Latest AI Papers August 7 2025 Agents Misinformation Detection LLMs Representation Learning And Multimodal Learning

Stay up-to-date with the latest advancements in artificial intelligence with this compilation of 15 recent research papers. Covering diverse topics such as agents, misinformation detection, large language models (LLMs), representation learning, and multimodal learning, this summary provides a comprehensive overview of the cutting-edge research in the field. For a better reading experience and access to even more papers, be sure to check out the Github page.

Agent Papers

This section highlights recent papers focused on agent-based systems, exploring topics ranging from self-evolving agents to multi-agent collaboration and robotic systems. These papers delve into the fascinating world of intelligent agents and their potential applications.

1. SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience

In the realm of self-evolving computer use agents, a groundbreaking paper titled "SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience" introduces a novel approach to creating agents that can autonomously learn from their experiences. This innovative research presents an agent that can adapt and improve its performance over time, paving the way for more intelligent and versatile computer systems. The code for this project is available on Github, allowing researchers and developers to explore and build upon this exciting technology.

This paper delves into the intricate mechanisms of SEAgent, a pioneering agent designed to autonomously learn and evolve within a computer environment. The core concept revolves around enabling the agent to independently acquire knowledge and refine its skills through continuous interaction and feedback. The significance of this research lies in its potential to revolutionize how we interact with computers, shifting from traditional, pre-programmed systems to intelligent entities capable of adapting to user needs and evolving alongside technological advancements. The methodologies employed in SEAgent incorporate a blend of machine learning techniques, including reinforcement learning and deep learning, allowing the agent to not only perform tasks but also to understand the underlying principles and adapt its strategies accordingly. This approach empowers SEAgent to handle a wide array of computer-based tasks, ranging from simple operations to complex problem-solving scenarios. The implications of this research extend beyond mere automation; they hint at a future where computer agents become integral partners in our digital lives, assisting with everything from routine tasks to intricate decision-making processes. SEAgent's ability to learn autonomously marks a significant leap towards creating truly intelligent and adaptive computer systems, setting a new standard for agent-based technology.

2. From MAS to MARS: Coordination Failures and Reasoning Trade-offs in Hierarchical Multi-Agent Robotic Systems within a Healthcare Scenario

This paper explores the complexities of coordination failures and reasoning trade-offs in hierarchical multi-agent robotic systems within a healthcare setting. The research delves into the challenges of designing effective multi-agent systems for critical applications, such as healthcare, where seamless coordination and decision-making are paramount.

3. TurboTrain: Towards Efficient and Balanced Multi-Task Learning for Multi-Agent Perception and Prediction

Accepted for presentation at ICCV 2025, "TurboTrain" focuses on efficient and balanced multi-task learning for multi-agent perception and prediction. This research addresses the critical need for systems that can effectively handle multiple tasks simultaneously, which is essential for real-world applications involving multiple agents.

4. LLM Collaboration With Multi-Agent Reinforcement Learning

This paper investigates the synergistic potential of combining large language models (LLMs) with multi-agent reinforcement learning. The research explores how LLMs can enhance the capabilities of multi-agent systems by providing natural language understanding and reasoning abilities.

5. TURA: Tool-Augmented Unified Retrieval Agent for AI Search

"TURA" introduces a tool-augmented unified retrieval agent designed for AI search. This research focuses on developing agents that can effectively leverage external tools and resources to improve their search capabilities.

6. Beyond Brainstorming: What Drives High-Quality Scientific Ideas? Lessons from Multi-Agent Collaboration

This preprint explores the factors that drive high-quality scientific ideas in multi-agent collaboration. The research delves into the dynamics of collaborative brainstorming and identifies key elements that contribute to the generation of innovative concepts.

7. CONVERGE: A Multi-Agent Vision-Radio Architecture for xApps

"CONVERGE" presents a multi-agent vision-radio architecture for xApps. This paper explores the integration of vision and radio technologies in multi-agent systems, with potential applications in areas such as wireless communication and networking.

8. The Dark Side of LLMs: Agent-based Attacks for Complete Computer Takeover

This paper delves into the potential security risks associated with LLMs, specifically focusing on agent-based attacks that could lead to complete computer takeover. The research highlights the importance of developing robust security measures to mitigate these risks.

This comprehensive study sheds light on the vulnerabilities of LLMs when deployed as agents within computer systems. It meticulously outlines how malicious actors can exploit these vulnerabilities to launch sophisticated attacks, potentially gaining complete control over a target machine. The research team demonstrates various attack vectors, showcasing how an attacker can manipulate an LLM agent to execute arbitrary code, access sensitive data, and even propagate the attack to other systems. The findings underscore the critical need for robust security measures in the design and deployment of LLM-based agents. The paper advocates for a multi-faceted approach to security, including techniques such as input validation, output sanitization, and continuous monitoring of agent behavior. Furthermore, it emphasizes the importance of developing LLMs that are inherently resilient to adversarial attacks. This research serves as a crucial wake-up call for the AI community, highlighting the potential dangers of unchecked LLM deployment and urging proactive measures to ensure the safe and secure integration of these powerful models into real-world applications. The implications of this work extend beyond individual systems, touching upon the broader cybersecurity landscape and the need for a holistic approach to AI security.

9. AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments

"AgentSense" explores the use of LLM agents for virtual sensor data generation in simulated home environments. This research presents a novel approach to creating realistic sensor data for training and evaluating AI systems in smart home applications.

10. OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use

Accepted as an oral presentation at ACL 2025, this paper provides a survey of MLLM-based agents for general computing devices. The research offers a comprehensive overview of the current state of OS agents and their potential to revolutionize how we interact with computers.

11. A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles

This paper presents a value-based parallel update Monte Carlo tree search (MCTS) method for multi-agent cooperative decision-making in connected and automated vehicles. The research addresses the challenges of coordinating multiple vehicles in complex traffic scenarios.

12. Think Before You Segment: An Object-aware Reasoning Agent for Referring Audio-Visual Segmentation

"Think Before You Segment" introduces an object-aware reasoning agent for referring audio-visual segmentation. The project page for this research is available on Github, providing further details and resources.

This innovative study introduces a novel agent, aptly named TGS-Agent, which meticulously integrates object awareness and reasoning capabilities into the realm of audio-visual segmentation. The core principle behind TGS-Agent is to mimic human cognitive processes by encouraging the agent to first