Trending Research
garak: A Framework for Security Probing Large Language Models
As Large Language Models (LLMs) are deployed and integrated into thousands of applications, the need for scalable evaluation of how models respond to adversarial attacks grows rapidly.
The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use
The recently released model, Claude 3. 5 Computer Use, stands out as the first frontier AI model to offer computer use in public beta as a graphical user interface (GUI) agent.
MureObjectStitch: Multi-reference Image Composition
bcmi/mureobjectstitch-image-composition
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Generative image composition aims to regenerate the given foreground object in the background image to produce a realistic composite image.
Accelerating Vision Diffusion Transformers with Skip Branches
Diffusion Transformers (DiT), an emerging image and video generation model architecture, has demonstrated great potential because of its high generation quality and scalability properties.
Adaptive Blind All-in-One Image Restoration
Blind all-in-one image restoration models aim to recover a high-quality image from an input degraded with unknown distortions.
Treat Visual Tokens as Text? But Your MLLM Only Needs Fewer Efforts to See
ZhangAIPI/YOPO_MLLM_Pruning
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In this study, we investigate the redundancy in visual computation at both the parameter and computational pattern levels within LLaVA, a representative MLLM, and introduce a suite of streamlined strategies to enhance efficiency.
Learning to Fly in Seconds
Our framework enables Simulation-to-Reality (Sim2Real) transfer for direct RPM control after only 18 seconds of training on a consumer-grade laptop as well as its deployment on microcontrollers to control a multirotor under real-time guarantees.
Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models
In this paper, we present Insight-V, an early effort to 1) scalably produce long and robust reasoning data for complex multi-modal tasks, and 2) an effective training pipeline to enhance the reasoning capabilities of multi-modal large language models (MLLMs).
A Survey on LLM-as-a-Judge
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale.
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP).
