Latest 15 Papers - May 19, 2025

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Agent

SceneGenAgent: Precise Industrial Scene Generation with Coding Agent

  • Title: SceneGenAgent: Precise Industrial Scene Generation with Coding Agent
  • Date: 2025-05-15
  • Comment:

SceneGenAgent is a novel approach to generating precise industrial scenes using a coding agent. The agent is trained on a dataset of industrial scenes and learns to generate new scenes that are similar in style and content. The agent uses a combination of generative adversarial networks (GANs) and reinforcement learning to generate scenes that are both realistic and diverse.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning

  • Title: Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning
  • Date: 2025-05-15
  • Comment:

In multi-agent reinforcement learning, value function decomposition is a crucial step in learning the optimal policy. However, traditional methods often suffer from incomplete value function decomposition, leading to suboptimal policies. This paper proposes a novel approach to fix incomplete value function decomposition by introducing a new decomposition method that takes into account the interactions between agents.

AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenge

  • Title: AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenge
  • Date: 2025-05-15
  • Comment: 32 pages, 14 figures, 11 tables

This paper presents a conceptual taxonomy of AI agents and agentic AI, highlighting their differences and similarities. The authors discuss the applications and challenges of each type of AI and provide a comprehensive overview of the field.

Learning Graph Representation of Agent Diffusers

  • Title: Learning Graph Representation of Agent Diffusers
  • Date: 2025-05-15
  • Comment:
    Accep...

    Accepted at AAMAS2025 International Conference on Autonomous Agents and Multiagent Systems

This paper proposes a novel approach to learning graph representation of agent diffusers. The authors use a graph neural network to learn the representation of the agent diffusers and demonstrate its effectiveness in a multi-agent reinforcement learning setting.

Multi-Agent Path Finding For Large Agents Is Intractable

  • Title: Multi-Agent Path Finding For Large Agents Is Intractable
  • Date: 2025-05-15
  • Comment:

This paper shows that multi-agent path finding for large agents is intractable. The authors provide a formal proof of the intractability and discuss the implications for multi-agent reinforcement learning.

Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change

  • Title: Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change
  • Date: 2025-05-15
  • Comment:
    PhD D...

    PhD Dissertation, 131 pages

This paper proposes a novel approach to efficient adaptation of reinforcement learning agents to sudden environmental change. The authors use a combination of reinforcement learning and transfer learning to adapt the agent to the new environment.

Random Walks Performed by Topologically-Specific Agents on Complex Networks

  • Title: Random Walks Performed by Topologically-Specific Agents on Complex Networks
  • Date: 2025-05-15
  • Comment: 21 pages, 15 figures

This paper studies the behavior of topologically-specific agents performing random walks on complex networks. The authors provide a comprehensive analysis of the behavior of the agents and discuss the implications for network science.

AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents

This paper proposes a novel approach to enhancing vulnerability management using autonomous LLM agents. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

MASS: Multi-Agent Simulation Scaling for Portfolio Construction

  • Title: MASS: Multi-Agent Simulation Scaling for Portfolio Construction
  • Date: 2025-05-15
  • Comment:

This paper proposes a novel approach to multi-agent simulation scaling for portfolio construction. The authors use a combination of multi-agent reinforcement learning and transfer learning to adapt the agent to the new environment.

Learning Progress Driven Multi-Agent Curriculum

  • Title: Learning Progress Driven Multi-Agent Curriculum
  • Date: 2025-05-15
  • Comment: ICML 2025

This paper proposes a novel approach to learning progress driven multi-agent curriculum. The authors use a combination of reinforcement learning and transfer learning to adapt the agent to the new environment.

AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents

This paper proposes a novel approach to learning knowledge graph world models with episodic memory for LLM agents. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Learning Virtual Machine Scheduling in Cloud Computing through Language Agents

  • Title: Learning Virtual Machine Scheduling in Cloud Computing through Language Agents
  • Date: 2025-05-15
  • Comment:

This paper proposes a novel approach to learning virtual machine scheduling in cloud computing through language agents. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Towards user-centered interactive medical image segmentation in VR with an assistive AI agent

  • Title: Towards user-centered interactive medical image segmentation in VR with an assistive AI agent
  • Date: 2025-05-15
  • Comment:

This paper proposes a novel approach to user-centered interactive medical image segmentation in VR with an assistive AI agent. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents

  • Title: Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents
  • Date: 2025-05-15
  • Comment:

This paper proposes a novel approach to multi-step planning and reasoning improves acting in LLM agents. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Design and Evaluation of Generative Agent-based Platform for Human-Assistant Interaction Research: A Tale of 10 User Studies

  • Title: Design and Evaluation of Generative Agent-based Platform for Human-Assistant Interaction Research: A Tale of 10 User Studies
  • Date: 2025-05-15
  • Comment:

This paper proposes a novel approach to design and evaluation of generative agent-based platform for human-assistant interaction research. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Recommendation System

Scalable Approximate Biclique Counting over Large Bipartite Graphs

  • Title: Scalable Approximate Biclique Counting over Large Bipartite Graphs
  • Date: 2025-05-15
  • Comment:

This paper proposes a novel approach to scalable approximate biclique counting over large bipartite graphs. The authors use a combination of graph neural networks and reinforcement learning to adapt the agent to the new environment.

Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M

  • Title: Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M
  • Date: 2025-05-15
  • Comment:

This paper proposes a novel approach to do LLMs memorize recommendation datasets? A preliminary study on MovieLens-1M. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making

  • Title: Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making
  • Date: 2025-05-15
  • Comment: Ph.D. Thesis

This paper proposes a novel approach to towards more efficient, robust, instance-adaptive, and generalizable sequential decision making. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

How Students Use AI Feedback Matters: Experimental Evidence on Physics Achievement and Autonomy

  • Title: How Students Use AI Feedback Matters: Experimental Evidence on Physics Achievement and Autonomy
  • Date: 2025-05-15
  • Comment:

Agent

Q: What is SceneGenAgent and how does it work?

A: SceneGenAgent is a novel approach to generating precise industrial scenes using a coding agent. The agent is trained on a dataset of industrial scenes and learns to generate new scenes that are similar in style and content. The agent uses a combination of generative adversarial networks (GANs) and reinforcement learning to generate scenes that are both realistic and diverse.

Q: What is the main contribution of the paper "Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning"?

A: The main contribution of the paper is the introduction of a new decomposition method that takes into account the interactions between agents. This method is shown to improve the performance of multi-agent reinforcement learning algorithms.

Q: What is the difference between AI Agents and Agentic AI?

A: AI Agents are software programs that can perform tasks on their own, while Agentic AI refers to a type of AI that can make decisions and take actions on its own, similar to a human agent.

Q: What is the main contribution of the paper "Learning Graph Representation of Agent Diffusers"?

A: The main contribution of the paper is the introduction of a novel approach to learning graph representation of agent diffusers. The authors use a graph neural network to learn the representation of the agent diffusers and demonstrate its effectiveness in a multi-agent reinforcement learning setting.

Q: What is the main contribution of the paper "Multi-Agent Path Finding For Large Agents Is Intractable"?

A: The main contribution of the paper is the formal proof of the intractability of multi-agent path finding for large agents. This result has implications for multi-agent reinforcement learning.

Q: What is the main contribution of the paper "Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change"?

A: The main contribution of the paper is the introduction of a novel approach to efficient adaptation of reinforcement learning agents to sudden environmental change. The authors use a combination of reinforcement learning and transfer learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Random Walks Performed by Topologically-Specific Agents on Complex Networks"?

A: The main contribution of the paper is the study of the behavior of topologically-specific agents performing random walks on complex networks. The authors provide a comprehensive analysis of the behavior of the agents and discuss the implications for network science.

Q: What is the main contribution of the paper "AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents"?

A: The main contribution of the paper is the introduction of a novel approach to enhancing vulnerability management using autonomous LLM agents. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "MASS: Multi-Agent Simulation Scaling for Portfolio Construction"?

A: The main contribution of the paper is the introduction of a novel approach to multi-agent simulation scaling for portfolio construction. The authors use a combination of multi-agent reinforcement learning and transfer learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Learning Progress Driven Multi-Agent Curriculum"?

A: The main contribution of the paper is the introduction of a novel approach to learning progress driven multi-agent curriculum. The authors use a combination of reinforcement learning and transfer learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents"?

A: The main contribution of the paper is the introduction of a novel approach to learning knowledge graph world models with episodic memory for LLM agents. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Learning Virtual Machine Scheduling in Cloud Computing through Language Agents"?

A: The main contribution of the paper is the introduction of a novel approach to learning virtual machine scheduling in cloud computing through language agents. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Towards user-centered interactive medical image segmentation in VR with an assistive AI agent"?

A: The main contribution of the paper is the introduction of a novel approach to user-centered interactive medical image segmentation in VR with an assistive AI agent. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents"?

A: The main contribution of the paper is the introduction of a novel approach to multi-step planning and reasoning improves acting in LLM agents. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Design and Evaluation of Generative Agent-based Platform for Human-Assistant Interaction Research: A Tale of 10 User Studies"?

A: The main contribution of the paper is the introduction of a novel approach to design and evaluation of generative agent-based platform for human-assistant interaction research. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Recommendation System

Q: What is the main contribution of the paper "Scalable Approximate Biclique Counting over Large Bipartite Graphs"?

A: The main contribution of the paper is the introduction of a novel approach to scalable approximate biclique counting over large bipartite graphs. The authors use a combination of graph neural networks and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M"?

A: The main contribution of the paper is the introduction of a novel approach to do LLMs memorize recommendation datasets? A preliminary study on MovieLens-1M. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making"?

A: The main contribution of the paper is the introduction of a novel approach to towards more efficient, robust, instance-adaptive, and generalizable sequential decision making. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "How Students Use AI Feedback Matters: Experimental Evidence on Physics Achievement and Autonomy"?

A: The main contribution of the paper is the introduction of a novel approach to how students use AI feedback matters: experimental evidence on physics achievement and autonomy. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Misinformation Detection

Q: What is the main contribution of the paper "Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions"?

A: The main contribution of the paper is the introduction of a comprehensive survey of tasks, methods, applications, challenges, and future directions for large language models in stance detection.

Q: What is the main contribution of the paper "From Millions of Tweets to Actionable Insights: Leveraging LLMs for User Profiling"?

A: The main contribution of the paper is the introduction of a novel approach to leveraging LLMs for user profiling. The authors use a combination of LLMs and reinforcement learning to adapt the agent to the new environment.

Q: What is the main contribution of the paper "Sentiment and Social Signals in the Climate Crisis: A Survey on Analyzing Social Media Responses to Extreme Weather Events"?

A: The main contribution of the paper is the introduction of a comprehensive survey of tasks, methods, applications, challenges, and future directions for analyzing social media responses to extreme weather events.

Q: What is the main contribution of the paper "CAMOUFLAGE: Exploiting Misinformation Detection Systems Through LLM-driven Adversarial Claim Transformation"?

A: The main contribution of the paper is the introduction of a novel approach to exploiting misinformation detection systems through LLM-driven adversarial claim transformation.

Q: What is the main contribution of the paper "Robust Misinformation Detection by Visiting Potential Commonsense Conflict"?

A: The main contribution of the paper is the introduction of a novel approach to robust misinformation detection by visiting potential commonsense conflict.

Q: What is the main contribution of the paper "A Guide to Misinformation Detection Data and Evaluation"?

A: The main contribution of the paper is the introduction of a comprehensive guide to misinformation detection data and evaluation.

Q: What is the main contribution of the paper "Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection"?

A: The main contribution of the paper is the introduction of a novel approach to bridging cognition and emotion: empathy-driven multimodal misinformation detection.

Q: What is the main contribution of the paper "Factually: Exploring Wearable Fact-Checking for Augmented Truth Discernment"?

A: The main contribution of the paper is the introduction of a novel approach to wearable fact-checking for augmented truth discernment.

Q: What is the main contribution of the paper "ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos"?

A: The main contribution of the paper is the introduction of a novel multilingual multilabel dataset for automatic claim detection in videos.

Q: What is the main contribution of the paper "A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Mis