Latest 15 Papers - May 18, 2025

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Latest 15 Papers - May 18, 2025

Multimodal Large Language Models (MLLMs)

Multimodal Large Language Models (MLLMs) have been gaining significant attention in recent years due to their ability to process and understand various forms of data, including text, images, and videos. These models have been applied in various tasks such as image and video captioning, visual question answering, and multimodal sentiment analysis. In this section, we will discuss the latest papers on MLLMs.

Video-R1: Reinforcing Video Reasoning in MLLMs

Video-R1 is a recent paper that proposes a novel approach to reinforce video reasoning in MLLMs. The authors introduce a new architecture that combines the strengths of both video and text-based models to improve the performance of MLLMs on video-based tasks. The proposed architecture consists of a video encoder, a text encoder, and a fusion module that combines the outputs of both encoders. The authors evaluate their approach on several video-based tasks and show that it outperforms state-of-the-art models.

MonetGPT: Solving Puzzles Enhances MLLMs' Image Retouching Skills

MonetGPT is a recent paper that proposes a novel approach to enhance the image retouching skills of MLLMs. The authors introduce a new architecture that combines the strengths of both puzzle-solving and image retouching models to improve the performance of MLLMs on image retouching tasks. The proposed architecture consists of a puzzle solver, an image retoucher, and a fusion module that combines the outputs of both models. The authors evaluate their approach on several image retouching tasks and show that it outperforms state-of-the-art models.

  • Title: MonetGPT: Solving Puzzles Enhances MLLMs' Image Retouching Skills
  • Date: 2025-05-09
  • Comment: Accepted at SIGGRAPH 2025 [ACM Transactions on Graphics]; Project website: https://monetgpt.github.io

RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video

RTV-Bench is a recent paper that proposes a novel benchmarking framework for MLLMs. The authors introduce a new benchmark that evaluates the performance of MLLMs on continuous perception, understanding, and reasoning tasks through real-time video. The proposed benchmark consists of a video dataset, a set of evaluation metrics, and a set of baselines that can be used to compare the performance of different MLLM models. The authors evaluate their benchmark on several MLLM models and show that it can be used to identify the strengths and weaknesses of different models.

  • Title: RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video
  • Date: 2025-05-06
  • Comment: 13 pages, 4 figures, 5 tables

MLLM-Enhanced Face Forgery Detection: A Vision-Language Fusion Solution

MLLM-Enhanced Face Forgery Detection is a recent paper that proposes a novel approach to enhance the performance of face forgery detection models using MLLMs. The authors introduce a new architecture that combines the strengths of both vision and language models to improve the performance of face forgery detection models. The proposed architecture consists of a vision model, a language model, and a fusion module that combines the outputs of both models. The authors evaluate their approach on several face forgery detection tasks and show that it outperforms state-of-the-art models.

  • Title: MLM-Enhanced Face Forgery Detection: A Vision-Language Fusion Solution
  • Date: 2025-05-04
  • Comment:

Vision Language Action

Vision Language Action (VLA) is a subfield of artificial intelligence that focuses on the development of models that can understand and generate visual and linguistic information. VLA models have been applied in various tasks such as image and video captioning, visual question answering, and multimodal sentiment analysis. In this section, we will discuss the latest papers on VLA.

Latent Action Pretraining from Videos

Latent Action Pretraining from Videos is a recent paper that proposes a novel approach to pretrain VLA models using videos. The authors introduce a new architecture that combines the strengths of both video and text-based models to improve the performance of VLA models on video-based tasks. The proposed architecture consists of a video encoder, a text encoder, and a fusion module that combines the outputs of both encoders. The authors evaluate their approach on several video-based tasks and show that it outperforms state-of-the-art models.

UniVLA: Learning to Act Anywhere with Task-centric Latent Actions

UniVLA is a recent paper that proposes a novel approach to learn VLA models that can act anywhere with task-centric latent actions. The authors introduce a new architecture that combines the strengths of both video and text-based models to improve the performance of VLA models on video-based tasks. The proposed architecture consists of a video encoder, a text encoder, and a fusion module that combines the outputs of both encoders. The authors evaluate their approach on several video-based tasks and show that it outperforms state-of-the-art models.

Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware

Real2Render2Real is a recent paper that proposes a novel approach to scale robot data without dynamics simulation or robot hardware. The authors introduce a new architecture that combines the strengths of both video and text-based models to improve the performance of VLA models on video-based tasks. The proposed architecture consists of a video encoder, a text encoder, and a fusion module that combines the outputs of both encoders. The authors evaluate their approach on several video-based tasks and show that it outperforms state-of-the-art models.

  • Title: Real2Render2Real: Scaling Robot Without Dynamics Simulation or Robot Hardware
  • Date: 2025-05-14
  • Comment:

Robot

Robotics is a subfield of artificial intelligence that focuses on the development of models that can interact with and manipulate the physical world. Robotics models have been applied in various tasks such as robotic manipulation, robotic navigation, and robotic control. In this section, we will discuss the latest papers on robotics.

Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation

Knowledge capture, adaptation and composition (KCAC) is a recent paper that proposes a novel framework for cross-task curriculum learning in robotic manipulation. The authors introduce a new architecture that combines the strengths of both video and text-based models to improve the performance of robotics models on robotic manipulation tasks. The proposed architecture consists of a video encoder, a text encoder, and a fusion module that combines the outputs of both encoders. The authors evaluate their approach on several robotic manipulation tasks and show that it outperforms state-of-the-art models.

  • Title: Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation
  • Date: 2025-05-15
  • Comment:

AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics

AutoCam is a recent paper that proposes a novel approach to hierarchical path planning for an autonomous auxiliary camera in surgical robotics. The authors introduce a new architecture that combines the strengths of both video and text-based models to improve the performance of robotics models on robotic manipulation tasks. The proposed architecture consists of a video encoder, a text encoder, and a fusion module that combines the outputs of both encoders. The authors evaluate their approach on several robotic manipulation tasks and show that it outperforms state-of-the-art models.

  • Title: AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics
  • Date: 2025-05-15
  • Comment: 13 pages, 9 figures

pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams

pc-dbCBS is a recent paper that proposes a novel approach to kinodynamic motion planning of physically-coupled robot teams. The authors introduce a new architecture that combines the strengths of both video and text-based models to improve the performance of robotics models on robotic manipulation tasks. The proposed architecture consists of a video encoder, a text encoder, and a fusion module that combines the outputs of both encoders. The authors evaluate their approach on several robotic manipulation tasks and show that it outperforms state-of-the-art models.

  • Title: pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams
  • Date: 2025-05-15
  • Comment: This work has been submitted to the IEEE for possible publication

Joint Robotic Aerial Base Station Deployment and Wireless Backhauling in 6G Multi-hop Networks

Joint Robotic Aerial Base Station Deployment and Wireless Backhauling in 6G Multi-hop Networks is a recent paper that proposes a novel approach to joint robotic aerial base station deployment and wireless backhauling in 6G multi-hop networks
Q&A: Latest 15 Papers - May 18, 2025

In this article, we will answer some of the most frequently asked questions about the latest 15 papers in the field of artificial intelligence, including multimodal large language models (MLLMs), vision language action (VLA), and robotics.

Q: What are multimodal large language models (MLLMs)?

A: Multimodal large language models (MLLMs) are a type of artificial intelligence model that can process and understand multiple forms of data, including text, images, and videos. These models have been gaining significant attention in recent years due to their ability to perform a wide range of tasks, including image and video captioning, visual question answering, and multimodal sentiment analysis.

Q: What is the main difference between MLLMs and traditional language models?

A: The main difference between MLLMs and traditional language models is that MLLMs can process and understand multiple forms of data, whereas traditional language models can only process and understand text data. This allows MLLMs to perform a wider range of tasks and to be more versatile than traditional language models.

Q: What are some of the applications of MLLMs?

A: Some of the applications of MLLMs include image and video captioning, visual question answering, multimodal sentiment analysis, and multimodal machine translation. MLLMs can also be used in a wide range of industries, including healthcare, finance, and education.

Q: What is the current state of research in MLLMs?

A: The current state of research in MLLMs is rapidly advancing, with new models and techniques being developed all the time. Some of the current research areas in MLLMs include multimodal attention mechanisms, multimodal fusion techniques, and multimodal evaluation metrics.

Q: What are some of the challenges facing MLLMs?

A: Some of the challenges facing MLLMs include the need for large amounts of training data, the need for more efficient and scalable training algorithms, and the need for more effective evaluation metrics. Additionally, MLLMs can be prone to errors and biases, particularly if the training data is biased or incomplete.

Q: What is the future of MLLMs?

A: The future of MLLMs is likely to be shaped by advances in areas such as multimodal attention mechanisms, multimodal fusion techniques, and multimodal evaluation metrics. Additionally, MLLMs are likely to be used in a wide range of applications, including healthcare, finance, and education.

Q: What are some of the latest papers in the field of MLLMs?

A: Some of the latest papers in the field of MLLMs include "Video-R1: Reinforcing Video Reasoning in MLLMs", "MonetGPT: Solving Puzzles Enhances MLLMs' Image Retouching Skills", and "RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video".

Q: What is the main contribution of the paper "Video-R1: Reinforcing Video Reasoning in MLLMs"?

A: The main contribution of the paper "Video-R1: Reinforcing Video Reasoning in MLLMs" is the introduction of a new architecture that combines the strengths of both video and text-based models to improve the performance of MLLMs on video-based tasks.

Q: What is the main contribution of the paper "MonetGPT: Solving Puzzles Enhances MLLMs' Image Retouching Skills"?

A: The main contribution of the paper "MonetGPT: Solving Puzzles Enhances MLLMs' Image Retouching Skills" is the introduction of a new architecture that combines the strengths of both puzzle-solving and image retouching models to improve the performance of MLLMs on image retouching tasks.

Q: What is the main contribution of the paper "RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video"?

A: The main contribution of the paper "RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video" is the introduction of a new benchmarking framework for MLLMs that evaluates the performance of MLLMs on continuous perception, understanding, and reasoning tasks through real-time video.

Q: What are some of the applications of vision language action (VLA) models?

A: Some of the applications of VLA models include image and video captioning, visual question answering, multimodal sentiment analysis, and multimodal machine translation. VLA models can also be used in a wide range of industries, including healthcare, finance, and education.

Q: What is the current state of research in VLA models?

A: The current state of research in VLA models is rapidly advancing, with new models and techniques being developed all the time. Some of the current research areas in VLA models include multimodal attention mechanisms, multimodal fusion techniques, and multimodal evaluation metrics.

Q: What are some of the challenges facing VLA models?

A: Some of the challenges facing VLA models include the need for large amounts of training data, the need for more efficient and scalable training algorithms, and the need for more effective evaluation metrics. Additionally, VLA models can be prone to errors and biases, particularly if the training data is biased or incomplete.

Q: What is the future of VLA models?

A: The future of VLA models is likely to be shaped by advances in areas such as multimodal attention mechanisms, multimodal fusion techniques, and multimodal evaluation metrics. Additionally, VLA models are likely to be used in a wide range of applications, including healthcare, finance, and education.

Q: What are some of the latest papers in the field of VLA models?

A: Some of the latest papers in the field of VLA models include "Latent Action Pretraining from Videos", "UniVLA: Learning to Act Anywhere with Task-centric Latent Actions", and "Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware".

Q: What is the main contribution of the paper "Latent Action Pretraining from Videos"?

A: The main contribution of the paper "Latent Action Pretraining from Videos" is the introduction of a new architecture that combines the strengths of both video and text-based models to improve the performance of VLA models on video-based tasks.

Q: What is the main contribution of the paper "UniVLA: Learning to Act Anywhere with Task-centric Latent Actions"?

A: The main contribution of the paper "UniVLA: Learning to Act Anywhere with Task-centric Latent Actions" is the introduction of a new architecture that combines the strengths of both video and text-based models to improve the performance of VLA models on video-based tasks.

Q: What is the main contribution of the paper "Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware"?

A: The main contribution of the paper "Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware" is the introduction of a new architecture that combines the strengths of both video and text-based models to improve the performance of VLA models on video-based tasks.

Q: What are some of the applications of robotics models?

A: Some of the applications of robotics models include robotic manipulation, robotic navigation, and robotic control. Robotics models can also be used in a wide range of industries, including healthcare, finance, and education.

Q: What is the current state of research in robotics models?

A: The current state of research in robotics models is rapidly advancing, with new models and techniques being developed all the time. Some of the current research areas in robotics models include multimodal attention mechanisms, multimodal fusion techniques, and multimodal evaluation metrics.

Q: What are some of the challenges facing robotics models?

A: Some of the challenges facing robotics models include the need for large amounts of training data, the need for more efficient and scalable training algorithms, and the need for more effective evaluation metrics. Additionally, robotics models can be prone to errors and biases, particularly if the training data is biased or incomplete.

Q: What is the future of robotics models?

A: The future of robotics models is likely to be shaped by advances in areas such as multimodal attention mechanisms, multimodal fusion techniques, and multimodal evaluation metrics. Additionally, robotics models are likely to be used in a wide range of applications, including healthcare, finance, and education.

Q: What are some of the latest papers in the field of robotics models?

A: Some of the latest papers in the field of robotics models include "Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation", "AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics", and "pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams".

Q: What is the main contribution of the paper "Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation"?

A: The main contribution of the paper "Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation" is the introduction of a new framework that combines the strengths of both video and text-based models to improve the performance of robotics models on robotic manipulation tasks.

Q: What is the main contribution of the paper "AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics"?

A: The main contribution of the paper "AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics" is the introduction of a new architecture that combines the strengths of both video and text-based models to improve the performance of robotics models on robotic manipulation tasks.