“This paper addresses this critical gap by presenting a survey of current explainability and interpretability methods specifically for MLLMs.”

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“The best-performing MLLM achieves only 58.0% accuracy.”

“Enhancing the baseline agent with Chain-of-Thought (CoT) reasoning and self-reflection leads to an unexpected performance decrease, suggesting MLLMs exhibit poor context awareness in embodied navigation tasks.”

“UniAct achieves a 19% improvement in the success rate of zero-shot tracking of imperfect reference motions.”

“The paper demonstrates a 24.0% relative improvement in reducing model hallucinations on counterfactual videos compared to the Qwen2.5-VL-7B baseline.”

“DiffThinker significantly outperforms leading closed source models including GPT-5 (+314.2%) and Gemini-3-Flash (+111.6%), as well as the fine-tuned Qwen3-VL-32B baseline (+39.0%), highlighting generative multimodal reasoning as a promising approach for vision-centric reasoning.”

“RSAgent achieves a zero-shot performance of 66.5% gIoU on ReasonSeg test, improving over Seg-Zero-7B by 9%, and reaches 81.5% cIoU on RefCOCOg, demonstrating state-of-the-art performance.”

“ThinkGen employs a decoupled architecture comprising a pretrained MLLM and a Diffusion Transformer (DiT), wherein the MLLM generates tailored instructions based on user intent, and DiT produces high-quality images guided by these instructions.”

“Models excel at extracting explicit text, but struggle with deep chemical logic and precise structural recognition.”

“The paper introduces SpatialMosaic, a comprehensive instruction-tuning dataset featuring 2M QA pairs, and SpatialMosaic-Bench, a challenging benchmark for evaluating multi-view spatial reasoning under realistic and challenging scenarios, consisting of 1M QA pairs across 6 tasks.”

“Current models struggle to adapt to the complex visual and cognitive demands of low-altitude scenarios.”

“JavisGPT outperforms existing MLLMs, particularly in complex and temporally synchronized settings.”

“The paper highlights that VPTracker 'significantly enhances tracking stability and target disambiguation under challenging scenarios, opening a new avenue for integrating MLLMs into visual tracking.'”

“TEXT achieves the best performance cross four datasets among all tested models, including three recently proposed approaches and three MLLMs.”

“The paper quantifies energy overheads ranging from 17% to 94% across different MLLMs for identical inputs, highlighting the variability in energy consumption.”

“SR-MCR improves both answer accuracy and reasoning coherence across a broad set of visual benchmarks; among open-source models of comparable size, SR-MCR-7B achieves state-of-the-art performance with an average accuracy of 81.4%.”

“The paper's core contribution is the development of a system that uses a multi-agent framework with specialized tools to improve 3D object arrangement using MLLMs.”

“iSHIFT matches state-of-the-art performance on multiple benchmark datasets.”

“VideoZoomer invokes a temporal zoom tool to obtain high-frame-rate clips at autonomously chosen moments, thereby progressively gathering fine-grained evidence in a multi-turn interactive manner.”

“UniPercept outperforms existing MLLMs on perceptual-level image understanding and can serve as a plug-and-play reward model for text-to-image generation.”

“The research aims for training-free and state-aware MLLM personalized assistants.”

“The article likely details the methodology, experiments, and results of EraseLoRA.”

“widgets are compact, context-free micro-interfaces that summarize key information through dense layouts and iconography under strict spatial constraints.”

“"PRISM achieves superior personality consistency aligned with human ground truth, significantly outperforming standard homogeneous and Big Five benchmarks."”

“To address these limitations, we propose M$^3$KG-RAG, a Multi-hop Multimodal Knowledge Graph-enhanced RAG that retrieves query-aligned audio-visual knowledge from MMKGs, improving reasoning depth and answer faithfulness in MLLMs.”

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“Cube Bench is a benchmark for spatial visual reasoning in MLLMs.”

“The paper is from ArXiv.”

“The study reveals a spatial reasoning gap in MLLMs.”

“The paper focuses on debiasing importance and promoting structural diversity in the token selection process.”

“The paper focuses on self-verified and efficient test-time scaling for diffusion multi-modal large language models.”

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“The paper introduces IPCV, an information-preserving compression method.”

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“The research focuses on learning cost-aware MLLM agents.”

“The article likely discusses a method to extend the visual context available to MLLMs.”

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“The paper likely presents novel scheduling algorithms or resource allocation strategies tailored for MLLM inference.”

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“The article's source is ArXiv, indicating a research paper.”

“CodeDance is a Dynamic Tool-integrated MLLM for Executable Visual Reasoning.”

“The article likely discusses a technique named 'Sketch-in-Latents'.”

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“We present UniGen-1.5, a unified multimodal large language model (MLLM) for advanced image understanding, generation and editing.”

“The article is sourced from ArXiv.”

“DrivePI utilizes spatial-aware 4D MLLMs for unified autonomous driving understanding, perception, prediction, and planning.”

“The article itself is the source, so there is no subordinate quote.”