✨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

✨Fara-7B: An Efficient Agentic Model for Computer Use

📝 Summary:
FaraGen creates synthetic datasets for computer use agents, solving a data scarcity problem. This data trains Fara-7B, a small on-device model that perceives computers via screenshots and outperforms larger models on diverse web tasks.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19663
• PDF: https://arxiv.org/pdf/2511.19663

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#AIAgents #OnDeviceAI #SyntheticData #MachineLearning #ComputerVision

✨Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning

📝 Summary:
Agent0-VL is a self-evolving vision-language agent that integrates tool usage into both reasoning and self-evaluation. It uses a Solver and Verifier in a self-evolving cycle for continuous improvement without human annotation or external rewards, achieving a 12.5% performance gain.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19900
• PDF: https://arxiv.org/pdf/2511.19900

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#AIAgents #VisionLanguage #SelfEvolvingAI #ToolAugmentedAI #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

✨DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action

📝 Summary:
DualVLA tackles action degeneration in VLAs by boosting action performance while retaining reasoning. It uses dual-layer data pruning and dual-teacher adaptive distillation. This balances precise action and multimodal understanding, leading to high success rates.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22134
• PDF: https://arxiv.org/pdf/2511.22134
• Project Page: https://costaliya.github.io/DualVLA/
• Github: https://costaliya.github.io/DualVLA/

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#EmbodiedAI #VLAs #AIagents #DeepLearning #AIResearch

✨Agentic Policy Optimization via Instruction-Policy Co-Evolution

📝 Summary:
INSPO introduces a novel framework dynamically optimizing instructions within the reinforcement learning loop for autonomous agents. It substantially outperforms static instruction methods in multi-turn reasoning by discovering innovative, strategic reasoning paths.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01945
• PDF: https://arxiv.org/pdf/2512.01945
• Github: https://github.com/cambridgeltl/inspo

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#ReinforcementLearning #AIAgents #PolicyOptimization #MachineLearning #AI

✨SIMA 2: A Generalist Embodied Agent for Virtual Worlds

📝 Summary:
SIMA 2 is a Gemini-based embodied agent for 3D virtual worlds. It reasons about goals, handles complex instructions, and autonomously learns new skills. This closes the gap with human performance and validates continuous learning agents.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04797
• PDF: https://arxiv.org/pdf/2512.04797

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#EmbodiedAI #AI #VirtualWorlds #ReinforcementLearning #AIagents

✨Thinking with Images via Self-Calling Agent

📝 Summary:
sCoT is a novel visual reasoning paradigm that reformulates interleaved multimodal CoT as a language-only CoT with self-calling subagents. It improves reasoning performance and efficiency by avoiding explicit multimodal interleaving and using group-relative policy optimization.

🔹 Publication Date: Published on Dec 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08511
• PDF: https://arxiv.org/pdf/2512.08511
• Github: https://github.com/YWenxi/think-with-images-through-self-calling

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#VisualReasoning #MultimodalAI #LLMs #AIagents #AIResearch

✨A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

📝 Summary:
This survey reviews self-evolving AI agents that adapt to dynamic environments via automatic enhancement from interaction data. It proposes a unified framework and systematically reviews current techniques, addressing evaluation, safety, and ethics.

🔹 Publication Date: Published on Aug 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07407
• PDF: https://arxiv.org/pdf/2508.07407
• Project Page: https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
• Github: https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents

✨ Spaces citing this paper:
• https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents

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

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#SelfEvolvingAI #AIAgents #FoundationModels #LifelongLearning #ArtificialIntelligence

✨Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem

📝 Summary:
The Agentic Learning Ecosystem ALE is a new infrastructure to streamline LLM agent development for real-world tasks. ALE comprises ROLL for optimization, ROCK for sandboxing, and iFlow CLI for context. Their agent ROME, built with ALE, shows strong benchmark performance.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24873
• PDF: https://arxiv.org/pdf/2512.24873

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#AIAgents #LLMDevelopment #AgenticLearning #AIArchitecture #MachineLearning

✨Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization

📝 Summary:
Youtu-Agent scales LLM agent productivity, automating generation and enabling continuous evolution. Its hybrid optimization, using in-context learning and scalable reinforcement learning, yields top performance and boosted capabilities.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24615
• PDF: https://arxiv.org/pdf/2512.24615
• Project Page: https://tencentcloudadp.github.io/youtu-agent/
• Github: https://github.com/TencentCloudADP/youtu-tip

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#LLM #AIAgents #ReinforcementLearning #MachineLearning #AI

✨MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents

📝 Summary:
MAGMA is a multi-graph memory architecture that improves AI agent long-context reasoning. It decouples memory representation from retrieval logic across semantic, temporal, causal, and entity graphs for query-adaptive selection, outperforming existing agentic memory systems.

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03236
• PDF: https://arxiv.org/pdf/2601.03236

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#AIAgents #MemoryArchitecture #LongContextReasoning #GraphAI #ArtificialIntelligence

✨SimpleMem: Efficient Lifelong Memory for LLM Agents

📝 Summary:
SimpleMem is an efficient memory framework for LLM agents that uses semantic lossless compression. It employs a three-stage pipeline to distill, consolidate, and retrieve historical experiences efficiently. SimpleMem significantly improves accuracy and reduces token consumption by up to 30-fold c...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02553
• PDF: https://arxiv.org/pdf/2601.02553
• Project Page: https://aiming-lab.github.io/SimpleMem-Page/
• Github: https://aiming-lab.github.io/SimpleMem-Page/

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

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#LLM #AIAgents #LifelongLearning #AI #DeepLearning

✨Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts

📝 Summary:
A case study of four LLM agent attempts to autonomously generate ML research papers reveals six recurring failure modes. Most attempts failed, though one was accepted to a special AI-first author venue, leading to proposed design principles for future AI-scientist systems.

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03315
• PDF: https://arxiv.org/pdf/2601.03315

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

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#LLMs #AIResearch #MachineLearning #AIAgents #AutonomousSystems

✨Distilling Feedback into Memory-as-a-Tool

📝 Summary:
This framework converts transient critiques into retrievable guidelines using a file-based memory system and agent tools. It enables LLMs to achieve test-time refinement performance with significantly reduced inference costs.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05960
• PDF: https://arxiv.org/pdf/2601.05960
• Github: https://github.com/vicgalle/feedback-memory-as-a-tool

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#LLMs #AIAgents #MemorySystems #AIResearch #MachineLearning

✨MemoBrain: Executive Memory as an Agentic Brain for Reasoning

📝 Summary:
Long-horizon tasks strain tool-augmented agents due to accumulating context. MemoBrain is an executive memory model that organizes and prunes reasoning steps, maintaining a compact, high-salience backbone within a fixed context. This improves coherent, goal-directed reasoning.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08079
• PDF: https://arxiv.org/pdf/2601.08079
• Github: https://github.com/qhjqhj00/MemoBrain

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#AIagents #ExecutiveMemory #Reasoning #LLM #CognitiveAI