✨MIRIX: Multi-Agent Memory System for LLM-Based Agents

📝 Summary:
MIRIX is a modular multi-agent memory system for LLM-based agents that integrates diverse memory types and a dynamic framework. It significantly enhances memory capabilities for multimodal and long-form conversations. MIRIX achieves superior performance on challenging benchmarks, outperforming ex...

🔹 Publication Date: Published on Jul 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.07957
• PDF: https://arxiv.org/pdf/2507.07957
• Project Page: https://mirix.io/
• Github: https://github.com/Mirix-AI/MIRIX

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#LLM #MultiAgentSystems #AISystems #MemorySystems #AI

✨The Collaboration Gap

📝 Summary:
A new benchmark reveals a collaboration gap where AI models performing well solo degrade significantly when paired. Starting with a stronger agent relay inference helps bridge this gap. This suggests a need for collaboration-aware evaluation and training.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02687
• PDF: https://arxiv.org/pdf/2511.02687

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#AI #Collaboration #MultiAgentSystems #AIResearch #AIEvaluation

✨JoyAgent-JDGenie: Technical Report on the GAIA

📝 Summary:
This paper introduces JoyAgent-JDGenie, a generalist AI agent architecture. It integrates multi-agent planning, hierarchical memory, and advanced tools to achieve superior performance across diverse tasks, outperforming baselines and approaching proprietary systems.

🔹 Publication Date: Published on Oct 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.00510
• PDF: https://arxiv.org/pdf/2510.00510
• Github: https://github.com/jd-opensource/joyagent-jdgenie

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#AIAgent #GeneralistAI #MultiAgentSystems #AIResearch #MachineLearning

✨The Station: An Open-World Environment for AI-Driven Discovery

📝 Summary:
The Station is an open-world multi-agent AI environment enabling autonomous scientific discovery. Agents engage in full scientific journeys, achieving state-of-the-art results across diverse benchmarks. This new paradigm fosters emergent behaviors and novel method development, moving beyond rigid...

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06309
• PDF: https://arxiv.org/pdf/2511.06309
• Github: https://github.com/dualverse-ai/station

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#AI #MultiAgentSystems #ScientificDiscovery #OpenWorldAI #AutonomousAI

✨Adaptive Multi-Agent Response Refinement in Conversational Systems

📝 Summary:
This paper presents a multi-agent framework for refining conversational responses across factuality, personalization, and coherence. It employs dynamic agent coordination, outperforming single LLM approaches on challenging conversational datasets.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08319
• PDF: https://arxiv.org/pdf/2511.08319

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#MultiAgentSystems #ConversationalAI #LLMs #NLP #AIResearch

✨FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces

📝 Summary:
FilmAgent is an LLM-based multi-agent framework that automates end-to-end virtual film production, covering scriptwriting, cinematography, and actor positioning. Human evaluations show it outperforms baselines, proving multi-agent collaboration is feasible for filmmaking.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.12909
• PDF: https://huggingface.co/papers/2501.11233
• Project Page: https://filmagent.github.io/
• Github: https://filmagent.github.io/

==================================

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#AI #LLM #VirtualProduction #MultiAgentSystems #Filmmaking

🤖🧠 Claude-Flow v2.7: The Next Generation of Enterprise AI Orchestration

🗓️ 12 Nov 2025
📚 AI News & Trends

Artificial intelligence is rapidly transforming software development, research and enterprise workflows. As AI models become increasingly complex, managing, coordinating and optimizing them efficiently has become a critical challenge. Enter Claude-Flow v2.7, an advanced AI orchestration platform that blends multi-agent intelligence, persistent memory and swarm-based coordination to deliver enterprise-level automation and reasoning at scale. Developed by ...

#ClaudeFlow #EnterpriseAI #AIOrchestration #MultiAgentSystems #AIAutomation #PersistentMemory

✨MADD: Multi-Agent Drug Discovery Orchestra

📝 Summary:
MADD is a multi-agent system integrating LLMs and specialized models to enhance hit identification in drug discovery. It builds customized pipelines from natural language queries, demonstrating superior performance and accessibility for researchers.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08217
• PDF: https://arxiv.org/pdf/2511.08217
• Github: https://github.com/sb-ai-lab/MADD

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ITMO-NSS/MADD_Benchmark_and_results

==================================

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#DrugDiscovery #MultiAgentSystems #LLMs #AI #AIforScience

✨MarsRL: Advancing Multi-Agent Reasoning System via Reinforcement Learning with Agentic Pipeline Parallelism

📝 Summary:
MarsRL enhances multi-agent reasoning systems by jointly optimizing all agents through reinforcement learning and agentic pipeline parallelism. This novel approach significantly boosts open-source LLM accuracy on complex tasks, even outperforming larger models on benchmarks like AIME2025.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11373
• PDF: https://arxiv.org/pdf/2511.11373
• Github: https://github.com/liushulinle/MarsRL

🔹 Models citing this paper:
• https://huggingface.co/forestliutc/MarsRL

==================================

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#ReinforcementLearning #MultiAgentSystems #LLM #AIResearch #MachineLearning

✨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

✨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