Research: Research: Multimodal Large Language Models: Navigating Safety, Reasoning, and Real-world Applications
Latest 53 papers on multimodal large language models: Jan. 24, 2026
Multimodal Large Language Models (MLLMs) are revolutionizing how AI interacts with the world, bridging the gap between language and diverse sensory inputs like vision, audio, and even neural signals. This rapidly evolving field is pushing boundaries, but also surfacing critical challenges in areas like safety, robustness, and true understanding of complex real-world phenomena. Recent research delves deep into these facets, offering groundbreaking advancements and crucial benchmarks that promise to shape the future of intelligent systems.
The Big Idea(s) & Core Innovations
At the heart of recent MLLM progress lies a dual focus: enhancing core reasoning capabilities and ensuring responsible deployment. One major theme is the quest for more robust and secure MLLMs. The paper, “Provable Robustness in Multimodal Large Language Models via Feature Space Smoothing” by Song Xia and colleagues from Nanyang Technological University, introduces Feature-space Smoothing (FS) to offer certified robustness against adversarial attacks, a critical step towards building trustworthy MLLMs. Complementing this, research from Beijing University of Posts and Telecommunications in “Beyond Visual Safety: Jailbreaking Multimodal Large Language Models for Harmful Image Generation via Semantic-Agnostic Inputs” (Mingyu Yu et al.) reveals vulnerabilities where MLLMs can be tricked into generating harmful images, underscoring the urgency for stronger safety alignments. This concern is further echoed by the comprehensive evaluation in “A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Doubao 1.8, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5” by Xingjun Ma et al. from Fudan University, which highlights heterogeneous safety landscapes and persistent jailbreak vulnerabilities across frontier models, even those deemed state-of-the-art like GPT-5.2.
Another significant innovation focuses on making MLLMs smarter and more efficient in complex reasoning tasks. Tsinghua University researchers, in “AStar: Boosting Multimodal Reasoning with Automated Structured Thinking” (Jinyang Wu et al.), propose a training-free framework that uses ‘thought cards’ to guide structured reasoning, significantly outperforming models like GPT-4o in visual reasoning. For video understanding, “Event-VStream: Event-Driven Real-Time Understanding for Long Video Streams” by Zhenghui Guo et al. (University of Houston) introduces an event-aware framework to process long videos efficiently, mimicking human perception. Fudan University’s “HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding” (Haowei Zhang et al.) pushes this further by reusing KV cache for real-time streaming video understanding, achieving substantial speedups. Meanwhile, “Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring” by Dongxu Zhang et al. (Xi’an Jiaotong University) addresses CoT inefficiency by selectively preserving visually critical tokens, demonstrating a 2.9x speedup.
Beyond general reasoning, papers also tackle specialized domains. For medical AI, “Incentivizing Cardiologist-Like Reasoning in MLLMs for Interpretable Echocardiographic Diagnosis” (Yi Qin et al., HKUST) introduces CardiacMind, a reinforcement learning framework that aligns MLLMs with cardiologist reasoning for echocardiographic diagnosis. Similarly, “M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding” by Juntao Jiang et al. (ZJU) highlights the need for evaluating not just answers, but transparent reasoning paths in medical image understanding. Peking University and collaborators introduce “PhysicsMind: Sim and Real Mechanics Benchmarking for Physical Reasoning and Prediction in Foundational VLMs and World Models”, uncovering that current models struggle with physics-based reasoning, often relying on appearance heuristics. Another crucial area of focus is human-AI interaction. “Human-AI Alignment of Multimodal Large Language Models with Speech-Language Pathologists in Parent-Child Interactions” by Weiyan Shi and Kenny Tsu Wei Choo (Singapore University of Technology and Design) demonstrates how MLLMs can be aligned with human experts (SLPs) to interpret complex social behaviors.
Under the Hood: Models, Datasets, & Benchmarks
Recent research heavily relies on and contributes to a robust ecosystem of specialized models, datasets, and benchmarks:
- PhysicsMind Benchmark: Introduced in “PhysicsMind: Sim and Real Mechanics Benchmarking for Physical Reasoning and Prediction in Foundational VLMs and World Models” by Mak et al. (Peking University) for evaluating VLMs on physics-aware reasoning and prediction. It uses law-specific tasks like center-of-mass alignment and lever equilibrium.
- BVS Framework & Benchmark Dataset: From “Beyond Visual Safety: Jailbreaking Multimodal Large Language Models for Harmful Image Generation via Semantic-Agnostic Inputs” (Yu et al., Beijing University of Posts and Telecommunications), this is a new image-text pair jailbreaking framework, achieving a 98.21% jailbreak success rate against GPT-5. The code is available at https://github.com/Steganographyer/JailBreak_MLLM.
- Event-VStream Framework: In “Event-VStream: Event-Driven Real-Time Understanding for Long Video Streams” by Guo et al. (University of Houston), this event-aware framework uses an event boundary detector and a lightweight event-level memory bank for real-time video understanding, showing performance improvements with LLaMA-3-8B.
- REVEAL-CXR Dataset: “RSNA Large Language Model Benchmark Dataset for Chest Radiographs of Cardiothoracic Disease: Radiologist Evaluation and Validation Enhanced by AI Labels (REVEAL-CXR)” by Wei et al. (Weill Cornell Medicine) is a high-quality benchmark of 200 chest radiographic studies with 12 cardiothoracic findings, validated by radiologists.
- LiViBench & LiVi-LLM-7B: Introduced in “LiViBench: An Omnimodal Benchmark for Interactive Livestream Video Understanding” by Wang et al. (Peking University), LiViBench is the first omnimodal benchmark for interactive livestream videos, featuring a semi-automatic annotation workflow. Its accompanying model, LiVi-LLM-7B, with tailored instruction tuning and a Video-to-Comment Retrieval (VCR) module, is open-source at https://github.com/Wang-Xiaodong1899/LiViBench.
- HERMES Framework: Developed by Zhang et al. from Fudan University in “HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding”, it’s a training-free architecture leveraging hierarchical KV cache management for efficient streaming video understanding. Code is available at https://github.com/haowei-freesky/HERMES.
- MIR-SafetyBench: From Tsinghua University’s Renmiao Chen et al. in “The Side Effects of Being Smart: Safety Risks in MLLMs’ Multi-Image Reasoning”, this is the first comprehensive benchmark for evaluating multi-image reasoning safety in MLLMs. The code is available at https://github.com/thu-coai/MIR-SafetyBench.
- CausalSpatial Benchmark & COW Framework: “CausalSpatial: A Benchmark for Object-Centric Causal Spatial Reasoning” by Ma et al. (Johns Hopkins University) evaluates causal spatial reasoning. Their CAUSAL OBJECT WORLD MODEL (COW) framework enables models to simulate object motion, and the code is available at https://github.com/CausalSpatial/CausalSpatial.
- Enginuity Dataset: Bayer et al. from Predii AI and USC introduce “Enginuity: Building an Open Multi-Domain Dataset of Complex Engineering Diagrams”, a large-scale, expert-labeled, multi-domain dataset of complex engineering diagrams. Code is at https://github.com/predii-ai/engineering-diagram-dataset.
- INTEGRITY-BENCH & DOPE Framework: “DoPE: Decoy Oriented Perturbation Encapsulation Human-Readable, AI-Hostile Documents for Academic Integrity” by Shekhar et al. (Arizona State University) introduces INTEGRITY-BENCH, a novel benchmark of 1826 exams with watermarked variants for evaluating document-layer defenses against AI assistance. The code is at https://github.com/ArizonaStateUniversity/INTEGRITY-BENCH.
- Q-Probe & Vista-Bench: “Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing” by Li et al. (USTC, Hefei University of Technology) presents Q-Probe, an agentic IQA model for high-resolution scenarios, along with Vista-Bench, a new benchmark for fine-grained degradation analysis. Datasets Probe-CoT-3K and Probe-RL-4K are also provided.
- SLAM-LLM: Xie Chen (Shanghai Jiao Tong University) introduces “SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing”, an open-source framework for multimodal inputs (speech, language, audio, music). Code is at https://github.com/X-LANCE/SLAM-LLM.
- SMORE Framework: “See More, Store Less: Memory-Efficient Resolution for Video Moment Retrieval” by Jeon et al. (Chung-Ang University) enhances memory efficiency in video moment retrieval through query-guided caption generation and structured visual compression.
- MCGA Corpus: Du et al. (Harbin Institute of Technology) present “MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus”, the first open-source, fully copyrighted audio corpus for classical Chinese literature. Code is at https://github.com/yxduir/MCGA.
- UR-Bench: “UR-Bench: A Benchmark for Multi-Hop Reasoning over Ultra-High-Resolution Images” by Li et al. (Zhejiang University) is a benchmark for multi-hop reasoning on ultra-high-resolution images, crucial for real-world applications.
- GI-Bench: Zhu et al. (Fudan University, Microsoft Research Asia) introduce “GI-Bench: A Panoramic Benchmark Revealing the Knowledge-Experience Dissociation of Multimodal Large Language Models in Gastrointestinal Endoscopy Against Clinical Standards”, a comprehensive benchmark for evaluating MLLMs in gastrointestinal endoscopy.
- E²-LLM: “E²-LLM: Bridging Neural Signals and Interpretable Affective Analysis” by Ma et al. (Guangdong Laboratory of AI and Digital Economy) is the first MLLM for interpretable emotion analysis from EEG signals.
- MLLM-VADStory: Yang et al. (Meta) present “MLLM-VADStory: Domain Knowledge-Driven Multimodal LLMs for Video Ad Storyline Insights”, a framework for large-scale video ad storyline understanding using real-world ads.
- LLaVAction & EPIC-KITCHENS-100-MQA: Qi et al. (EPFL) introduce “LLaVAction: evaluating and training multi-modal large language models for action understanding”, a model for enhancing MLLMs’ action understanding, along with a reformulated benchmark from EPIC-KITCHENS-100. Code is available at https://github.com/AdaptiveMotorControlLab/LLaVAction.
- SIN-Bench & SIN-Data: Ren et al. (Tsinghua University) introduce “SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature”, a benchmark to evaluate MLLMs on explicit cross-modal evidence chains in scientific documents, with code at https://github.com/IIGROUP/sin-bench.
- DR2Seg: He et al. (National University of Defense Technology) propose “DR2Seg: Decomposed Two-Stage Rollouts for Efficient Reasoning Segmentation in Multimodal Large Language Models”, a self-rewarding framework for efficient reasoning segmentation.
- Omni-R1: Cheng et al. (The Hong Kong Polytechnic University) introduce “Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning”, a framework unifying multimodal reasoning through generative image creation during reasoning steps. Code is at https://github.com/ModalityDance/Omni-R1.
- Video-MSR & MSR-9K: Zhu et al. (Baidu Inc.) define and benchmark Multi-hop Spatial Reasoning in dynamic videos in “Video-MSR: Benchmarking Multi-hop Spatial Reasoning Capabilities of MLLMs”, and curate MSR-9K, a specialized instruction-tuning dataset.
- FutureOmni: Chen et al. (Fudan University) introduce “FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs”, a benchmark for evaluating future forecasting in MLLMs using audio-visual inputs. Code and dataset at https://github.com/OpenMOSS/FutureOmni.
- Docs2Synth: Ding et al. (University of Western Australia) present “Docs2Synth: A Synthetic Data Trained Retriever Framework for Scanned Visually Rich Documents Understanding”, leveraging synthetic data to train lightweight visual retrievers for document understanding. Code is at https://github.com/docling-project/docling.
- Hummus Dataset: Tong et al. (University of Amsterdam) introduce “Hummus: A Dataset of Humorous Multimodal Metaphor Use” for analyzing humorous multimodal metaphors in image-caption pairs. Code at github.com/xiaoyuisrain/humorous-multimodal-metaphor-use.
- REF-VLM: Author A and Author B introduce “REF-VLM: Triplet-Based Referring Paradigm for Unified Visual Decoding”, a triplet-based referring paradigm for unifying visual decoding. Code at https://github.com/REF-VLM/REF-VLM.
- FaceXBench: Narayan et al. (University of Michigan) present “FaceXBench: Evaluating Multimodal LLMs on Face Understanding”, a benchmark for evaluating MLLMs in face understanding. Code at https://github.com/open-compass/VLMEvalKit.
- KidVis: Doe et al. (University of Cambridge) introduce “KidVis: Do Multimodal Large Language Models Possess the Visual Perceptual Capabilities of a 6-Year-Old?”, a benchmark for evaluating visual perception in MLLMs. Code at https://github.com/KidVis/KidVis.
- ChartAttack & AttackViz: Ortiz-Barajas et al. (INSAIT) introduce “ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation”, a framework for generating misleading charts and AttackViz, a multi-label chart QA dataset. Code at https://github.com/insait-institute/chartAttack.
- ChartComplete: Mustapha et al. (American University of Beirut) introduce “ChartComplete: A Taxonomy-based Inclusive Chart Dataset”, a comprehensive dataset of thirty chart types for MLLM evaluation.
- ROMA: Tian et al. (CAS Key Laboratory of AI Safety) introduce “ROMA: Real-time Omni-Multimodal Assistant with Interactive Streaming Understanding”, a real-time omni-multimodal assistant for streaming audio-video understanding.
- Optimizing MLLMs for Egocentric Video Understanding: Yang et al. (HD-EPIC VQA Challenge solution) outline a framework using Temporal Chain-of-Thought (T-CoT) prompting for egocentric video understanding in “Optimizing Multimodal LLMs for Egocentric Video Understanding: A Solution for the HD-EPIC VQA Challenge”.
- Advancing Adaptive Multi-Stage Video Anomaly Reasoning: Wang, Zhang, and Liu introduce a new benchmark dataset and method for video anomaly reasoning in “Advancing Adaptive Multi-Stage Video Anomaly Reasoning: A Benchmark Dataset and Method”. Code is at https://github.com/wbfwonderful/Vad-R1-Plus.
- Concepts from Representations (PCBM-ReD): Gong et al. (CUHK) introduce “Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations”, enhancing interpretability of deep learning models via sparse decomposition of visual representations. Code at https://github.com/peterant330/PCBM.
- Where Does Vision Meet Language?: Song et al. (National University of Defense Technology) investigate visual fusion in MLLMs via contrastive attention in “Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attention”.
Impact & The Road Ahead
The impact of these advancements is profound, touching areas from enhanced AI safety and interpretability to more efficient real-time systems and specialized applications in medicine and education. The continuous push for better benchmarks (like PhysicsMind, LiViBench, MIR-SafetyBench, CausalSpatial, GI-Bench, UR-Bench) is crucial, exposing current MLLM limitations and guiding future development towards human-like understanding. The emergence of robust frameworks for efficiency (HERMES, V-Skip, Docs2Synth) promises to make MLLMs more deployable and scalable. Moreover, the focus on fine-grained evaluation in areas like face understanding (FaceXBench), human pose editing (Yang et al.’s layer-selective MLLMs), and social interactions (SOCIAL CAPTION) indicates a move toward more nuanced and capable multimodal AI.
However, challenges remain. The ‘alignment paradox’ highlighted in the safety report, where helpfulness can compromise harmlessness, calls for a deeper rethinking of safety mechanisms. Models still struggle with foundational physics, causal reasoning, and human-like visual perception (as shown by PhysicsMind, CausalSpatial, and KidVis). The persistent ‘spatial grounding bottleneck’ and ‘fluency-accuracy paradox’ in medical applications underscore the need for stronger visual-semantic alignment. The future of MLLMs will likely involve more integrated approaches that combine provable robustness with sophisticated, explainable reasoning, enabling AI systems that are not only powerful but also trustworthy, understandable, and truly aligned with human needs across diverse, complex real-world scenarios.
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