✨UFO^3: Weaving the Digital Agent Galaxy

📝 Summary:
UFO^3 unifies diverse digital devices into a single orchestration fabric, enabling AI agents to collaborate seamlessly across platforms. It models tasks dynamically for asynchronous execution, achieving efficient, resilient, and accurate cross-device task orchestration with improved parallelism a...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11332
• PDF: https://arxiv.org/pdf/2511.11332
• Project Page: https://microsoft.github.io/UFO/
• Github: https://github.com/microsoft/UFO/

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#AIAgents #TaskOrchestration #DistributedSystems #EdgeAI #MultiAgentSystems

✨Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics

📝 Summary:
Enterprise Deep Research EDR is a multi-agent system for automated report generation and real-time data analysis in enterprises. It integrates specialized agents, tools, and a reflection mechanism for adaptive research. EDR outperforms state-of-the-art systems on open benchmarks without human ste...

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17797
• PDF: https://arxiv.org/pdf/2510.17797
• Github: https://github.com/SalesforceAIResearch/enterprise-deep-research

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Salesforce/EDR-200

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#MultiAgentSystems #EnterpriseAI #DataAnalytics #AIResearch #AutomatedReporting

✨Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO

📝 Summary:
Training multi-agent systems with distinct LLMs faces optimization challenges. M-GRPO, a hierarchical GRPO extension, addresses this by aligning heterogeneous trajectories and decoupling agent training. This improves stability and sample efficiency for tool-augmented reasoning tasks.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13288
• PDF: https://arxiv.org/pdf/2511.13288

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#MultiAgentSystems #ReinforcementLearning #DeepLearning #LLM #AI

✨PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC

📝 Summary:
PC-Agent is a hierarchical multi-agent framework improving MLLM-based GUI agents for complex PC tasks. It uses an Active Perception Module and a hierarchical decision-making architecture with Manager, Progress, and Decision agents. A Reflection agent provides feedback. It achieved a 32% task succ...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.14282
• PDF: https://arxiv.org/pdf/2502.14282
• Github: https://github.com/X-PLUG/MobileAgent/tree/main/PC-Agent

✨ Spaces citing this paper:
• https://huggingface.co/spaces/junyangwang0410/PC-Agent

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#MultiAgentSystems #AIAgents #MLLMs #PCAutomation #DeepLearning

✨SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System

📝 Summary:
SciEducator is a self-evolving multi-agent system designed for scientific video understanding and education. It integrates professional knowledge and step-wise reasoning to interpret scientific activities and produce multimodal educational content. SciEducator significantly outperforms existing m...

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17943
• PDF: https://arxiv.org/pdf/2511.17943

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#MultiAgentSystems #AIEducation #VideoUnderstanding #EdTech #AIResearch

✨Latent Collaboration in Multi-Agent Systems

📝 Summary:
LatentMAS enables LLM agents to collaborate directly in latent space, surpassing text-based communication. This boosts reasoning quality, accuracy, and efficiency speed, tokens without extra training.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20639
• PDF: https://arxiv.org/pdf/2511.20639
• Github: https://github.com/Gen-Verse/LatentMAS

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#LLM #MultiAgentSystems #LatentSpace #AIAgents #ArtificialIntelligence

✨Asking like Socrates: Socrates helps VLMs understand remote sensing images

📝 Summary:
Remote sensing models often show fake reasoning from coarse image understanding. This paper introduces RS-EoT, an iterative, language-driven system with a Socratic multi-agent approach and RL to seek visual evidence. It achieves state-of-the-art results, enabling genuine, evidence-grounded reason...

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22396
• PDF: https://arxiv.org/pdf/2511.22396
• Project Page: https://geox-lab.github.io/Asking_like_Socrates/
• Github: https://github.com/GeoX-Lab/Asking_like_Socrates

🔹 Models citing this paper:
• https://huggingface.co/ShaoRun/RS-EoT-7B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ShaoRun/RS-EoT-4K

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#VLM #RemoteSensing #AI #ReinforcementLearning #MultiAgentSystems

✨PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing

📝 Summary:
PaperDebugger is an in-editor, multi-agent academic writing assistant that integrates large language models directly into LaTeX environments. It allows deep interaction with document state and revision history for enhanced writing, review, and editing workflows.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02589
• PDF: https://arxiv.org/pdf/2512.02589
• Project Page: https://www.paperdebugger.com/
• Github: https://github.com/PaperDebugger/PaperDebugger

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#AcademicWriting #LLM #MultiAgentSystems #ResearchTools #AI

✨DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems

📝 Summary:
DoVer is an intervention-driven debugging approach for LLM multi-agent systems. It validates failure hypotheses and measures progress via targeted interventions, improving reliability. DoVer converts 18-49% of failed tasks into successes, offering an outcome-oriented debugging method.

🔹 Publication Date: Published on Dec 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06749
• PDF: https://arxiv.org/pdf/2512.06749
• Project Page: https://aka.ms/DoVer

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#LLM #MultiAgentSystems #Debugging #AI #Research

✨LongVideoAgent: Multi-Agent Reasoning with Long Videos

📝 Summary:
A multi-agent framework with a master LLM, grounding agent, and vision agent enhances long-video QA by improving temporal grounding and extracting visual details. This RL-trained system outperforms non-agent baselines on new datasets.

🔹 Publication Date: Published on Dec 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20618
• PDF: https://arxiv.org/pdf/2512.20618
• Github: https://longvideoagent.github.io/

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#MultiAgentSystems #LLM #VideoUnderstanding #ComputerVision #AI

✨Multi-Agent Software Development through Cross-Team Collaboration

📝 Summary:
Existing multi-agent LLM software development yields a single solution, missing better alternatives. We introduce Cross-Team Collaboration CTC, a framework where multiple agent teams propose and communicate diverse decisions. This significantly improves software quality and generalizes well.

🔹 Publication Date: Published on Jun 13, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.08979
• PDF: https://arxiv.org/pdf/2406.08979
• Github: https://github.com/OpenBMB/ChatDev

✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList

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#MultiAgentSystems #LLMAgents #SoftwareDevelopment #AICollaboration #AIResearch

✨TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration

📝 Summary:
TCAndon-Router TCAR is an adaptive reasoning router for multi-agent systems. It overcomes limitations of existing task routers by supporting dynamic agent onboarding and generating natural language reasoning chains to select agents. TCAR significantly improves routing accuracy, reduces conflicts,...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04544
• PDF: https://arxiv.org/pdf/2601.04544
• Github: https://github.com/Tencent/TCAndon-Router

🔹 Models citing this paper:
• https://huggingface.co/tencent/TCAndon-Router

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#MultiAgentSystems #AI #NLP #AdaptiveSystems #AIResearch