id":1346,"date":"2025-09-29T08:06:32","date_gmt":"2025-09-29T08:06:32","guid":{"rendered":"https:\/\/scipapermill.com\/index.php\/2025\/09\/29\/multimodal-large-language-models-a-leap-towards-unified-intelligent-understanding\/"},"modified":"2025-12-28T22:03:54","modified_gmt":"2025-12-28T22:03:54","slug":"multimodal-large-language-models-a-leap-towards-unified-intelligent-understanding","status":"publish","type":"post","link":"https:\/\/scipapermill.com\/index.php\/2025\/09\/29\/multimodal-large-language-models-a-leap-towards-unified-intelligent-understanding\/","title":{"rendered":"Multimodal Large Language Models: A Leap Towards Unified, Intelligent Understanding"},"content":{"rendered":"

Latest 50 papers on multimodal large language models: Sep. 29, 2025<\/h3>\n

Multimodal Large Language Models (MLLMs) are revolutionizing how AI interacts with and interprets the world, moving beyond text to encompass vision, audio, and even 3D environments. This rapidly evolving field is pushing the boundaries of what\u2019s possible, tackling challenges from nuanced human-like reasoning to robust real-world deployment. Recent research showcases incredible strides in integrating diverse modalities, enhancing model efficiency, and fortifying safety\u2014all while pushing towards a more unified and intelligent AI.<\/p>\n

The Big Idea(s) & Core Innovations<\/h3>\n

At the heart of these advancements lies a common ambition: to enable MLLMs to perceive, reason, and act with human-like proficiency across multiple data types. A central theme is the development of unified frameworks<\/em> that seamlessly blend different modalities. For instance, OmniBridge: Unified Multimodal Understanding, Generation, and Retrieval via Latent Space Alignment<\/strong> from Tsinghua University<\/a> introduces a framework for latent space alignment that unifies understanding, generation, and retrieval tasks. Similarly, MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer<\/strong> by Apple<\/a> tackles the challenge of integrating vision understanding and image generation within a single model, using a hybrid tokenizer to balance continuous embeddings for understanding and discrete tokens for generation.<\/p>\n

Another significant area of innovation is enhancing reasoning capabilities through targeted training and novel architectural components<\/em>. Papers like MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning<\/strong> from HKUST (GZ), HKUST, HIT<\/a> propose a reinforcement learning framework with process reasoning rewards to boost video temporal understanding. Expanding on this, VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception<\/strong> by researchers including Zhejiang University<\/a> introduces Visual Test-Time Scaling (VTTS) to enhance MLLMs through iterative visual perception, mimicking human hierarchical attention. For geometric reasoning, GeoRef: Referring Expressions in Geometry via Task Formulation, Synthetic Supervision, and Reinforced MLLM-based Solutions<\/strong> from authors associated with LLAVA-VL GitHub, OpenAI<\/a> leverages synthetic supervision and reinforcement learning to improve MLLM performance in complex geometry tasks. In the medical domain, LLaVA-RadZ: Can Multimodal Large Language Models Effectively Tackle Zero-shot Radiology Recognition?<\/strong> by East China Normal University<\/a> presents a framework and tailored training strategies to improve zero-shot medical disease recognition from radiology images.<\/p>\n

Efficiency and robustness<\/em> are also key drivers. Sparse Training Scheme for Multimodal LLM<\/strong> from Peking University, University of Illinois Urbana-Champaign<\/a> introduces a Sparse Training Scheme (STS) with a Visual Token Compressor and Layer Dynamic Skipper to significantly reduce training overhead. In a similar vein, MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe<\/strong> by OpenBMB<\/a> details improvements in architecture, data strategy, and training methods to create powerful MLLMs with reduced computational costs. Addressing real-world deployment challenges, Adaptive Guidance Semantically Enhanced via Multimodal LLM for Edge-Cloud Object Detection<\/strong> from Institute of Computing Technology, Chinese Academy of Sciences<\/a> offers an adaptive guidance framework for efficient edge-cloud collaborative object detection. Furthermore, MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference<\/strong> by a similar group of researchers from Institute of Computing Technology, Chinese Academy of Sciences<\/a> proposes dynamic workload scheduling based on modality-specific complexity to reduce latency and resource overhead in MLLM inference.<\/p>\n

Safety, trustworthiness, and ethical considerations<\/em> are increasingly paramount. SUA: Stealthy Multimodal Large Language Model Unlearning Attack<\/strong> from The Pennsylvania State University, Amazon<\/a> exposes vulnerabilities in MLLM unlearning processes, showing how forgotten knowledge can be recovered via adversarial perturbations. Complementary to this, SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning<\/strong> by The Hong Kong University of Science and Technology (Guangzhou)<\/a> introduces a benchmark and novel techniques like Prompt Decouple Loss to enhance safety unlearning without over-forgetting. Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models<\/strong> by ByteDance, CASIA<\/a> addresses the critical problem of OCR hallucinations in MLLMs when processing degraded documents, introducing a new benchmark and a GRPO-based framework. Finally, Both Text and Images Leaked! A Systematic Analysis of Data Contamination in Multimodal LLM<\/strong> from Lehigh University, The Chinese University of Hong Kong, Shenzhen<\/a> provides a critical look at data contamination across MLLMs, highlighting its prevalence and impact on benchmarks.<\/p>\n

Under the Hood: Models, Datasets, & Benchmarks<\/h3>\n

This collection of papers highlights the critical role of specialized models, expansive datasets, and rigorous benchmarks in driving MLLM progress. Here\u2019s a snapshot of key resources:<\/p>\n